Effect of sensor view angle on the assessment of agronomic traits by ground level hyper-spectral reflectance measurements in durum wheat under contrasting Mediterranean conditions - Aparicio, N.; Villegas, D.; Royo, C.; Casadesus, J.; Araus, J. L. (International Journal of Remote Sensing, 2004)
The objective of this work is to study the effect of changing the sensor view angle on spectral reflectance indices and their relationships with yield and other agronomic traits. Canopy reflectance spectra of 25 durum wheat genotypes were measured with a field spectroradiometer at two view angles, nadir and 30Â°, from anthesis to maturity in two years and two water regimes. Nine spectral reflectance indices were calculated from reflectance measurements for correlation with yield and several agronomic traits. At off-nadir position more reflected radiation was collected, associated with the reflective characteristics of stems. The performance of the indices predicting the yield and the agronomic traits varied as a function of sensor view angle, and were moreover affected by leaf area index (LAI) value. At high LAI, simple ratio (SR) and normalized difference vegetation index (NDVI), calculated at off-nadir position, were better predictors of traits related to the density of stems and poorer predictors of traits related to green area. On the other hand, at low LAI the indices normalized pigment chlorophyll index (NPCI) and water index (WI) were better predictors of yield and all the other traits when the sensor view angle was at nadir, whereas no differences due to sensor angle were accounted for the other three indices. The different performance of indices at low and high LAI is discussed.

A review of vegetation indices - Bannari, A.; Morin, D.; Bonn, F.; Huete, A. R. (Remote Sensing Reviews, 1995)
Abstract In the field of remote sensing applications, scientists have developed vegetation indices (VI) for qualitatively and quantitatively evaluating vegetative covers using spectral measurements. The spectral response of vegetated areas presents a complex mixture of vegetation, soil brightness, environmental effects, shadow, soil color and moisture. Moreover, the VI is affected by spatial?temporal variations of the atmosphere. Over forty vegetation indices have been developed during the last two decades in order to enhance vegetation response and minimize the effects of the factors described above. This paper summarizes, refers and discusses most of the vegetation indices found in the literature. It presents different existing classifications of indices and proposes to group them in a new classification.

Potentials and limits of vegetation indices for LAI and APAR assessment - Baret, F.; Guyot, G. (Remote Sensing of Environment, 1991)

Crop biomass evaluation using radiometric measurements - Baret, F.; Guyot, G.; Major, D. J. (Photogrammetria, 1989)
Crop biomass can be evaluated from radiometric measurements either by relating biomass to instantaneous measurements or by relating an integral of biomass to a radiometric value integrated over the corresponding portion of the growth period. In this study, the success obtained by using these two methods is discussed. A simple radiative transfer model was used in conjunction with experimental results to demonstrate the universality of the relationship between the normalized difference vegetation index (ND) and leaf area index (LAI) or the photosynthetically active radiation (PAR) absorbed by the crop. It shows that the relationship between ND and absorbed PAR is less dependent on leaf orientation than the relationship between ND and LAI. A remaining problem is the sensitivity of those two relationships to soil optical properties. Nevertheless, temporal integration of radiometric data using the absorbed PAR concept appears to be a more promising approach than one-time measurements.

Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data - Barnes, E.M.; Clarke, T.R.; Richards, S.E.; Colaizzi, P.D.; Haberland, J.; Kostrzewski, M.; Waller, P.; Choi, C., Riley, E.; Thompson, T.; Lascano, R.J.; Li, H.; Moran, M.S. (Proc. 5th Int. Conf. Precis Agric, 2000)

Remote sensing for irrigated agriculture: examples from research and possible applications - Bastiaanssen, Wim G. M.; Molden, David J.; Makin, Ian W. (Agricultural Water Management, 2000)
Researchers in various international programs have studied the potential use of remotely sensed data to obtain accurate information on land surface processes and conditions. These studies have demonstrated that quantitative assessment of the soil-vegetation-atmosphere-transfer processes can lead to a better understanding of the relationships between crop growth and water management. Information on land surface can now be obtained at a wide range of spatial (5â5000 m) and temporal resolutions (0.5â24 days). However, even though considerable progress has been made over the past 20 years in research applications, remotely sensed data remain underutilized by practicing water resource managers. This paper seeks to bridge the gap between researchers and practitioners, first, by illustrating where research tools and techniques have practical applications and, second, by identifying real problems that remote sensing could solve, albeit with additional research and development. As freshwater becomes an increasingly scarce resource, all opportunities to better manage water uses, particularly in irrigated agriculture, must be taken. An important challenge in the field of water resources is to utilize the timely, objective and accurate information provided by remote sensing.
Keywords: Remote sensing; Irrigated farming; Land management; Water resources management; Crop yield; Water use efficiency; Water rights

Soil background effects on reflectance-based crop coefficients for corn - Bausch, Walter C. (Remote Sensing of Environment, 1993)

A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data - Becker-Reshef, I.; Vermote, E.; Lindeman, M.; Justice, C. (Remote Sensing of Environment, 2010)
Wheat is one of the key cereal crops grown worldwide, providing the primary caloric and nutritional source for millions of people around the world. In order to ensure food security and sound, actionable mitigation strategies and policies for management of food shortages, timely and accurate estimates of global crop production are essential. This study combines a new BRDF-corrected, daily surface reflectance dataset developed from NASA's Moderate resolution Imaging Spectro-radiometer (MODIS) with detailed official crop statistics to develop an empirical, generalized approach to forecast wheat yields. The first step of this study was to develop and evaluate a regression-based model for forecasting winter wheat production in Kansas. This regression-based model was then directly applied to forecast winter wheat production in Ukraine. The forecasts of production in Kansas closely matched the USDA/NASS reported numbers with a 7% error. The same regression model forecast winter wheat production in Ukraine within 10% of the official reported production numbers six weeks prior to harvest. Using new data from MODIS, this method is simple, has limited data requirements, and can provide an indication of winter wheat production shortfalls and surplus prior to harvest in regions where minimal ground data is available.
Keywords: Remote sensing; Yield; Agriculture; Wheat

Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density - Broge, N. H.; Leblanc, E. (Remote Sensing of Environment, 2000)
Hyperspectral reflectance data representing a wide range of canopies were simulated using the combined PROSPECT+SAIL model. The simulations were used to study the stability of recently proposed vegetation indices (VIs) derived from adjacent narrowband spectral reflectance data across the visible (VIS) and near infrared (NIR) region of the electromagnetic spectrum. The prediction power of these indices with respect to green leaf area index (LAI) and canopy chlorophyll density (CCD) was compared, and their sensitivity to canopy architecture, illumination geometry, soil background reflectance, and atmospheric conditions were analyzed. The second soil-adjusted vegetation index (SAVI2) proved to be the best overall choice as a greenness measure. However, it is also shown that the dynamics of the VIs are very different in terms of their sensitivity to the different external factors that affects the spectral reflectance signatures of the various modeled canopies. It is concluded that hyperspectral indices are not necessarily better at predicting LAI and CCD, but that selection of a VI should depend upon (1) which parameter that needs to be estimated (LAI or CCD), (2) the expected range of this parameter, and (3) a priori knowledge of the variation of external parameters affecting the spectral reflectance of the canopy.

Estimating Error in an Analysis of Forest Fragmentation Change Using North American Landscape Characterization (NALC) Data - Brown, Daniel G.; Duh, Jiunn-Der; Drzyzga, Scott A. (Remote Sensing of Environment, 2000)
We describe an approach for estimating measurement error in an analysis of forest fragmentation dynamics. We classified North American Landscape Characterization (NALC) images in four path-row locations in the Upper Midwest to characterize changing patterns of forest cover. To estimate error, we calculated the differences in values of forest fragmentation metrics for overlapping scene pairs from the same time frame (or epoch). The overlapping image areas were subdivided into landscape partitions. We tested the effects of amount of forest cover, landscape phenology, atmospheric variability (e.g., haze and clouds), and alternative processing approaches on the consistency of metric values calculated for the same place and approximate time but from different images. Two of the metrics tested (average patch size and number of patches) were more sensitive to image characteristics and contained more measurement error in a change detection analysis than the others (percent forest cover and edge density). Increasing the landscape partition size moderately reduced the amount of error in landscape change analysis, but at the cost of reduced spatial resolution. Processes used to generalize the forest map, such as small-polygon sieving and majority filtering, were not found to consistently decrease measurement error in metric values and in some cases increased error. Predictive models of error in a forest fragmentation change analysis were developed and significantly explained up to 50% of the variation in error. We demonstrate how, in a change analysis, predicted error can be used to identify locations that exhibit change substantially greater than the error in value estimation.

Site-specific weed management: sensing requirements - what do we need to see? - Brown, R. B.; Noble, S. D. (Weed Science, 2005)
Automated detection and identification of weeds in crop fields is the greatest obstacle to development of practical site-specific weed management systems. Research progress is summarized for two different approaches to the problem, remote sensing weed mapping and ground-based detection using digital cameras or nonimaging sensors. The general spectral and spatial limitations reported for each type of weed identification system are reviewed. Airborne remote sensing has been successful for detection of distinct weed patches when the patches are dense and uniform and have unique spectral characteristics. Identification of weeds is hampered by spectral mixing in the relatively large pixels (typically larger than 1 by 1 m) and will not be possible from imagery where weed seedlings are sparsely distributed among crop plants. The use of multispectral imaging sensors such as color digital cameras on a ground-based mobile platform shows more promise for weed identification in field crops. Spectral features plus spatial features such as leaf shape and texture and plant organization may be extracted from these images. However, there is a need for research in areas such as artificial lighting, spectral band requirements, image processing, multiple spatial resolution systems, and multiperspective images.
ISI Document Delivery No.: 906EW Times Cited: 31 Cited Reference Count: 57 Brown, RB Noble, SD Symposium on Site-Specific Weed Management held at theAnnual Conference of the Weed-Science-Society-of-America 2004 Kansas City, MO Weed Sci Soc Amer Lawrence
Keywords: machine vision; remote sensing; site-specific; spectral reflectance; weed identification; plant-identification; machine vision; fractal dimension; spectral; characteristics; image segmentation; digital images; mapping weeds; shape; discrimination; crops

On the relation between NDVI, fractional vegetation cover, and leaf area index - Carlson, T. N.; Ripley, David A. (Remote Sensing of Environment, 1997)
We use a simple rudiative transfer model with vegetation, soil, and atmospheric components to illustrate hoW the normalized diflerence vegetation index @DVl), leaf area index (LAZ), and fractional vegetation cover are dependent. In particular, we suggest that LA1 and fractional vegetation cover may not be independent quantitites, ut least when the fermer is dejÃ¯ned without regard to the presence of hare patches between plante, and that the customary variation of LAI with NDVI can be erplained as resulting from a variation in fractional vegetation cover. The following points are made: i) Fractional vegetation cover and LA1 are not entirely independent yuantities, depending on how LA1 is defined. Gare must be taken in using LA1 and fractional vegetation cover independently in a model because the forrner may partially take account of the latter; ii) A scaled NDVI taken between the linzits of minimum (bare ,soil) and maximum fractional vegetation cover ia insenstive to atmosphetic correction for both clear und hazy conditions, at least f3r viewing angles less than aborct 20 degrees from nadir; iii) A ,simple relution between scaled NDVI and fractional vegetation cover, previously described in the literature, is ficrther c.on&-med by the .simulations; iv) The sensitice dependence of LA1 (~1 ND17 when the fonwr is below a value of about 24 may bc viewed as being dut to the variation in the bar{: .soil component. Ol%evier Sciencc lx., 1997

Detecting vegetation leaf water content using reflectance in the optical domain - Ceccato, Pietro; Flasse, Stéphane; Tarantola, Stefano; Jacquemoud, Stéphane; Grégoire, Jean-Marie (Remote Sensing of Environment, 2001)
This paper outlines the first part of a series of research studies to investigate the potential and approaches for using optical remote sensing to assess vegetation water content. It first analyzes why most methods used as approximations of vegetation water content (such as vegetation stress indices, estimation of degree of curing and chlorophyll content) are not suitable for retrieving water content at leaf level. It then documents the physical basis supporting the use of remote sensing to directly detect vegetation water content in terms of Equivalent Water Thickness (EWT) at leaf level. Using laboratory measurements, the radiative transfer model PROSPECT and a sensitivity analysis, it shows that shortwave infrared (SWIR) is sensitive to EWT but cannot be used alone to retrieve EWT because two other leaf parameters (internal structure and dry matter) also influence leaf reflectance in the SWIR. A combination of SWIR and NIR (only influenced by these two parameters) is necessary to retrieve EWT at leaf level. These results set the basis towards establishing operational techniques for the retrieval of EWT at top-of-canopy and top-of-atmospheric levels.
Keywords: Leaf water content; Fuel moisture content; Optical domain; Shortwave infrared

Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: Theoretical approach - Ceccato, Pietro; Gobron, Nadine; Flasse, StÃ©phane; Pinty, Bernard; Tarantola, Stefano (Remote Sensing of Environment, 2002)
This paper describes the methodology used to create a spectral index to retrieve vegetation water content from remotely sensed data in the solar spectrum domain. A global sensitivity analysis (GSA) using radiative transfer models is used to understand and quantify vegetation water content effects on the signal measured at three levels: leaf, canopy, and atmosphere. An index is then created that optimises retrieval of vegetation water content (in terms of water quantity per unit area at canopy level) and minimises perturbing effects of geophysical and atmospheric effects. The new index, optimised for the new SPOT-VEGETATION sensor, is presented as an example. Limitations and robustness of the index are also discussed.

Evaluation of vegetation indices and a modified simple ratio for boreal applications - Chen, J.M. (Canadian Journal of Remote Sensing, 1996)
A Modified Simple Ratio (MSR) Is proposed for retrieving biophysical parameters of boreal forests using remote sensing data. This vegetation index is formulated based on an evaluation of several two-band vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR), Soil Adjusted Vegetation Indices (SAVI, SAVI1, SAVI2), Weighted Difference Vegetation Index (WDVI), Global Environment Monitoring Index (GEMI), Non-Linear Index (NLI), and Renormalized Difference Vegetation Index (RDVI). MSR is an improved version of RDVI for the purpose of linearizing their relationships with biophysical parameters. All indices were obtained from Landsat-5 TM band 3 (visible) and band 4 (near infrared) images after atmospheric corrections (except for GEMI) and were correlated with ground-based measurements made in 20 Jack Pine (Pinus banksiana) and Black Spruce (Picea mariana) stands during the BOREAS field experiment in 1994. The measurements include Leaf Area Index (LAI) and the Fraction of Photosynthetically Active Radiation (FPAR) absorbed by the forest canopies. Among these vegetation indices, SR, MSR, and NDVI were found to be best correlated with LAI and FPAR in both spring and summer. All other indices performed poorly. Both NDVI and MSR can be expressed as a function of SR. Measurement errors in remote sensing data often occur due to changes in solar zenith angle, subpixel contamination of clouds, or dissimilar surface features and the variation in the local topography and other environmental factors. These errors generally cause simultaneous increases or decreases in the red and near infrared reflectances, and their effects can be greatly reduced by taking the ratio. All other indices involving mathematical operations other than ratioing could retain the errors or even amplify them. The major problem in using the vegetation indices obtained from red and near infrared bands is the small sensitivity to the overstorey vegetation conditions. Although many of the vegetation indices such as SAVI, SAVI1, and SAVI2 are developed to minimize the effect of the background on retrieving the vegetation information, they also reduce their sensitivity to the changes in the overstorey conditions.
Keywords: Boreal forests; FPAR; LAI; Vegetation index

Monitoring and Assessing the Environmental Impact of Mining in Europe Using Advanced Earth Observation Techniques - MINEO - Chevrel, Stephane; Belocky, Reinhard; GrÃ¶sel, Klemens (Environmental Communication in the Information Society - Proceedings of the 16th Conference, 2002)

Spectral reflectance, growth and chlorophyll relationships for rice crop in a semi-arid region of India - Choubey, V. K.; Choubey, R. (Water Resources Management, 1999)
Relations among spectral reflectance, chlorophyll 'a', and growth of rice plants grown on irrigated light textured soil in a semi arid region are presented here. There was a linear relation between spectral reflectance and rice plant height (r = 0.97), for band 1 (0.45-0.52 mu m) reflectance values. On the other hand, in bands 2 (0.52-0.60 mu m) and 3 (0.63-0.69 mu m), reflectance values decreased until 70 days after planting (DAP) and then increased during the reproductive phase of the crop. The near infrared band 4 (0.76-0.90 mu m) showed a maximum reflectance at 59 DAP (panicle initiation stage) and a decline in reflectance thereafter through maturity. The peak value of IR/R ratio was 16.39 at 62 DAP during the early reproductive phase; thereafter, it declines gradually with the maturity of the crop. Chlorophyll 'a' concentration was high during early growth (vegetative and early reproductive stages) and decreased during the flowering and maturity stages. The rice plant canopy show a high chlorophyll 'a' concentration at 64 and 59 DAP for sites A and B, respectively. Chlorophyll 'a' concentration is higher in site A plant canopies than it is in site B during the entire crop cycle. A good inverse correlation (r = 0.91) has been found between chlorophyll 'a' and band 1, while the IR/R ratio and the normalised difference vegetation index (NDVI) showed a relationship (r = 0.78) with the chlorophyll 'a' concentration during the crop cycle. Band 2, 3 and 4 radiance values show a biphasic linear relationship with chlorophyll 'a' concentrations, negative for early growth and positive for flowering and maturity stages. Results indicate that the period between 66 to 70 DAP is most suitable for the assessment of rice crop yield, based on chlorophyll 'a' concentration.
ISI Document Delivery No.: 258EE Times Cited: 2 Cited Reference Count: 23 Choubey, VK Choubey, R Kluwer academic publ Dordrecht
Keywords: chlorophyll; growth; rice crop; spectral reflectance; red edge; leaf biochemistry; vegetation; prospect; model

Planar domain indices: a method for measuring a quality of a single component in two-component pixels - Clarke, T. R.; Moran, M. S.; Barnes, E. M.; Pinter, P. J., Jr.; Qi, J. (Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International, 2001)
A method is presented that reduces the difficulty of measuring a particular quality of one component in a multi-component element. The planar domain index design requires two measurements: that of a signal sensitive to the desired quality of the target component and another signal sensitive to the component's weight or relative proportion to the whole. The quality signal and component weight signal form the two dimensions of a plane, and the maximum and minimum possible values for each signal define the boundaries of a domain within this plane. The position of a coordinate pair within the domain can then be correlated to the quality being measured, independent of the component's proportion to the whole. Examples given involve mixed vegetation and soil targets, with vegetation indices used to measure the component weight. Quality signals of the example applications include canopy minus air temperature as a measure of evapotranspiration, a normalized difference of near infrared and far red wavelengths as a measure of chlorophyll content, and differential synthetic aperture radar (SAR) for measuring near-surface soil moisture
Keywords: agriculture, geophysical signal processing, geophysical techniques, image processing, terrain mapping, vegetation mapping, canopy, geophysical measurement technique, land surface, planar domain index, planar domain indices, quality, remote sensing, single

Application of a weighted infrared-red vegetation index for estimating leaf Area Index by Correcting for Soil Moisture - Clevers, J. G. P. W. (Remote Sensing of Environment, 1989)
The simplified reflectance model described earlier (Clevers, 1988b) for estimating leaf area index (LAI) is further simplified. In this model the nearinfrared reflectance was corrected for soil background (in particular differences in soil moisture content) and subsequently used for estimating LAI by applying the inverse of a special case of the Mitscherlich function. In the specific situation that the ratio of the reflectances of bare soil in the red and near-infrared is constant for a given soil background, the corrected near-infrared reflectance is now ascertained as a weighted difference of total measured near-infrared and red reflectances (socalled WDVI = weighted difference vegetation index). The validity of this approach is confirmed by simulations with the SAIL model. The above concept was tested at the experimental farm of the Agricultural University Wageningen, by using reflectance factors measured in field trials by means of multispectral aerial photography. The soil type at the experimental farm yielded constant ratios between green, red, and near-infrared reflectances independent of soil moisture content (that is, as a function of time). The difference between measured near-infrared and red reflectances provided a satisfactory approximation of the corrected near-infrared reflectance. The estimation of LAI by this corrected near-infrared reflectance for real field data yielded good results in this study, resulting in the ascertainment of treatment effects with larger precision than by means of the LAI measured in the field by conventional field sampling methods.

Calculating the vegetation index faster - Crippen, Robert E. (Remote Sensing of Environment, 1990)
The near-infrared (NIR) versus red âinfrared percentage vegetation index,â NIR/(NIR + Red), is functionally and linearly equivalent to the normalized difference vegetation index, (NIR-Red/(NIR + Red). Advantageously, it is both computationally faster and never negative.

Identifying the spatial variability of soil constraints using multi-year remote sensing - Dang, Y. P.; Pringle, M. J.; Schmidt, M.; Dalal, R. C.; Apan, A. (Field Crops Research, 2011)
In north-eastern Australia, soil attributes such as salinity, sodicity, acidity, and phytotoxic concentrations of chloride constrain the growth of crops. It is difficult to delineate constrained areas using conventional sampling methods. Alternatively, where crops fail, over multiple years, to pass a certain yield threshold, we might infer the presence of a soil constraint. For a wheat-growing farm over 10-year period we used remote sensing to obtain a large volume of surrogate yield data, by calibrating an archive of Normalised Difference Vegetation Index (NDVI) to an archive of (limited) ground-based observations. The model used was a generalised additive model that related wheat yield as a non-linear function of NDVI and a linear function of post-anthesis rainfall. Field locations where predicted yield consistently failed to reach the 75th percentile in a given year, over a number of years, we regarded as limited by at least one unknown soil constraint. Soil samples averaged for the constrained locations showed, compared with the unconstrained locations, relatively high concentrations of subsoil chloride, and, in the topsoil, relatively high exchangeable sodium percentage, and unused nitrate nitrogen. On-farm experiments suggested that, for constrained areas, preclusion of monoammonium phosphate (MAP) fertiliser application, coupled to gypsum amelioration, could potentially benefit the farm by A$32/ha/year (MAP) and A$207/ha/3 years (gypsum). Crown Copyright (c) 2011 Published by Elsevier B.V. All rights reserved.
ISI Document Delivery No.: 808OU Times Cited: 1 Cited Reference Count: 42 Dang, Y. P. Pringle, M. J. Schmidt, M. Dalal, R. C. Apan, A. Grains Research & Development Corporation We wish to thank Peter and Jim Russell for providing their yield data and access to their farm. We thank the Grains Research & Development Corporation for partial funding of this study. Thanks are due to Dr. Jeremy Whish for doing the APSIM simulations and to Drs. Brett Robinson and Robert Denham for comments on draft versions. Elsevier science bv Amsterdam
Keywords: Soil constraints; Crop yield; Precision agriculture; NDVI; Whole-farm; economics; crop yield; wheat yield; subsoil constraints; grain production; australia; vegetation; satellite; ndvi; management; salinity

The MERIS terrestrial chlorophyll index - Dash, J.; Curran, P. J. (International Journal of Remote Sensing, 2004)
The long wavelength edge of the major chlorophyll absorption feature in the spectrum of a vegetation canopy moves to longer wavelengths with an increase in chlorophyll content. The position of this red-edge has been used successfully to estimate, by remote sensing, the chlorophyll content of vegetation canopies. Techniques used to estimate this red-edge position (REP) have been designed for use on small volumes of continuous spectral data rather than the large volumes of discontinuous spectral data recorded by contemporary satellite spectrometers. Also, each technique produces a different value of REP from the same spectral data and REP values are relatively insensitive to chlorophyll content at high values of chlorophyll content. This paper reports on the design and indirect evaluation of a surrogate REP index for use with spectral data recorded at the standard band settings of the Medium Resolution Imaging Spectrometer (MERIS). This index, termed the MERIS terrestrial chlorophyll index (MTCI), was evaluated using model spectra, field spectra and MERIS data. It was easy to calculate (and so can be automated), was correlated strongly with REP but unlike REP was sensitive to high values of chlorophyll content. As a result this index became an official MERIS level-2 product of the European Space Agency in March 2004. Further direct evaluation of the MTCI is proposed, using both greenhouse and field data.

Remote Sensing of Water Content in Eucalyptus Leaves - Datt, Bisun (Australian Journal of Botany, 1999)
The spectral reflectance of leaves from several Eucalyptus species was measured over the 400â2500 nm wavelengths with a laboratory spectroradiometer. The relationship of reflectance with the gravimetric water content and equivalent water thickness (EWT) of the leaves was analysed. The results showed that EWT was strongly correlated with reflectance in several wavelength regions. No significant correlations could be obtained between reflectance and gravimetric water content. It was also possible to confirm theoretically that reflectance changes of leaves could be directly linked to changes in EWT but not to changes in gravimetric water content. Several existing reflectance indices were evaluated for estimation of leaf water content and some new indices were developed and tested. Two semi-empirical indices developed in this study, (R850 - R2218)/(R850 - R1928) and (R850 - R1788)/(R850 - R1928), were found to show significantly stronger correlations with EWT than all other indices tested. It was also shown that these new indices were least sensitive to the effects of radiation scatter. The indices (R850 - R2218)/(R850 - R1928) and (R850 - R1788)/(R850 - R1928) are therefore proposed as two new indices for the remote estimation of vegetation water content.

Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance - Daughtry, C. S. T.; Walthall, C. L.; Kim, M. S.; de Colstoun, E. Brown; McMurtrey Iii, J. E. (Remote Sensing of Environment, 2000)
Farmers must balance the competing goals of supplying adequate N for their crops while minimizing N losses to the environment. To characterize the spatial variability of N over large fields, traditional methods (soil testing, plant tissue analysis, and chlorophyll meters) require many point samples. Because of the close link between leaf chlorophyll and leaf N concentration, remote sensing techniques have the potential to evaluate the N variability over large fields quickly. Our objectives were to (1) select wavelengths sensitive to leaf chlorophyll concentration, (2) simulate canopy reflectance using a radiative transfer model, and (3) propose a strategy for detecting leaf chlorophyll status of plants using remotely sensed data. A wide range of leaf chlorophyll levels was established in field-grown corn (Zea mays L.) with the application of 8 N levels: 0%, 12.5%, 25%, 50%, 75%, 100%, 125%, and 150% of the recommended rate. Reflectance and transmittance spectra of fully expanded upper leaves were acquired over the 400-nm to 1,000-nm wavelength range shortly after anthesis with a spectroradiometer and integrating sphere. Broad-band differences in leaf spectra were observed near 550 nm, 715 nm, and >750 nm. Crop canopy reflectance was simulated using the SAIL (Scattering by Arbitrarily Inclined Leaves) canopy reflectance model for a wide range of background reflectances, leaf area indices (LAI), and leaf chlorophyll concentrations. Variations in background reflectance and LAI confounded the detection of the relatively subtle differences in canopy reflectance due to changes in leaf chlorophyll concentration. Spectral vegetation indices that combined near-infrared reflectance and red reflectance (e.g., OSAVI and NIR/Red) minimized contributions of background reflectance, while spectral vegetation indices that combined reflectances of near-infrared and other visible bands (MCARI and NIR/Green) were responsive to both leaf chlorophyll concentrations and background reflectance. Pairs of these spectral vegetation indices plotted together produced isolines of leaf chlorophyll concentrations. The slopes of these isolines were linearly related to leaf chlorophyll concentration. A limited test with measured canopy reflectance and leaf chlorophyll data confirmed these results. The characterization of leaf chlorophyll concentrations at the field scale without the confounding problem of background reflectance and LAI variability holds promise as a valuable aid for decision making in managing N applications.

Assessing the Spatial Distribution of Plant Litter - Daughtry, C.; Hunt, E. R.; Walthall, C. L.; Gish, T. J.; Liang, Shunlin; Kramer, E.J. (Proceedings of the Tenth JPL Airborne Earth Science Workshop, 2001)
Quantifying crop residue cover on the soil surface is important for improving estimates of surface energy balance, net primary productivity, nutrient cycling, and carbon sequestration. Quantifying crop residue cover is also an important factor in controlling soil erosion and evaluating the effectiveness of conservation tillage practices. By reducing the movement of eroded soil into streams and rivers, the movement of nutrients and pesticides is reduced. The overall result is less soil erosion and improved water quality. Current methods for quantifying crop residue cover are tedious and somewhat subjective. The standard technique for measuring crop residue cover used by the USDA Natural Resources Conservation Service (NRCS) is visual estimation along a line-transect. Reviews of crop residue measurement techniques document recent modifications and illustrate the unresolved problems of current techniques. Rapid, accurate, and objective methods to quantify residue cover are needed. The objectives of this research were to: (1) determine the spectral reflectance of green plants, plant litters, and soils as a function of water content; (2) assess the limits of discrimination that can be expected; and (3) evaluate hyperspectral imaging data for providing information on the spatial distribution of plant litter. This research provides the scientific foundation required for sensor development and field testing.

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling - Dorigo, W. A.; Zurita-Milla, R.; de Wit, A. J. W.; Brazile, J.; Singh, R.; Schaepman, M. E. (International Journal of Applied Earth Observation and Geoinformation, 2007)
During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise. In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical-empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections. The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave. (c) 2006 Elsevier B.V. All rights reserved.
ISI Document Delivery No.: 165QP Times Cited: 39 Cited Reference Count: 181 Dorigo, W. A. Zurita-Milla, R. de Wit, A. J. W. Brazile, J. Singh, R. Schaepman, M. E. Conference on Advances in Airborne Electromagnetics and Remote Sensing of Agro-Ecosystems 2004 Wageningen, NETHERLANDS Elsevier science bv Amsterdam
Keywords: data assimilation; agroecosystem modeling; vegetation indices; canopy; reflectance modeling; biophysical variables; biochemical variables; parallel processing; radiative-transfer models; leaf-area index; hyperspectral vegetation; indexes; hydrologic data assimilation; multiple linear-regression; canopy chlorophyll density; ensemble kalman filter; bidirectional; reflectance; soil-moisture; crop models

Statistical derivation of fPAR and LAI for irrigated cotton and rice in arid Uzbekistan by combining multi-temporal RapidEye data and ground measurements - Ehammer, Andrea; Fritsch, Sebastian; Conrad, Christopher; Lamers, John; Dech, Stefan (2010)

Using insitu measurements to evaluate the new RapidEyeâ¢ satellite series for prediction of wheat nitrogen status - Eitel, J. U. H.; Long, D. S.; Gessler, P. E.; Smith, A. M. S. (International Journal of Remote Sensing, 2007)
This study assessed whether vegetation indices derived from broadband RapidEye? data containing the red edge region (690?730Â nm) equal those computed from narrow band data in predicting nitrogen (N) status of spring wheat (Triticum aestivum L.). Various single and combined indices were computed from in?situ spectroradiometer data and simulated RapidEye? data. A new, combined index derived from the Modified Chlorophyll Absorption Ratio Index (MCARI) and the second Modified Triangular Vegetation Index (MTVI2) in ratio obtained the best regression relationships with chlorophyll meter values (Minolta Soil Plant Analysis Development (SPAD) 502 chlorophyll meter) and flag leaf N. For SPAD, r 2 values ranged from 0.45 to 0.69 (p<0.01) for narrow bands and from 0.35 and 0.77 (p<0.01) for broad bands. For leaf N, r 2 values ranged from 0.41 to 0.68 (p<0.01) for narrow bands and 0.37 to 0.56 (p<0.01) for broad bands. These results are sufficiently promising to suggest that MCARI/MTVI2 employing broadband RapidEye? data is useful for predicting wheat N status.

Remote sensing of cotton nitrogen status using the Canopy Chlorophyll Content Index (CCCI) - El-Shikha, D. M.; Barnes, E. M.; Clarke, T. R.; Hunsaker, D. J.; Haberland, J. A.; Pinter, P. J.; Waller, P. M.; Thompson, T. L. (Transactions of the Asabe, 2008)
Various remote sensing indices have been used to infer crop nitrogen (N) status for field-scale nutrient management. However, such indices may indicate erroneous N status if there is a decrease in crop canopy density influenced by other factors, such as water stress. The Canopy Chlorophyll Content Index (CCCI) is a two-dimensional remote sensing index that has been proposed for inferring cotton N status. The CCCI uses reflectances in the near-infrared (NIR) and red spectral regions to account for seasonal changes in canopy density, while reflectances in the NIR and far-red regions are used to detect relative changes in canopy chlorophyll, a surrogate for N content. The primary objective of this study was to evaluate the CCCI and several other remote sensing indices for detecting the N status for cotton during the growing season. A secondary objective was to evaluate the ability of the indices to appropriately detect N in the presence of variable water status. Remote sensing data were collected during the 1998 (day of year [DOY] 114 to 310) and 1999 (DOY 106 to 316) cotton seasons in Arizona, in which treatments of optimal and low levels of N and water were imposed. In the 1998 season, water treatments were not imposed until late in the season (DOY 261), well after full cover Following an early season N application in 1998 for the optimal (DOY 154) but not the low N treatment, the CCCI detected significant differences in crop N status between the N treatments starting on DOY 173, when canopy cover was about 30%. A common vegetation index, the ratio of NIR to red (RVI), also detected significant separation between N treatments, but RVI detection occurred 16 days after the CCCI response. After an equal amount of N was applied to both optimal and low N treatments on DOY 190 in 1998, the CCCI indicated comparable N status for the N treatments on DOY 198, a trend not detected by RVI. In the 1999 season, both N and water treatments were imposed early and frequently during the season. The N status was poorly described by both the CCCI and RVI under partial canopy conditions when water status differed among treatments. However, once full canopy was obtained in 1999, the CCCI provided reliable N status information regardless of water status. At full cotton cover, the CCCI was significantly correlated with measured parameters of N status, including petiole NO3-N (r = 0.74), SPAD chlorophyll (r = 0.65), and total leaf N contents (r = 0.86). For well-watered cotton, the CCCI shows promise as a useful indicator of cotton N status after the canopy reaches about 30% cover However, further study is needed to develop the CCCI as a robust N detection tool independent of water stress.
ISI Document Delivery No.: 294QY Times Cited: 6 Cited Reference Count: 37 El-Shikha, D. M. Barnes, E. M. Clarke, T. R. Hunsaker, D. J. Haberland, J. A. Pinter, P. J., Jr. Waller, P. M. Thompson, T. L. Amer soc agricultural & biological engineers St joseph
Keywords: canopy reflectance; fertility detection; radiometers; spectral analysis; water stress; reflectance indexes; spectral radiance; winter-wheat; corn leaves; grain-yield; water; stress; plants; light; field

Indices spectraux pour la dégradation des milieux naturels en Tunisie aride - Escadafal, R.; Belghith, A.; Ben Moussa, H. (6ème Symp. Int. Mesures Physiques et Signatures en Télédétection, ISPRS-CNES, 1994)

Improvement in remote sensing of low vegetation cover in arid regions by correcting vegetation indices for soil "noise' - Escadafal, R.; Huete, A. (Comptes Rendus - Academie des Sciences, Serie II, 1991)
The variations of near-infrared/red reflectance ratios of ten arid soil samples were correlated with a "redness index' computed from red and green spectral bands. These variations have been shown to limit the performances of vegetation indices (NDVI and soil adjusted VI) in discriminating low vegetation covers. The redness index is used to adjust for this "soil noise'. The "noise-corrected' SAVI was able to assess vegetation amounts with an error four times smaller than the uncorrected NDVI. These is an abridged English version. -from English summary
Keywords: GEOBASE Subject Index: arid region; biomass; soil noise; vegetation index

Crop yield estimation by satellite remote sensing - Ferencz, C.; Bognar, P.; Lichtenberger, J.; Hamar, D.; Tarscai, G.; Timar, G.; Molnar, G.; Pasztor, S.; Steinbach, P.; Szekely, B.; Ferencz, O. E.; Ferencz-Arkos, I. (International Journal of Remote Sensing, 2004)
Two methods for estimating the yield of different crops in Hungary from satellite remote sensing data are presented. The steps of preprocessing the remote sensing data (for geometric, radiometric, atmospheric and cloud scattering correction) are described. In the first method developed for field level estimation, reference crop fields were selected by using Landsat Thematic Mapper (TM) data for classification. A new vegetation index (General Yield Unified Reference Index (GYURI)) was deduced using a fitted double-Gaussian curve to the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data during the vegetation period. The correlation between GYURI and the field level yield data for corn for three years was R(2)=0.75. The county-average yield data showed higher correlation (R(2)=0.93). A significant distortion from the model gave information of the possible stress of the field. The second method presented uses only NOAA AVHRR and officially reported county-level yield data. The county-level yield data and the deduced vegetation index, GYURRI, were investigated for eight different crops for eight years. The obtained correlation was high (R(2)=84.6-87.2). The developed robust method proved to be stable and accurate for operational use for county-, region- and country-level yield estimation. The method is simple and inexpensive for application in developing countries, too.
ISI Document Delivery No.: 856DL Times Cited: 8 Cited Reference Count: 53 Ferencz, C Bognar, P Lichtenberger, J Hamar, D Tarscai, G Timar, G Molnar, G Pasztor, S Steinbach, P Szekely, B Ferencz, OE Ferencz-Arkos, I Taylor & francis ltd Abingdon
Keywords: high-resolution radiometer; near-infrared channels; noaa-avhrr; cloud; detection; postlaunch calibration; spectral reflectance; winter-wheat; sensed data; leaf-area; landsat

Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data - Galvão, Lênio Soares; Formaggio, Antônio Roberto; Tisot, Daniela Arnold (Remote Sensing of Environment, 2005)
Hyperspectral data acquired by the Hyperion instrument, on board the Earth Observing-1 (EO-1) satellite, were evaluated for the discrimination of five important Brazilian sugarcane varieties (RB72-454, SP80-1816, SP80-1842, SP81-3250, and SP87-365). The radiance values were converted into surface reflectance images by a MODTRAN4-based technique. To discriminate varieties with similar reflectance values, multiple discriminant analysis (MDA) was applied over the data. To obtain an adequate discriminant function, a stepwise method was used to select the best variables among surface reflectance values, ratios of reflectance, and several spectral indices potentially sensitive to changes in chlorophyll content, leaf water, and lignin-cellulose. Results showed that the five Brazilian sugarcane varieties were discriminated using EO-1 Hyperion data. Differences in canopy architecture affected sunlight penetration and reflectance, resulting in a higher reflectance for planophile (e.g., SP81-3250) than erectophile (e.g., SP80-1842) sugarcane plants. The variety SP80-1842 presented lower reflectance values, deeper lignin-cellulose absorption bands at 2103 nm and 2304 nm, shallower leaf liquid water absorption bands at 983 nm and 1205 nm, and lower leaf liquid water content than the other sugarcane varieties. To discriminate this cultivar, a single near-infrared (NIR) band threshold was used. To discriminate the other four sugarcane varieties with similar reflectance values, MDA was used producing a classification accuracy of 87.5% for a hold-out set of pixels. The comparison between the ground truth data and the MDA-derived classification image confirmed the model' capacity to differentiate the varieties accurately. The best results were obtained for the cultivar SP87-365 that presented the lowest spectral variability in the discriminant space. Some misclassified areas were associated with the cultivars SP80-1816 and SP81-3250 that showed the highest spectral variability.
Keywords: Hyperspectral remote sensing; Sugarcane varieties; Hyperion; Discriminant analysis; Agriculture; Crops

A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency - Gamon, J. A.; PeÃ±uelas, J.; Field, C. B. (Remote Sensing of Environment, 1992)

Assessing leaf pigment content and activity with a reflectometer - Gamon, J. A.; Surfus, J. S. (New Phytologist, 1999)
This study explored reflectance indices sampled with a âleaf reflectometerâ as measures of pigment content for leaves of contrasting light history, developmental stage and functional type (herbaceous annual versus sclerophyllous evergreen). We employed three reflectance indices: a modified normalized difference vegetation index (NDVI), an index of chlorophyll content; the red/green reflectance ratio (RRED∶RGREEN), an index of anthocyanin content; and the change in photochemical reflectance index upon darkâlight conversions (ΔPRI), an index of xanthophyll cycle pigment activity. In Helianthus annuus (sunflower), xanthophyll cycle pigment amounts were linearly related to growth light environment; leaves in full sun contained approximately twice the amount of xanthophyll cycle pigments as leaves in deep shade, and at midday a larger proportion of these pigments were in the photoprotective, de-epoxidized forms relative to shade leaves. Reflectance indices also revealed contrasting patterns of pigment development in leaves of contrasting structural types (annual versus evergreen). In H. annuus sun leaves, there was a remarkably rapid increase in amounts of both chlorophyll and xanthophyll cycle pigments along a leaf developmental sequence. This pattern contrasted with that of Quercus agrifolia (coast live oak, a sclerophyllous evergreen), which exhibited a gradual development of both chlorophyll and xanthophyll cycle pigments along with a pronounced peak of anthocyanin pigment content in newly expanding leaves. These temporal patterns of pigment development in Q. agrifolia leaves suggest that anthocyanins and xanthophyll cycle pigments serve complementary photoprotective roles during early leaf development. The results illustrate the use of reflectance indices for distinguishing divergent patterns of pigment activity in leaves of contrasting light history and functional type.
Keywords: leaf development, leaf pigments, anthocyanins, chlorophyll, xanthophyll cycle, leaf reflectometer, reflectance indices, photoprotection

Retrieval of vegetation biophysical variables from CHRIS/PROBA data in the SPARC campaing - Gandia, S.; FernÃ¡ndez, G.; GarcÃa, J. C.; & Moreno, J. (Proceedings of the 2nd CHRISProba Workshop ESAESRIN Frascati Italy 2830, 2004)
In the context of the SPARC campaign, a total of 10 different view angle images are available from CHRIS/PROBA data, all of them acquired in Mode 1 (62 spectral bands). These data make possible to test some of the algorithms developed to extract vegetation biophysical variables from high-spectral resolution data with multiangular capabilities, in the context of the SPECTRA mission. Validation of retrievals with the large dataset of ground measurements that is available in SPARC represents a unique opportunity to exploit the innovative CHRIS/PROBA data. Variability in ground measurements was evaluated by statistical techniques, according to the sampling used in the data collection. Two different approaches have been followed in the retrieval of vegetation biophysical variables. First, a large number of spectral indices have been tested with the available spectral information. The second method used is based on model inversion techniques. A combination of adapted versions of PROSPECT 1, and SAIL 2, models have been tested in forward mode, by using all available ground measurements to reproduce the top-of-canopy reflectance, and then compare these forward simulations with atmospherically corrected CHRIS data.

NDWIâA normalized difference water index for remote sensing of vegetation liquid water from space - Gao, Bo-cai (Remote Sensing of Environment, 1996)

Monitoring Leaf Area Index of Mediterranean oak woodlands: Comparison of remotely-sensed estimates with simulations from an ecological process-based model - Garson, D. Caraux; Lacaze, B. (International Journal of Remote Sensing, 2003)
Annual vegetation abundance mapping was carried out within the DeMon II European project over a period of 12 years (1984-1996). The project relied on advanced satellite-based methods for spatial and temporal monitoring of Mediterranean oak woodlands by means of a series of Landsat Thematic Mapper (TM) satellite data. A standardized approach developed previously focuses on the Languedoc site, Hautes Garrigues, a typical sensitive Mediterranean region, but now recovering after centuries of grazing and agricultural activities. After geometric and radiometric rectification of nine full Landsat TM scenes with a refined correction in a smaller area of 75 km 2 75 km, a GIS database was created containing satellite data, thematic maps of vegetation, geological maps, climatic data and field measurements. An empirical relation between radiometric ground truth measurements and satellite derived Normalized Difference Vegetation Index (NDVI) allows us to derive Leaf Area Index (LAI). An ecological process-based model (Forest BGC) has been adapted to simulate ecosystem processes in a satisfying way at a local scale. Consistent results were obtained from remote sensing data and from simulations at a local scale, suggesting the possible use of remote-sensing data to monitor vegetation abundance changes at a regional scale. Without considering human disturbances, it can be noted that not much variation of LAI induced by natural factors can be detected over the considered 12-year period.

Novel algorithms for remote estimation of vegetation fraction - Gitelson, A. A.; Kaufman, Y. J.; Stark, R.; Rundquist, D. (Remote Sensing of Environment, 2002)
Spectral properties of a wheat canopy with vegetation fraction (VF) from 0% to 100% in visible and near-infrared (NIR) ranges of the spectrum were studied in order to devise a technique for remote estimation of VF. When VF was <60%, from emergence till middle of the elongation stage, four distinct, and quite independent, spectral bands of reflectance existed in the visible range of the spectrum: 400 to 500 nm, 530 to 600 nm, near 670 nm, and around 700 nm. When VF was between 60% and 100%, reflectance in the NIR leveled off or even decreases with an increase of VF. The decreased reflectance in the NIR, occurring at or near the midseason, can be a limiting factor in the use of that spectral region for VF estimation. It was found that for VF>60%, the information content of reflectance spectra in visible range can be expressed by only two independent pairs of spectral bands: (1) the blue from 400 to 500 nm and the red near 670 nm; (2) the green around 550 nm and the red edge region near 700 nm. We propose using only the visible range of the spectrum to quantitatively estimate VF. The green (as well as a 700-nm band) and the red (near 670 nm) reflectances were used in developing new indices, which were linearly proportional to wheat VF ranging from 0% to 100%. The Atmospherically Resistant Vegetation Index (ARVI) concept was used to correct indices for atmospheric effects. Visible Atmospherically Resistant Index in the form VARI=(Rgreen-Rred)/(Rgreen+Rred-Rblue) was found to be minimally sensitive to atmospheric effects allowing estimation of VF with an error of <10% in a wide range of atmospheric optical thickness. Validation of the newly suggested technique was carried out using wheat independent data sets and reflectance data obtained for cornfields in Nebraska. Predicted green VF was compared with retrieved from digital images. Despite the fact that the reflectance contrast among the visible channels is much smaller than between the visible and NIR, the sensitivity of suggested indices to moderate to high values of VF is much higher than for the Normalized Difference Vegetation Index (NDVI), and the error in VF prediction did not exceed 10%. Suggested indices will complement the widely used NDVI, ARVI, Soil Adjusted Vegetation Index (SAVI) and others, which are based on the red and the NIR bands in VF estimation, and also Green Atmospherically Resistant Index (GARI), which is based on the green and the NIR bands.

Remote estimation of chlorophyll content in higher plant leaves - Gitelson, A. A.; Merzlyak, M. N. (International Journal of Remote Sensing, 1997)
Abstract Indices for the non-destructive estimation of chlorophyll content were formulated using various instruments to measure reflectance and absorption spectra in visible and near-infrared ranges, as well as chlorophyll contents from several non-related species from different climatic regions. The proposed new algorithms are simple ratios between percentage reflectance at spectral regions that are highly sensitive (540 to 630nm and around 700nm) and insensitive (nearinfrared) to variations in chlorophyll content: R NIR / R 700 and R NIR / R 550. The developed algorithms predicting leaf chemistry from the leaf optics were validated for nine plant species in the range of chlorophyll content from 0.27 to 62.9mug cm -2. An error of less than 4.2 mugcm -2 in chlorophyll prediction was achieved. The use of green and red (near 700nm) channels increases the sensitivity of NDVI to chlorophyll content by about five-fold.

Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation - Gitelson, Anatoly A. (Journal of Plant Physiology, 2004)
Summary The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring, analyzing, and mapping temporal and spatial distributions of physiological and biophysical characteristics of vegetation. It is well documented that the NDVI approaches saturation asymptotically under conditions of moderate-to-high aboveground biomass. While reflectance in the red region (ρred) exhibits a nearly flat response once the leaf area index (LAI) exceeds 2, the near infrared (NIR) reflectance (ρNIR) continue to respond significantly to changes in moderate-to-high vegetation density (LAI from 2 to 6) in crops. However, this higher sensitivity of the ρNIR has little effect on NDVI values once the ρNIR exceeds 30 %. In this paper a simple modification of the NDVI was proposed. The Wide Dynamic Range Vegetation Index, WDRVI = (a * ρNIR-ρred)/(a * ρNIR+ρred), where the weighting coefficient a has a value of 0.1â0.2, increases correlation with vegetation fraction by linearizing the relationship for typical wheat, soybean, and maize canopies. The sensitivity of the WDRVI to moderate-to-high LAI (between 2 and 6) was at least three times greater than that of the NDVI. By enhancing the dynamic range while using the same bands as the NDVI, the WDRVI enables a more robust characterization of crop physiological and phenological characteristics. Although this index needs further evaluation, the linear relationship with vegetation fraction and much higher sensitivity to change in LAI will be especially valuable for precision agriculture and monitoring vegetation status under conditions of moderate-to-high density. It is anticipated that the new index will complement the NDVI and other vegetation indices that are based on the red and NIR spectral bands.
Keywords: leaf area index; reflectance; remote estimation; vegetation fraction; vegetation index

Use of a green channel in remote sensing of global vegetation from EOS-MODIS - Gitelson, Anatoly A.; Kaufman, Yoram J.; Merzlyak, Mark N. (Remote Sensing of Environment, 1996)
Most animals use a âgreenâ spectral range to remotely sense the presence and vitality of vegetation. While humans possess the same ability in their eyes, man-made space-borne sensors that sense evolution of global vegetation, have so far used a combination of the red and near infrared channels instead. In this article we challenge this approach, using measurements of reflectance spectra from 400 nm to 750 nm with spectral resolution of 2 nm, with simultaneous determination of pigment concentrations of mature and autumn senescing leaves. We show that, for a wide range of leaf greenness, the maximum sensitivity of reflectance coincides with the red absorption maximum of chlorophyll-a (Chl-a) at 670 nm. However, for yellow-green to green leaves (with Chl-a more than 3â5 μg/cm2), the reflectance near 670 nm is not sensitive to chlorophyll concentration because of saturation of the relationship of absorptions versus chlorophyll concentration. Maximum sensitivity of Chl-a concentration for a wide range of its variation (0.3â45 μg/cm2) was found, not surprisingly so, around the green band from 520 nm to 630 nm and also near 700 nm. We found that the inverse of the reflectance in the green band was proportional to Chl-a concentration with correlation r2 > 0.95. This band will be present on several future satellite sensors with a global view of vegetation (SeaWiFS to be launched in 1996, Polder on ADEOS-1 also in 1996, and MODIS on EOS in 1998 and 2000). New indexes that use the green channel and are resistant to atmospheric effects are developed. A green NDVI = (ϱnir − ϱgreen(ϱnir + ϱgreen) was tested for a range of Chl-a from 0.3 μg/cm2 to 45 μg/cm2, and found to have an error in the chlorophyll a derivation at leaf level of less than 3 μg/cm2. The new index has wider dynamic range than the NDVI and is, on average, at least five times more sensitive to Chl-a concentration. A green atmospherically resistant vegetation index (GARI), tailored on the concept of ARVI (Kaufman and TanrÃ©, 1992), is developed and is expected to be as resistant to atmospheric effects as ARVI but more sensitive to a wide range of Chl-a concentrations. While NDVI and ARVI are sensitive to vegetation fraction and to rate of absorption of photosynthetic solar radiation, a green vegetation index like GARI should be added to sense the concentration of chlorophyll, to measure the rate of photosynthesis and to monitor plant stress.

Non-destructive and remote sensing techniques for estimation of vegetation status - Gitelson, Anatoly A.; Merzlyak, Mark N.; Zur, Y.; Stark, R.; Gritz, U. (Third European Conference on Precision Agriculture, 2001)

Remote estimation of leaf area index and green leaf biomass in maize canopies - Gitelson, Anatoly A.; ViÃ±a, AndrÃ©s; Arkebauer, Timothy J.; Rundquist, Donald C.; Keydan, Galina: Leavitt, Bryan (Geophys. Res. Lett., 2003)
Leaf area index (LAI) is an important variable for climate modeling, estimates of primary production, agricultural yield forecasting, and many other diverse studies. Remote sensing provides a considerable potential for estimating LAI at local to regional and global scales. Several spectral vegetation indices have been proposed, but their capacity to estimate LAI is highly reduced at moderate-to-high LAI. In this paper, we propose a technique to estimate LAI and green leaf biomass remotely using reflectances in two spectral channels either in the green around 550 nm, or at the red edge near 700 nm, and in the NIR (beyond 750 nm). The technique was tested in agricultural fields under a maize canopy, and proved suitable for accurate estimation of LAI ranging from 0 to more than 6.
Keywords: Remote sensing, Instruments and techniques. General or miscellaneous

Vegetation Index Methods for Estimating Evapotranspiration by Remote Sensing - Glenn, E. P.; Nagler, P. L.; Huete, A. R. (Surveys in Geophysics, 2010)
Evapotranspiration (ET) is the largest term after precipitation in terrestrial water budgets. Accurate estimates of ET are needed for numerous agricultural and natural resource management tasks and to project changes in hydrological cycles due to potential climate change. We explore recent methods that combine vegetation indices (VI) from satellites with ground measurements of actual ET (ET(a)) and meteorological data to project ET(a) over a wide range of biome types and scales of measurement, from local to global estimates. The majority of these use time-series imagery from the Moderate Resolution Imaging Spectrometer on the Terra satellite to project ET over seasons and years. The review explores the theoretical basis for the methods, the types of ancillary data needed, and their accuracy and limitations. Coefficients of determination between modeled ET(a) and measured ET(a) are in the range of 0.45-0.95, and root mean square errors are in the range of 10-30% of mean ET(a) values across biomes, similar to methods that use thermal infrared bands to estimate ET(a) and within the range of accuracy of the ground measurements by which they are calibrated or validated. The advent of frequent-return satellites such as Terra and planed replacement platforms, and the increasing number of moisture and carbon flux tower sites over the globe, have made these methods feasible. Examples of operational algorithms for ET in agricultural and natural ecosystems are presented. The goal of the review is to enable potential end-users from different disciplines to adapt these methods to new applications that require spatially-distributed ET estimates.
ISI Document Delivery No.: 681LY Times Cited: 7 Cited Reference Count: 112 Glenn, Edward P. Nagler, Pamela L. Huete, Alfredo R. Springer Dordrecht
Keywords: NDVI; Enhanced Vegetation Index; Fluxnet; MODIS; Remote sensing; land-surface evaporation; basal crop coefficients; colorado river delta; modis satellite data; canopy reflectance; potential evapotranspiration; riparian evapotranspiration; meteorological data; eddy covariance; energy-balance

Review - Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape - Glenn, Edward; Huete, Alfredo; Nagler, Pamela; Nelson, Stephen (Sensors, 2008)

Advanced vegetation indices optimized for up-coming sensors: Design, performance, and applications - Gobron, N.; Pinty, B.; Verstraete, M. M.; Widlowski, J. L. (Geoscience and Remote Sensing, IEEE Transactions on, 2000)
This paper describes the implementation of a physical and mathematical approach to designing advanced vegetation indices optimized for future sensors operating in the solar domain such as the medium resolution imaging spectrometer (MERIS), the global imager (GLI), and the VEGETATION instrument, and proposes an initial evaluation of such indices. These optimized indices address sensor-specific issues such as dependencies with respect to the actual spectral response of the sensor as well as the natural sensitivity of remote sensing measurements to illumination and observing geometry, to atmospheric absorption and scattering effects, and to soil color or brightness changes. The derivation of vegetation index formulae optimized to estimate the same vegetation property fraction of absorbed photosynthetically active radiation (FAPAR) from data generated by different sensors allows the comparison of their relative performances compared with existing vegetation indices, both from a theoretical and experimental point of view and permits the creation of global products, as well as the constitution of long time series from multiple sensors.
Keywords: geophysical techniques, remote sensing, vegetation mapping, FAPAR, GLI, IR, MERIS, SPOT, VEGETATION, advanced vegetation indices, formula, fraction of absorbed photosynthetically active radiation, geophysical measurement technique, global imager, infrared, mathematical approach, medium resolution imaging spectrometer, optical method, vegetation index, visible

Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation - Goel, Narendra S.; Qin, Wenhan (Remote Sensing Reviews, 1994)
Abstract Vegetation Indices (VIs) are often used to estimate important biophysical parameters, like LAI and Fpar, from vegetation canopy reflectance data. In this study, a three?dimensional model (Diana) is utilized to generate architecturally realistic tree and crop canopies in various development stages and to calculate bidirectional reflectance factors in the principal plane. We investigate the influence of various factors like soil brightness, optical properties of canopy elements, leaf angle distribution, spacing distance between plants and solar and view geometries on relationships between VI and Fpar, LAI and percentage ground cover (GC) in order to determine optimal VI and viewing conditions for the estimation of Fpar and LAI/GC. These simulation studies suggest that: (1) in most cases, Vis using off?nadir reflectances are more informative and useful than those based on nadir reflectances; (2) the optimal VI and sun/view geometries are usually different for inferring different parameters, depending on canopy architecture; (3) LAI can be practically estimated by VI only for homogeneous canopies while GC and Fpar can be inferred even for an inhomogeneous canopy; and (4) when optical properties of vegetation elements vary within a canopy, neither LAI/GC nor Fpar can be estimated by means of VI method with an acceptable accuracy.

Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data - GonzÃ¡lez-Sanpedro, MC; Le Toan, T; Moreno, J; Kergoat, L; Rubio, E (Remote Sensing of Environment, 2008)

Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture - Haboudane, Driss; Miller, John R.; Pattey, Elizabeth; Zarco-Tejada, Pablo J.; Strachan, Ian B. (Remote Sensing of Environment, 2004)
A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters, as well as to external factors affecting canopy reflectance. In this context, leaf and canopy radiative transfer models are valuable for modeling and understanding the behavior of such indices. In the present work, PROSPECT and SAILH models have been used to simulate a wide range of crop canopy reflectances in an attempt to study the sensitivity of a set of vegetation indices to green leaf area index (LAI), and to modify some of them in order to enhance their responsivity to LAI variations. The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI, and to develop new algorithms that adequately predict the LAI of crop canopies. Analyses based on both simulated and real hyperspectral data were carried out to compare performances of existing vegetation indices (Normalized Difference Vegetation Index [NDVI], Renormalized Difference Vegetation Index [RDVI], Modified Simple Ratio [MSR], Soil-Adjusted Vegetation Index [SAVI], Soil and Atmospherically Resistant Vegetation Index [SARVI], MSAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI]) and to design new ones (MTVI1, MCARI1, MTVI2, and MCARI2) that are both less sensitive to chlorophyll content variations and linearly related to green LAI. Thorough analyses showed that the above existing vegetation indices were either sensitive to chlorophyll concentration changes or affected by saturation at high LAI levels. Conversely, two of the spectral indices developed as a part of this study, a modified triangular vegetation index (MTVI2) and a modified chlorophyll absorption ratio index (MCARI2), proved to be the best predictors of green LAI. Related predictive algorithms were tested on CASI (Compact Airborne Spectrographic Imager) hyperspectral images and, then, validated using ground truth measurements. The latter were collected simultaneously with image acquisition for different crop types (soybean, corn, and wheat), at different growth stages, and under various fertilization treatments. Prediction power analysis of proposed algorithms based on MCARI2 and MTVI2 resulted in agreements between modeled and ground measurement of non-destructive LAI, with coefficients of determination (r2) being 0.98 for soybean, 0.89 for corn, and 0.74 for wheat. The corresponding RMSE for LAI were estimated at 0.28, 0.46, and 0.85, respectively.
Keywords: Hyperspectral; Spectral indices; Green LAI; Prediction algorithms; Chlorophyll content; Precision agriculture

Relationships between blue- and red-based vegetation indices and leaf area and yield of alfalfa - Hancock, D. W.; Dougherty, C. T. (Crop Science, 2007)
The need for site-specific yield assessments of alfalfa (Medicago sativa L.) has spurred interest in developing methods to remotely sense biomass at harvest, Relationships between reflectance-based vegetation indices (Vis) and yield and yield-components of alfalfa have not been fully characterized. The objectives of this study were to evaluate the relationships between blue- and red-reflectance based Vis and canopy variables such as leaf area index (LAI), mass shoot(-1), shoot length, and alfalfa yield. Canopy reflectance was obtained with two reflectance spectrometers 1 d before each of five harvests in 2005 within rainfed and subsurface drip-irrigated alfalfa. Blue- and red-based normalized difference vegetation indices (NDVIs) and wide dynamic range vegetation indices (WDRVIs) at three levels of a near-infrared (NIR) reflectance-scalar ('alpha' = 0.1, 0.05, and 0.01) were calculated and regressed on alfalfa canopy variables. A quadratic-plateau model was used to determine when VIs no longer detected yield increments. Both blue- and red-based NDVIs and WDRVIs exhibited significant (P < 0.0001) saturative responses to LAI, yield components, and dry matter (DM) yield. Decreasing a widened the estimable yield range (0-1.82 vs. 0-2.76 Mg ha(-1) and 0-2.60 vs. 0-3.74 Mg ha-1, respectively) of both blue- and red-based WDRVIs. Significant (P < 0.0001) yield regression models within the effective range of the VIs (<Yield(max)) were found within two harvests in 2005 and when data were pooled across all harvests. These results indicate that the use of a NIR reflectance-scalar can extend the range of herbage biomass (to 3.74 and 2.76 Mg ha(-1), respectively) within which blue- and red-based indices may be used to estimate alfalfa yield.
ISI Document Delivery No.: 237XF Times Cited: 1 Cited Reference Count: 30 Hancock, Dennis W. Dougherty, Charles T. Crop science soc amer Madison
Keywords: mass-flow-rate; forage harvester; crop management; reflectance; sensors; grass

Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data - Heiskanen, J. (International Journal of Remote Sensing, 2006)

SWIR-based spectral indices for assessing nitrogen content in potato fields - Herrmann, I.; Karnieli, A.; Bonfil, D. J.; Cohen, Y.; Alchanatis, V. (International Journal of Remote Sensing, 2010)
Nitrogen (N) is an essential element in plant growth and productivity, and N fertilizer is therefore of prime importance in cultivated crops. The amount and timing of N application has economic and environmental implications and is consequently considered to be an important issue in precision agriculture. Spectral indices derived from handheld, airborne and spaceborne spectrometers are used for assessing N content. The majority of these indices are based on indirect indicators, mostly chlorophyll content, which is proven to be physiologically linked to N content. The current research aimed to explore the performance of new N spectral indices dependent upon the shortwave infrared (SWIR) region (1200?2500 nm), and particularly the 1510 nm band because it is related directly to N content. Traditional nitrogen indices (NIs) and four proposed new SWIR-based indices were tested with canopy-level spectral data obtained during two growing seasons in potato experimental plots in the northwest Negev, Israel. Above-ground biomass samples were collected at the same location of the spectral sampling to provide in-situ N content data. The performance of all indices was evaluated by three methods: (1) correlations between the existing and proposed indices and N as well as correlations among the indices themselves; (2) the root mean square error prediction (RMSEP) of the N content; and (3) the indices relative sensitivity (S r) to the N content. The results reveal a firm advantage for the proposed SWIR-based indices in their ability to predict, and in their sensitivity to, N content. The best index is one that combines information from the 1510 and 660 nm bands but no significant differences were found among the new SWIR-based indices.

Interpreting spatial heterogeneity of crop yield with a process model and remote sensing - Hu, S.; Mo, X. G. (Ecological Modelling, 2011)
A process-based crop growth model (Vegetation Interface Processes (VIP) model) is used to estimate crop yield with remote sensing over the North China Plain. Spatial pattern of the key parameter-maximum catalytic capacity of Rubisco (V(cmax)) for assimilation is retrieved from Normalized Difference of Vegetation Index (NDVI) from Terra-MODIS and statistical yield records. The regional simulation shows that the agreements between the simulated winter wheat yields and census data at county-level are quite well with R(2) being 0.41-0.50 during 2001-2005. Spatial variability of photosynthetic capacity and yield in irrigated regions depend greatly on nitrogen input. Due to the heavy soil salinity, the photosynthetic capacity and yield in coastal region is less than 50 mu mol C m(-2) s(-1) and 3000 kg ha(-1), respectively, which are much lower than that in non-salinized region, 84.5 mu mol C m(-2) s(-1) and 5700 kg ha(-1). The predicted yield for irrigated wheat ranges from 4000 to 7800 kg ha(-1), which is significantly larger than that of rainfed, 1500-3000 kg ha(-1). According to the path coefficient analysis, nitrogen significantly affects yield, by which water exerts noticeably indirect influences on yield. The effect of water on yield is regulated, to a certain extent, by crop photosynthetic capacity and nitrogen application. It is believed that photosynthetic parameters retrieved from remote sensing are reliable for regional production prediction with a process-based model. (C) 2010 Elsevier B.V. All rights reserved.
ISI Document Delivery No.: 796GB Times Cited: 1 Cited Reference Count: 59 Hu, Shi Mo, Xingguo 973 Basic Research Project[2010CB428404]; China MOST[0911]; CAS[KZCX2-YW-Q06-1] This study was jointly supported by 973 Basic Research Project (2010CB428404), International Cooperation Project (0911) of China MOST, and the Innovation project of CAS (KZCX2-YW-Q06-1). Many thanks to the two anonymous reviews and the Editor whose pertinent comments have greatly improved the quality of this paper. Elsevier science bv Amsterdam Si
Keywords: Remote sensing data; V(cmax); VIP model; water-use efficiency; north china plain; winter-wheat; durum-wheat; gas-exchange; elevated co2; grain-yield; corn yield; ndvi data; nitrogen

Spatial Modeling and Variability Analysis for Modeling and Prediction of Soil and Crop Canopy Coverage Using Multispectral Imagery from an Airborne Remote Sensing System - Huang, Y.; Lan, Y.; Ge, Y.; Hoffmann, W. C.; Thomson, S. J. (Transactions of the Asabe, 2010)
Spatial modeling and variability analysis of soil and crop canopy coverage has been accomplished using aerial multispectral images. Multispectral imagery was acquired using an MS-4100 multispectral camera at different flight altitudes over a 40 ha cotton field. After the acquired images were geo-registered and processed, spatial relationships between the aerial images and ground-based soil conductivity and NDVI (normalized difference vegetation index) measurements were estimated and compared using two spatial analysis approaches (model-driven spatial regression and data-driven geostatistics) and one non-spatial approach (multiple linear regression). Comparison of the three approaches indicated that OLS (ordinary least squares) solutions from multiple linear regression models performed worst in modeling ground-based soil conductivity and NDVI with high AIC (Akaike information criterion) (-668.3 to 2980) and BIC (Bayesian information criterion) (-642.4 to 3006) values. Spatial regression and geostatistics performed much better in modeling soil conductivity, with low AIC (2698 to 2820) and BIC (2732 to 2850) values. For modeling ground-based NDVI, the AIC and BIC values were -681.7 and -652.1, respectively, for spatial error regression and -679.8 and -646.5, respectively, for geostatistics, which were only moderate improvements over OLS (-668.3 and -642.4). Validation of the geostatistical models indicated that they could predict soil conductivity much better than the corresponding multiple linear regression models, with lower RMSE (root mean squared error) values (0.096 to 0.186, compared to 0.146 to 0.306). Results indicated that the aerial images could be used for spatial modeling and prediction, and they were informative for spatial prediction of ground soil and canopy coverage variability. The methods used for this study could help deliver baseline data for crop monitoring with remote sensing and establish a procedure for general crop management.
ISI Document Delivery No.: 654UZ Times Cited: 0 Cited Reference Count: 46 Huang, Y. Lan, Y. Ge, Y. Hoffmann, W. C. Thomson, S. J. Amer soc agricultural & biological engineers St joseph
Keywords: Aerial multispectral imagery; Crop management; Geostatistics; Image; processing; Spatial regression; maximum-likelihood; weed infestations; management; regression; yield; integration; attributes; variograms; moments; index

A soil-adjusted vegetation index (SAVI) - Huete, A. R. (Remote Sensing of Environment, 1988)
A transformation technique is presented to minimize soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. Graphically, the transformation involves a shifting of the origin of reflectance spectra plotted in NIR-red wavelength space to account for first-order soil-vegetation interactions and differential red and NIR flux extinction through vegetated canopies. For cotton (Gossypium hirsutum L. var DPI-70) and range grass (Eragrosticslehmanniana Nees) canopies, underlain with different soil backgrounds, the transformation nearly eliminated soil-induced variations in vegetation indices. A physical basis for the soil-adjusted vegetation index (SAVI) is subsequently presented. The SAVI was found to be an important step toward the establishment of simple Â°lobalâ that can describe dynamic soil-vegetation systems from remotely sensed data

Normalization of multidirectional red and NIR reflectances with the SAVI - Huete, A. R.; Hua, G.; Qi, J.; Chehbouni, A.; van Leeuwen, W. J. D. (Remote Sensing of Environment, 1992)
Directional reflectance measurements were made over a semidesert gramma (Bouteloua spp.) grassland at various times of the growing season. Azimuthal strings of view angle measurements from + 40Â° to − 40Â° were made for various solar zenith angles and soil moisture conditions. The sensitivity of the normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI) to these bidirectional measurements was assessed for purposes of improving remote temporal monitoring of vegetation activity. The NDVI response from the grassland canopy was strongly anisotropic about nadir view angles while the SAVI response was symmetric about nadir. This occurred for all sun angles, soil moisture condition, and grass densities. This enabled variations in SAVI-view angle response to be minimized with a cosine function. It is expected that this study will aid in improving the characterization of vegetation temporal activity from Landsat TM, SPOT, AVHRR, and the Earth Observing System MODIS sensor.

Soil and atmosphere influences on the spectra of partial canopies - Huete, A. R.; Jackson, R. D. (Remote Sensing of Environment, 1988)
An atmospheric radiant transfer model was used to compare ground-measured radiances over partially vegetated canopies with their simulated responses at the top of a clear (100 km meteorological range) and a turbid (10 km) atmosphere. Radiance measurements in the first four bands of the Thematic Mapper were taken over incomplete cotton (Gossypium hirsutum L.) and Lehmann lovegrass (Eragrostis lehmanniana Nees) canopies with different soil backgrounds separately inserted underneath. Atmospheric influences on the spectra of partial canopies were found to be significantly dependent on the âbrightnessâ of the underlying soil. The change in canopy red and near-infrared radiance between the ground and the top of the atmosphere was such that an increase, decrease, or no change could be observed, depending on the magnitude of the canopy substrate contribution. Both increasing soil âbrightnessâ and atmospheric turbidity lowered the ratio (RVI) and normalized difference vegetation index values (NDVI). Consequently, atmospheric-induced RVI and NDVI degradation were greatest over canopies with darker soils and were not detectable over canopies with light-colored soils. In contrast, soil and atmospheric effects on the perpendicular vegetation index were independent with atmosphere degradation being similar across all soil backgrounds. Soil influences on vegetation indices from partial canopies were found to be of similar magnitude to those attributed to the atmosphere for the range of soil and atmosphere conditions examined here.

A comparison of vegetation indices over a global set of TM images for EOS-MODIS - Huete, A. R.; Liu, H. Q.; Batchily, K.; van Leeuwen, W. (Remote Sensing of Environment, 1997)
A set of Landsat Thematic Mapper images representing a wide range of vegetation conditions from the NASA Landsat Pathfinder, global land cover test site (GLCTS) initiative were processed to simulate the Moderate Resolution Imaging Spectroradiometer (MODIS), global vegetation index imagery at 250 m pixel size resolution. The sites included boreal forest, temperate coniferous forest, temperate deciduous forest, tropical rainforest, grassland, savanna, and desert biomes. Differences and similarities in sensitivity to vegetation conditions were compared among various spectral vegetation indices (VIs). All VIs showed a qualitative relationship to variations in vegetation. However, there were significant differences among the VIs over desert, grassland, and forested biomes. The normalized difference vegetation index (NDVI) was sensitive to and responded primarily to the highly absorbing red reflectance band, while other indices such (is the soil and atmosphere resistant vegetation index (SARVI) were more responsive to variations in the near-infrared (NIR) band. As a result, we found the NDVI to mimic red reflectances and saturate over the forested sites while the SARVI, by contrast, did not saturate and followed variations in NIR refleetances. In the arid and semiarid biomes, the NDVI was much more sensitive to canopy background variations than the SARVI. Maximum differences among vegetation index behavior occurred over the evergreen needleleaf forest sites relative to the deciduous broadleaf forests and drier, grassland, and shrub sites. These differences appear to be useful in complementing the NDVI for improved monitoring of vegetation, with the NDVI sensitive to fraction of absorbed photosynthetic active radiation and the SARVI more sensitive to structural canopy parameters such as leaf area index and leaf morphology.

Overview of the radiometric and biophysical performance of the MODIS vegetation indices - Huete, A.; Didan, K.; Miura, T.; Rodriguez, E. P.; Gao, X.; Ferreira, L. G. (Remote Sensing of Environment, 2002)
We evaluated the initial 12 months of vegetation index product availability from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Earth Observing System-Terra platform. Two MODIS vegetation indices (VI), the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), are produced at 1-km and 500-m resolutions and 16-day compositing periods. This paper presents an initial analysis of the MODIS NDVI and EVI performance from both radiometric and biophysical perspectives. We utilize a combination of site-intensive and regionally extensive approaches to demonstrate the performance and validity of the two indices. Our results showed a good correspondence between airborne-measured, top-of-canopy reflectances and VI values with those from the MODIS sensor at four intensively measured test sites representing semi-arid grass/shrub, savanna, and tropical forest biomes. Simultaneously derived field biophysical measures also demonstrated the scientific utility of the MODIS VI. Multitemporal profiles of the MODIS VIs over numerous biome types in North and South America well represented their seasonal phenologies. Comparisons of the MODIS-NDVI with the NOAA-14, 1-km AVHRR-NDVI temporal profiles showed that the MODIS-based index performed with higher fidelity. The dynamic range of the MODIS VIs are presented and their sensitivities in discriminating vegetation differences are evaluated in sparse and dense vegetation areas. We found the NDVI to asymptotically saturate in high biomass regions such as in the Amazon while the EVI remained sensitive to canopy variations.

Development of Vegetation and Soil Indices for MODIS- EOS - Huete, A.R.; Justice, C.; Liu, H. (Remote Sensing of Environment, 1994)

Detection of changes in leaf water content using Near- and Middle-Infrared reflectances - Hunt Jr, E. Raymond; Rock, Barrett N. (Remote Sensing of Environment, 1989)
Detection of plant water stress by remote sensing has been proposed using indices of Near-Infrared (NIR, 0.7â1.3 Âµm) and Middle-Infrared (MIR, 1.3â2.5 Âµm) wavelengths. The first objective of this study was to test the ability of the Leaf Water Content Index (LWCI) to determine leaf Relative Water content (RWC) of different species with different leaf morphologies. The second objective was to determine how the Moisture Stress Index (MSI; MIR / NIR) varies with RWC and the Equivalent Water Thickness (EWT). Reflectance factors at 0.82 Âµm and 1.6 Âµm were measured on leaves of Quercus agrifolia (sclerophyllous leaves), Liquidambar styraciflua (hardwood deciduous tree leaves), Picea rubens and Picea pungens (conifer needles), and Glycine max (herbaceous dicot leaves) as they dried on a laboratory bench. RWC and EWT were measured concurrently with the reflectance measurements. The results showed that LWCI was equal to RWC for the species tested. However, the results of a sensitivity analysis indicated the reflectances at 1.6 Âµm for two different RWC must be known for accurate prediction of unknown RWC; thus the LWCI is impractical for field applications. MSI was linearly correlated to RWCwith each species having a different regression equation and to log10 EWT with data of all species falling on the same regression line. Because EWT is correlated with leaf area index, MSI should also be correlated with leaf area index. Assuming that the linear regression equation of MSI to EWT can be applied to canopies, then the minimum significant change of RWC that can be detected is 52%. For most plants, the natural variation in RWC from water stress is only about 20%, so that we conclude that indices derived from NIR and MIR reflectances cannot be used to remotely-sense water stress.

Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index - Hunt Jr., E. Raymond; Daughtry, C. S. T.; Eitel, Jan U. H.; Long, D. S. (Agronomy Journal, 2011)
Leaf chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to leaf nitrogen content. We propose the triangular greenness index (TGI), which calculates the area of a triangle with three vertices: ('r, Rr), ('g, Rg), and ('b, Rb), where ' is the wavelength (nm) and R is the reflectance for bands in red (r), green (g), and blue (b) wavelengths. TGI was correlated with chlorophyll content using a variety of leaf and plot reflectance data. Generally, indices using the chlorophyll red-edge (710-730 nm) had higher correlations with chlorophyll content compared to TGI. However, with broad bands, correlations between TGI and chlorophyll content were equal or higher than other indices for corn and wheat. Simulations using the Scattering by Arbitrarily Inclined Leaves canopy model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, whereas at high crop LAI, TGI was only affected by leaf chlorophyll content. TGI will enable the use of low-cost sensors, including digital cameras, for nitrogen management by remote sensing.

On the Suitability of MODIS Time Series Metrics to Map Vegetation Types in Dry Savanna Ecosystems: A Case Study in the Kalahari of NE Namibia - HÃ¼ttich, Christian; Gessner, Ursula; Herold, Martin; Strohbach, Ben; Schmidt, Michael; Keil, Manfred; Dech, Stefan (Remote Sensing, 2009)

Interpreting vegetation indices - Jackson, Ray D.; Huete, Alfredo R. (Preventive Veterinary Medicine, 1991)

Monitoring wheat fields and grasslands using spectral reflectance data - Jacobsen, Anne; Broge, Niels H.; Hansen, Birger U. (International Symposium on Spectral Sensing Research (ISSSR), 1995)

AFRI - aerosol free vegetation index - Karnieli, A.; Kaufman, Y. J.; Remer, L.; Wald, A. (Remote Sensing of Environment, 2001)
Aircraft measurements using a field spectrometer over variety of ground surfaces in Israel reveals that under clear sky conditions, the shortwave infrared (SWIR) spectral bands around 1.6 and 2.1 m are highly correlated with the visible - blue, green, and red - spectral bands. Empirical linear relationships, such as 0.469=0.25 2.1; 0.555=0.33 2.1; 0.645=0.5 2.1; and 0.645=0.66 1.6, were found to be statistically significant and consistent with previous findings. Based on the above relationships, a modified vegetation index (VI) is proposed and named Aerosol Free Vegetation Index (AFRI). Two versions of this VI are formulated as: AFRI1.6=( NIR-0.66 1.6)/( NIR+0.66 1.6) and AFRI2.1=( NIR-0.5 2.1)/( NIR+0.5 2.1). It is shown that under clear sky conditions, the AFRIs (and especially AFRI2.1) closely resemble the Normalized Difference Vegetation Index (NDVI) and their values are almost identical. The advantage of the derived AFRIs, based on the ability of the SWIR bands, is to penetrate the atmospheric column even when aerosols such as smoke or sulfates exist. Consequently, these indices have a major application in assessing vegetation in the presence of smoke, anthropogenic pollution, or volcanic plumes. This was demonstrated by applying the AFRI for a biomass burned forest in Brazil. Limited success of these indices is expected in case of dust due to presence of larger particles that are of similar size to the wavelength and therefore not transparent at 2.1 m. The AFRIs can be implemented to data from any sensor that has the SWIR bands. Currently, the most commonly used of such instruments are the Landsat - Thematic Mapper (TM) and Advanced Thematic Mapper Plus (ETM+), Moderate Resolution Imaging Spectrometer (MODIS), Advanced Spaceborne Thermal Emission And Reflection (ASTER), and Japanese Earth Resources Satellite - Optical System (JERS-OPS). Although the AFRI2.1 was demonstrated to perform better than the AFRI1.6, the latter still can be used for the same application in conjunction with instruments that have onboard only the 1.6- m band, such as Systeme Probatoire d'Observation del la Terre (SPOT4) - VEGETATION, Indian Remote Sensing (IRS-1C/D), and Resource-21.

Atmospherically resistant vegetation index (ARVI) for EOS-MODIS - Kaufman, Y. J.;Tanre, D. (Geoscience and Remote Sensing, IEEE Transactions on, 1992)
Keywords: ecology; remote sensing; 0.47 micron; 0.66 micron; 0.865 micron; EOS-HIRIS; EOS-MODIS; Earth Observing System; atmospherically resistant vegetation index; blue channel; dynamic range; near-IR channel; normalized difference vegetation index; radiance; radi

The use high spectral resolution bands for estimating absorbed photo synthetically active radiation (APAR) - Kim, Moon S.; Doughtry, C.S.T.; Chappelle, E. W.; Mcmurtrey, J. E.; Walthall, C. L. (ISPRSâ94 Val dâIsere, France, 17-21 January 1994, 1994)
Most remote sensing estimations of vegetation variables such as Leaf Area Index (LAI), Absorbed Photosynthetically Active Radiation (APAR), and phytomass are made using broad band sensors with a bandwidth of approximately 100 nm. However, high resolution spectrometers are available and have not been fully exploited for the purpose of improving estimates of vegetation variables. A study directed to investigate the use of high spectral resolution spectroscopy for remote sensing estimates of APAR in vegetation canopies in the presence of nonphotosynthetic background materials such as soil and leaf litter is presented. A high spectral resolution method defined as the Chlorophyll Absorption Ratio Index (CARI) was developed for minimizing the effects of nonphotosynthetic materials in the remote estimates of APAR. CARI utilizes three bands at 550, 670, and 700 nm with bandwidth of 10 nm. Simulated canopy reflectance of a range of LAI were generated with the SAIL model using measurements of 42 different soil types as canopy background. CARI obtained from the simulated canopy reflectance was compared with the broad band vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Simple Ratio (SR)). CARI reduced the effect of nonphotosynthetic background materials in the assessment of vegetation canopy APAR more effectively than broad band vegetation indices.

A comparison of methods to relate grass reflectance to soil metal contamination - Kooistra, L.; Leuven, R. S. E. W.; Wehrens, R.; Nienhuis, P. H.; Buydens, L. M. C. (International Journal of Remote Sensing, 2003)
Grass-dominated vegetation covers large areas of the Dutch river floodplains. Remotely sensed data on the conditions under which this vegetation grows may yield information about the degree of soil contamination. This paper explores the relationship between grassland canopy reflectance and zinc (Zn) contamination in the soil under semi-field conditions. A field radiometer was used to record reflectance spectra of perennial ryegrass (Lolium perenne) in an experimental field with Zn concentrations in the soil ranging from 32 to 1800mgkg?1. Several spectral vegetation indices (VIs) and a multivariate approach using partial least squares (PLS) regression were investigated to evaluate their potential use in estimating Zn contamination levels. Compared to the best PLS model (RMSEP = 181.4 mg kg?1), the narrow band vegetation index MSAVI2mm performed better (RMSEP = 162.9 mg kg?1). Both MSAVI2mm and PLS gave a high user accuracy for the strongly contaminated soil class (100% and 91%, respectively), while the total accuracy was satisfactory (60% and 55%, respectively). Results from this feasibility study indicate the potential of using remote sensing techniques for the classification of contaminated areas in river floodplains. But as the results from this study may be both resolution- and location-dependent, research on field and image scale is now required to test the established relations and to assess their susceptibility to seasonal influences, species heterogeneity, and increased levels of spectral noise.

Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements - le Maire, G.; Francois, C.; Dufrene, E. (Remote Sensing of Environment, 2004)
Fifty-three leaves were randomly sampled on different deciduous tree species, representing a wide range of chlorophyll contents, tree ages, and leaf structural features. Their reflectance was measured between 400 and 800 nm with a 1-nm,step, and their chlorophyll content determined by extraction. A larger simulated database (11,583 spectra) was built using the PROSPECT model, in order to test, calibrate, and obtain universal indices, i.e., indices applicable to a wide range of species and leaf structure. To our knowledge, almost all leaf chlorophyll indices published in the literature since 1973 have been tested on both databases. Fourteen canonical types of indices (published ones and new ones) were identified, and their wavelengths calibrated on the simulated database as well as on the experimental database to determine the best wavelengths and, hence, the best performances in chlorophyll estimation for each index types. These indices go from simple reflectance ratios to more sophisticated indices Using reflectance first derivatives (using the Savitzky and Golay method). We also tested other nondestructive methods to obtain total chlorophyll concentration: SPAD (Minolta Camera, Osaka, Japan) and neural networks. The validity of the actual PROSPECT model is challenged by our results: Important discordances are found when the indices are calculated with PROSPECT compared to experimental data, especially for some indices and wavelengths. The discordance is even greater when the indices are determined with PROSPECT and applied on the experimental database. A new calibration of PROSPECT is therefore necessary for any study aiming at using simulated spectra to determine or to calibrate indices. The "peak jump" and the multiple-peak feature observed on the first derivative of the reflectances (e.g., in the Red-Edge Inflection Point [REIP] index) has been investigated. It was shown that chlorophyll absorption alone can explain this feature. The peak jump disqualifies' the REIP to be a valuable chlorophyll index. A simple modified difference ratio gave the best results among all published indices (cross-validated RMSE = 2.1 mug/cm(2) on the experimental database). After calibration on the experimental database, modified Simple Ratio (mSR) and modified Normalized Difference (mND) indices gave the best 2 performances (RMSECV = 1.8 mug/cm(2) on the experimental database). The new Double Difference (DD) index, although not the best on the 2), 2 experimental database (RMSECV 2.9 mug/cm(2)), has the best results on the larger simulated database (RMSE = 3.7 mug/cm(2)) and is expected to give good results on larger experimental databases. The best reflectance-based indices give better performances than the current commercial nondestructive device SPAD (RMSECV = 4.5 mug/cm(2)). In This leaf-level study, the best indices are very near from each other, so that complex methods are useless: REIP-like, neural networks, and derivative-based indices are not necessary and give worst results than simpler properly chosen indices. These conclusions will certainly be different for. a canopy-level study, where the derivative-based indices may perform significantly better than the other ones. (C) 2003 Elsevier Inc. All rights reserved.
Keywords: universal broad leaf chlorophyll indices. PROSPECT. hyperspectral. reflectance measurements. neural-network classification. radiative-transfer models. remote-sensing. data. red-edge. vegetation indexes. spectral reflectance. optical-properties. bidirectio

Sensing technologies for precision specialty crop production - Lee, W. S.; Alchanatis, V.; Yang, C.; Hirafuji, M.; Moshou, D.; Li, C. (Computers and electronics in agriculture, 2010)
With the advances in electronic and information technologies, various sensing systems have been developed for specialty crop production around the world. Accurate information concerning the spatial variability within fields is very important for precision farming of specialty crops. However, this variability is affected by a variety of factors, including crop yield, soil properties and nutrients, crop nutrients, crop canopy volume and biomass, water content, and pest conditions (disease, weeds, and insects). These factors can be measured using diverse types of sensors and instruments such as field-based electronic sensors, spectroradiometers, machine vision, airborne multispectral and hyperspectral remote sensing, satellite imagery, thermal imaging, RFID, and machine olfaction system, among others. Sensing techniques for crop biomass detection, weed detection, soil properties and nutrients are most advanced and can provide the data required for site specific management. On the other hand, sensing techniques for diseases detection and characterization, as well as crop water status, are based on more complex interaction between plant and sensor, making them more difficult to implement in the field scale and more complex to interpret. This paper presents a review of these sensing technologies and discusses how they are used for precision agriculture and crop management, especially for specialty crops. Some of the challenges and considerations on the use of these sensors and technologies for specialty crop production are also discussed. (C) 2010 Elsevier B.V. All rights reserved.
ISI Document Delivery No.: 668LQ Times Cited: 11 Cited Reference Count: 367 Lee, W. S. Alchanatis, V. Yang, C. Hirafuji, M. Moshou, D. Li, C. Elsevier sci ltd Oxford
Keywords: Specialty crop; Precision agriculture; Sensing; Review; on-the-go; infrared reflectance spectroscopy; soil-moisture content; airborne hyperspectral imagery; grain-sorghum yield; leaf-area index; quickbird satellite imagery; difference vegetation index; tree canopy; characteristics; compaction profile sensor

Critical assessment of vegetation indices from AVHRR in a semi-arid environment - Leprieur, C.; Kerr, Y. H.; Pichon, J. M. (International Journal of Remote Sensing, 1996)
Abstract The most frequently used vegetation index (VI), the Normalized Difference Vegetation Index (NDVI) and its variants introduced recently to correct for atmospheric and soil optical response such as Global Environment Monitoring Index (GEMI) and Modified Soil-Adjusted Vegetation Index (MSAVI) are evaluated over a Sahelian region. The usefulness and limitations of the various vegetation indices are discussed, with special attention to cloud contamination and green vegetation detection from space. The HAPEX Sahel database is used as a test case to compare these indices in arid and semi-arid environments. Selected sites are characterized by sparse vegetation cover and day-to-day variability in atmospheric composition. Simulated indices values behaviour at the surface level shows that these VIs were all sensitive to the presence of green vegetation but were affected differently by changes in soil colour and brightness. We showed that GEMI is less sensitive to atmospheric variations than both NDVI and MSAVI since it exhibits a high atmospheric transmissivity over its entire range for various atmospheric aerosol loadings and water vapour contents. These results were first tested on a vegetation gradient, and secondly evaluated on a transect which encompasses various soils formations. On the vegetation gradient, it was found that GEMI computed from measurements at the top of the atmosphere is invariable from one day to the next. On the bare soils transect, MSAVI calculated at the surface level, has shown a great insensitivity to soil optical responses modifications, while GEMI exhibits from space noticeable variability in this bright soil context. Finally, it was illustrated that GEMI exhibits interesting properties for cloud detection because of the strong decrease of its value on cloudy pixels.

Estimation of canopy-average surface-specific leaf area using Landsat TM data - Lymburner, L.; Beggs, PJ.; Jacobson, CR. (Photogrammetric Engineering and Remote Sensing, 2000)
Specific leaf area (SLA) is an important ecological variable because of its links with plant ecophysiology and leaf biochemistry. Variations in SLA are associated with variations in leaf optical properties, and these changes in leaf optical properties have been found to result in changes in canopy reflectance. This paper utilizes these changes to explore the potential of estimating SLA using Landsat TM data. Fourteen sites with varying vegetation were sampled on the Lambert Peninsula in Ku-ring-gai Chase National Park to the north of Sydney, Australia. A sampling strategy that facilitated the calculation of canopy-average surface SLA (SLA(CS)) was developed. The relationship between SLA(CS), reflectance in Landsat TM bands, and a number of vegetation indices, were explored using univariate regression. The observed relationships between SLA(CS) and canopy reflectance are also discussed in terms of trends observed in a pre-existing leaf optical properties dataset (LOPEX 93). Field data indicate that there is a strong correlation between SLA(CS) and red, near-infrared, and the second midinfrared bands of Landsat TM data. A strong correlation between SLA(CS) and the following vegetation indices: Soil and Atmosphere Resistant Vegetation Index (SARVI2), Normalized Difference Vegetation Index (NDVI), and Ratio Vegetation Index (RVI), suggests that these vegetation indices could be used to estimate SLA(CS) using Landsat TM data.

New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra - Maccioni, Andrea; Agati, Giovanni; Mazzinghi, Piero (Journal of Photochemistry and Photobiology B: Biology, 2001)
Directional reflectance (R) spectra from 380 to 780 nm for nadir illuminated leaves of four different plants (croton, Codiaeum variegatum; spotted eleagnus, Eleagnus pungens Maculata; Japanese pittosporum, Pittosporum tobira and Benjamin fig, Ficus benjamina Starlight) were acquired at a viewing angle of 30Â° from the nadir direction. Chlorophyll-a and -b content of leaves covered a range of 1â60 and 0.5â21 μg/cm2, respectively. In contrast with previous results from hemispherical reflectance measurements, directional reflectance data does not correlate well with chlorophyll concentration. This is mainly due to the external reflectance (RE) at the leaf epidermis, caused by the mismatch of the refractive index at the airâepidermis and epidermisâinner layer boundary. The external reflectance can be identified with the blue flat reflectance between 380 and 480 nm. The inner reflectance (RI), obtained by subtracting the external reflectance from the measured spectra, was found to be linearly related to the logarithm of the chlorophyll content. Good fitting of the log (Chl) versus RI(λ) curves were obtained for RI in the green band (around 550 nm) and close to the inflection point in the red edge (around 700 nm). The coefficient of determination, r2, of curve fitting improved (up to 0.97) when the normalised inner reflectance NRI(λ)=RI(λ)/RI(λ0), with λ0≥750 nm, was used instead of the absolute reflectance. The best indices for Chl, Chl-a and Chl-b determination were RI542/RI750, RI706/RI750 and RI556/RI750, respectively. However, since the content of Chl-a relative to Chl-b was almost constant for the plants investigated, the two last indices must be further validated on leaves with a high variability in the Chl-a:Chl-b ratio. The error in the determination of chlorophyll content was found to be of the order of 10%. This value was lower than those obtained by applying the vegetation indices previously suggested. Therefore, the normalised inner reflectance in the green and in the red edge represents a more suitable index for the chlorophyll determination than those up to now used.
Keywords: Chlorophyll, Leaf directional reflectance, Leaf reflectance spectra, Remote sensing of chlorophyll

An investigation into robust spectral indices for leaf chlorophyll estimation - Main, Russell; Cho, Moses Azong; Mathieu, Renaud; OâKennedy, Martha M.; Ramoelo, Abel; Koch, Susan (ISPRS Journal of Photogrammetry and Remote Sensing, 2011)
Quantifying photosynthetic activity at the regional scale can provide important information to resource managers, planners and global ecosystem modelling efforts. With increasing availability of both hyperspectral and narrow band multispectral remote sensing data, new users are faced with a plethora of options when choosing an optical index to relate to their chosen or canopy parameter. The literature base regarding optical indices (particularly chlorophyll indices) is wide ranging and extensive, however it is without much consensus regarding robust indices. The wider spectral community could benefit from studies that apply a variety of published indices to differing sets of species data. The consistency and robustness of 73 published chlorophyll spectral indices have been assessed, using leaf level hyperspectral data collected from three crop species and a variety of savanna tree species. Linear regression between total leaf chlorophyll content and bootstrapping were used to determine the leafpredictive capabilities of the various indices. The indices were then ranked based on the prediction error (the average root mean square error (RMSE)) derived from the bootstrapping process involving 1000 iterative resampling with replacement. The results show two red-edge derivative based indices (red-edge position via linear extrapolation index and the modified red-edge inflection point index) as the most consistent and robust, and that the majority of the top performing indices (in spite of species variability) were simple ratio or normalised difference indices that are based on off-chlorophyll absorption centre wavebands (690â730 nm).
Keywords: Leaf level reflectance; Leaf chlorophyll; Red-edge; Vegetation indices; Photosynthetic activity

A ratio vegetation index adjusted for soil brightness - Major, D. J.; Baret, F.; Guyot, G. (International Journal of Remote Sensing, 1990)
Abstract Improved parameters for a soil-adjusted vegetation index (SAVI) are derived using SAIL model output for simulated wheat canopy reflectance. The SAVI is much less sensitive than the ratio vegetation index to changes in background caused by soil colour or surface soil moisture content. The parameters are developed to minimize variability due to soil brightness over the practical range of solar elevations, lear angle distributions (LAD) and leaf area indices (LAI). The parameters are added to the near-infrared (NIR) and red reflectances before calculating the NIR/red ratio. Three new versions of the SAVI are developed based on the theoretical consideration of the effects of wet and dry soils. All three are superior to the NIR/red ratio, the perpendicular vegetation index and a soil-adjusted vegetation index. Our simplest version requires the addition of a parameter to the red reflectance. The second and third versions require an iterative procedure to find the best parameters that are then added to the red or the NIR and red reflectances. In general, SAVI adjustment parameters required to remove soil-brightness effects decrease as factors that obscure the soil-surface increase. Low solar elevation and high LAI result in increasingly negative values for the parameters. The NIR/red ratio is the vegetation index least influenced by soil brightness at LAI greater than three. Our iterated versions have lowest overall errors but are occasionally subject to higher errors at the combination of low zenith angles and erectophile canopies. The SAVI based on the bare soil reflectance performs best on field data.

Candidate high spectral resolution infrared indices for crop cover - Malthus, Tim J.; Andrieu, Bruno; Danson, F. Mark; Jaggard, Keith W.; Steven, Michael D. (Remote Sensing of Environment, 1993)

Monitoringcommunityhysteresis using spectralshiftanalysis and the red-edgevegetationstressindex - Merton, R. N. (Seventh Annual JPL Airborne Earth Science Workshop, 1998)

Vegetation indices derived from high-resolution airborne videography for precision crop management - Metternicht, G. (International Journal of Remote Sensing, 2003)

Inter-Comparison of ASTER and MODIS Surface Reflectance and Vegetation Index Products for Synergistic Applications to Natural Resource Monitoring - Miura, T.; Yoshioka, H.; Fujiwara, K.; Yamamoto, H., (Sensors, 2008)

Opportunities and limitations for image-based remote sensing in precision crop management - Moran, M. S.; Inoue, Y.; Barnes, E. M. (Remote Sensing of Environment, 1997)
This review addresses the potential of image-based remote sensing to provide spatially and temporally distributed information for precision crop management (PCM). PCM is an agricultural management system designed to target crop and soil inputs according to within, field requirements to optimize profitability and protect the environment. Progress in. PCM has been hampered by a lack of timely, distributed information on crop and soil conditions. Based on a review of the information requirements of PCM, eight areas were identified in which image-based remote sensing technology could provide information that is currently lacking or inadequate. Recommendations were made for applications with potential for near-term implementation with available remote sensing technology and instrumentation. We found that both aircraft- and satellite-based re-trote sensing could provide valuable information for PCM applications. Images from aircraft-based sensors have a unique role for monitoring seasonally variable crop/soil conditions and for time specific and time-critical crop management; current satellitebased sensors have limited, but important, applications; and upcoming commercial Earth observation satellites may provide the resolution, timeliness, and high quality required for many PCM operations. The current limitations for image-based remote sensing applications are mainly due to sensor attributes, such as restricted spectral range, coarse spatial resolution, slow turnaround time, and inadequate repeat coverage. According to experts in PCM, the potential market for remote sensing products in PCM is good. Future work should be focused on assimilating remotely sensed infonna- tion into existing decision support systems (DSS), and conducting economic and technical analysis of remote sensing applications with season-long pilot projects.

Combining agricultural crop models and satellite observations: from field to regional scales - Moulin, S.; Bondeau, A.; Delecolle, R. (International Journal of Remote Sensing, 1998)
This review article gives an overview of how satellite observations are used to feed or tune crop models and improve their capability to predict crop yields in a region. Relations between crop characteristics which correspond to models state variables and satellite observations are briefly analysed, together with the various types of crop models commonly used. Various strategies for introducing short wavelength radiometric information into specific crop models are described, from direct update of model state variables to optimization of model parameter values, and some of them are exemplified. Methods to unmix crop-specific information from mixed pixels in coarse resolution-high frequency imagery are analysed. The conditions of use of the various methods and types of information are discussed.
ISI Document Delivery No.: ZK386 Times Cited: 118 Cited Reference Count: 72 Moulin, S Bondeau, A Delecolle, R Taylor & francis ltd London
Keywords: photosynthetically active radiation; leaf-area index; winter-wheat; use; efficiency; spectral reflectance; vegetation canopies; temporal; variations; simulation-models; yield estimation; sail model

Plant Litter and Soil Reflectance - Nagler, P. L; Daughtry, C. S. T.; Goward, S. N. (Remote Sensing of Environment, 2000)
The presence of plant litter on the soil surface influences the flow of nutrients, carbon, water, and energy in terrestrial ecosystems. Quantifying plant litter cover is important for interpreting vegetated landscapes and for evaluating the effectiveness of conservation tillage practices. Current methods of measuring litter cover are subjective, requiring considerable visual judgment. Reliable and objective methods are needed. The spectral reflectance (0.4â2.5 μm) of wet and dry soils (six types) and plant litters (2 crops, 14 forest, and 2 grasses) of different ages were measured. Discrimination of plant litters from the soils was ambiguous in the visible and near-infrared (0.4â1.1μm) wavelength region. An absorption feature associated with cellulose and lignin was observed at 2.1 μm in the spectra of dry plant litter, which was not present in the spectra of soils. A new spectral variable, cellulose absorption index (CAI), was defined using the relative depth of the reflectance spectra at 2.1 μm. CAI of dry litter was significantly greater than CAI of soils. CAI generally decreased with age of the litter. Water absorption dominated the spectral properties of both soils and plant litter and significantly reduced the CAI of the plant litters. Nevertheless, the CAI of wet litter was significantly greater then CAI of wet soil. This study provides a new methodology to discriminate plant litter from soils by differences in spectral reflectance produced by their physical and chemical attributes. This remote sensing method should improve quantification of plant litter cover and thus improve estimates of phytomass production, surface energy balance, and the effectiveness of soil conservation practices. Plant litter reflectance is a verifiable component in vegetative landscapes and should be labeled and modeled separately from soils in landscape studies.

Evapotranspiration on western U.S. rivers estimated using the Enhanced Vegetation Index from MODIS and data from eddy covariance and Bowen ratio flux towers - Nagler, Pamela L.; Scott, Russell L.; Westenburg, Craig; Cleverly, James R.; Glenn, Edward P.; Huete, Alfredo R. (Remote Sensing of Environment, 2005)
We combined remote sensing and in-situ measurements to estimate evapotranspiration (ET) from riparian vegetation over large reaches of western U.S. rivers and ET by individual plant types. ET measured from nine flux towers (eddy covariance and Bowen ratio) established in plant communities dominated by five major plant types on the Middle Rio Grande, Upper San Pedro River, and Lower Colorado River was strongly correlated with Enhanced Vegetation Index (EVI) values from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the NASA Terra satellite. The inclusion of maximum daily air temperatures (Ta) measured at the tower sites further improved this relationship. Sixteen-day composite values of EVI and Ta were combined to predict ET across species and tower sites (r2 = 0.74); the regression equation was used to scale ET for 2000â2004 over large river reaches with Ta from meteorological stations. Measured and estimated ET values for these river segments were moderate when compared to historical, and often indirect, estimates and ranged from 851â874 mm yr- 1. ET of individual plant communities ranged more widely. Cottonwood (Populus spp.) and willow (Salix spp.) stands generally had the highest annual ET rates (1100â1300 mm yr- 1), while mesquite (Prosopis velutina) (400â1100 mm yr- 1) and saltcedar (Tamarix ramosissima) (300â1300 mm yr- 1) were intermediate, and giant sacaton (Sporobolus wrightii) (500â800 mm yr- 1) and arrowweed (Pluchea sericea) (300â700 mm yr- 1) were the lowest. ET rates estimated from the flux towers and by remote sensing in this study were much lower than values estimated for riparian water budgets using crop coefficient methods for the Middle Rio Grande and Lower Colorado River.
Keywords: Evapotranspiration; Riparian; Water balance; MODIS; Saltcedar; Remote sensing

Remote sensing of photosynthetic-light-use efficiency of boreal forest - Nichol, Caroline J.; Huemmrich, Karl F.; Black, T. Andrew; Jarvis, Paul G.; Walthall, Charles L.; Grace, John; Hall, Forrest G. (Agricultural and Forest Meteorology, 2000)
Using a helicopter-mounted portable spectroradiometer and continuous eddy covariance data we were able to evaluate the photochemical reflectance index (PRI) as an indicator of canopy photosynthetic light-use efficiency (LUE) in four boreal forest species during the Boreal Ecosystem Atmosphere experiment (BOREAS). PRI was calculated from narrow waveband reflectance data and correlated with LUE calculated from eddy covariance data. Significant linear correlations were found between PRI and LUE when the four species were grouped together and when divided into functional type: coniferous and deciduous. Data from the helicopter-mounted spectroradiometer were then averaged to represent data generated by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). We calculated PRI from these data and relationships with canopy LUE were investigated. The relationship between PRI and LUE was weakened for deciduous species but strengthened for the coniferous species. The robust nature of this relationship suggests that relative photosynthetic rates may be derived from remotely-sensed reflectance measurements.
Keywords: Xanthophyll cycle; AVIRIS; Photochemical reflectance index; Eddy covariance

Photochemical reflectance index and leaf photosynthetic radiation-use-efficiency assessment in Mediterranean trees - Penuelas, J.; Llusia, J.; Pinol, J.; Filella, I. (International Journal of Remote Sensing, 1997)
Abstract This Letter presents new data validating the use of the photochemical reflectance index PRI (R570-R531)/(R570 + R531) to assess photosynthetic- radiation-use-efficiency under mild water stress. Gas exchange, fluorescence and the PRI of leaves from the Mediterranean trees Quercus ilex and Phillyrea latifolia growing in the field were followed from spring to summer 1996 in central Catalonia (NE Spain). The same variables were measured in seedlings of these two species and Pistacia lentiscus submitted to progressive drying after witholding irrigation. Except for severely drought damaged plants, significant relations of the reflectance index PRI with fluorescence yield of the photosystem II (Delta F/F' m), non photochemical fluorescence quenching ( qN ) and photosynthetic radiation use efficiency (PRUE) were found, thus indicating a functional relation among these parameters. A simple portable radiometer measuring ground level reflectance at narrow bands centred at 531 and 570nm could instantaneously calculate the PRI index and give the PRUE estimation.

Assessment of photosynthetic radiation-use efficiency with spectral reflectance - Penuelas, Josep; Filella, Iolanda; Gamon, John A. (New Phytologist, 1995)
Reflectance changes at 531 nm, associated with the zeaxanthin-antheraxanthin-violaxanthin interconversion and the related thylakoid energization, are widespread among plant species. We evaluated an index based on 531 nm reflectance (âPRIâ, Photochemical Reflectance Index calculated as (R531- R570)/(R531+ R570)) as an indicator of efficiency of photosynthetic radiation use in seven species representing both C3 and CAM photosynthetic pathways. Leaves exposed to a dark-lighted ark transition in a steady-state laboratory gas exchange system exhibited nearly parallel changes in PRI and PS II quantum yield (?F/Fm'). Similar PRI and ?F/Fm' responses were seen in leaves exposed to diurnally changing sunlight levels outdoors. PRI was linearly related to ?F/Fm', and both ?F/Fm' and PRI were exponentially related to instantaneous efficiency of photosynthetic radiation-use in different species over a range of different field conditions. These results extend previous studies by indicating a functional relationship between PRI. ?F/Fm', and photosynthetic radiation-use efficiency. The narrow-band PRI index offers a simple, portable means of assessing PS II radiation-use efficiency, analogous to ?F/Fm', and with the potential for remote applications at scales larger than the leaf.
Keywords: Photosynthetic radiation-use efficiency (PRUE); reflectance and fluorescence; zeaxanthin; photochemical reflectance index (PRI); quantum yield (?F/Fm')

Visible and near-infrared reflectance techniques for diagnosing plant physiological status - PeÃ±uelas, J.; Filella, I. (Trends in Plant Science, 1998)

Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves - PeÃ±uelas, J.; Gamon, J. A.; Fredeen, A. L.; Merino, J.; Field, C. B. (Remote Sensing of Environment, 1994)
We followed diurnal and seasonal changes in physiology and spectral reflectance of leaves throughout the canopies of sunflower plants grown in control, nitrogen (N)-limited, and water-stressed plots. Leaves from control sunflower plants had significantly higher levels of nitrogen, chlorophyll (chl), ribulose bis phosphate carboxylase / oxygenase (RuBPCase) activity and photosynthetic rates and lower starch content and leaf thickness than N-limited plants. Water-stressed plants had the highest N and chl contents (on an area basis). They also had the lowest water potential and photosynthetic rates, in spite of maintaining high RuBPCase activities. Leaves from stressed plants (especially N-limited) had significantly higher reflectances in the visible wavelengths and lower in the near IR than leaves from control plants. The only clear trend across canopy levels was the higher reflectance at all wavelengths but especially in the visible of the lower (oldest) leaves. NDVI-like parameters were useful in distinguishing stress and control leaves over the growing season. However, several narrow-band indices provided better physiological information than NDVI. The physiological reflectance index (PRI) (R550 - R530 / R550 + R530) followed diurnal changes in xanthophyll pigments and photosynthetic rates of control and N-limited leaves. The maximum of the first derivative of reflectance in the green (dG) was correlated with diurnal photosynthetic rate, and with seasonal chl and N changes. The normalized pigment chlorophyll ratio Index (NPCI) (R680 - R430 / R680 + R430) varied with total pigments / chl. The water band index (WBI) (R970 / R902) followed water status. The normalized ratio between the maxima of the first derivatives of reflectances at the red edge and green regions (EGFN) was correlated with chl and N content. Principal components analysis yielded several indicators of physiological status. The first principal component was higher in control leaves, the second was higher in N-limited leaves, and the third was higher in water-limited leaves. Discriminant analysis based on the combination of several narrow-band spectral indices clearly separated leaves into the three treatment groups. These results illustrate the promise of narrow-band spectroradiometry for assessing the physiological state of vegetation.

Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance - PeÃ±uelas, Josep; Gamon, John A.; Griffin, Kevin L.; Field, Christopher B. (Remote Sensing of Environment, 1993)
We studied the reflectance spectra of the aquatic vegetation of Searsville Lake in coastal central California using a high spectral resolution hand-held spectroradiometer. The three aquatic typesâsubmerged, floating, and emergentâexhibited clear differences in their spectral reflectance and can be distinguished on the basis of discriminant analysis using reflectance parameters. This technique can be used in large-area mapping of aquatic plants. The normalized difference vegetation index (NDVI) and the simple ratio (SR) were well correlated with chlorophyll content, photosynthetic efficiency, and biomass in the emergent species. New, narrow-bandwidth indices and reflectance indices calculated from first and second derivative spectra were strongly correlated with the ratio of secondary and protective pigments to chlorophyll a and with epoxidation state of the xanthophyll cycle pigments, and therefore, with photosynthetic efficiency. These new indices may be useful in the remote sensing of plant physiological status.

Remote sensing for crop management - Pinter, P. J.; Hatfield, J. L.; Schepers, J. S.; Barnes, E. M.; Moran, M. S.; Daughtry, C. S. T.; Upchurch, D. R. (Photogrammetric Engineering and Remote Sensing, 2003)
Scientists with the Agricultural Research Service (ARS) and various government agencies and private institutions have provided a great deal of fundamental information relating spectral reflectance and thermal emittance properties of soils and crops to their agronomic and biophysical characteristics. This knowledge has facilitated the development and use of various remote sensing methods for non-destructive monitoring of plant growth and development and for the detection of many environmental stresses which limit plant productivity. Coupled with rapid advances in computing and position-locating technologies, remote sensing from ground-, air-, and space-based platforms is now capable of providing detailed spatial and temporal information on plant response to their local environment that is needed for site specific agricultural management approaches. This manuscript, which emphasizes contributions by ARS researchers, reviews the biophysical basis of remote sensing; examines approaches that have been developed, refined, and tested for management of water, nutrients, and pests in agricultural crops; and assesses the role of remote sensing in yield prediction. It concludes with a discussion of challenges facing remote sensing in the future.
ISI Document Delivery No.: 817RA Times Cited: 88 Cited Reference Count: 256 Pinter, PJ Hatfield, JL Schepers, JS Barnes, EM Moran, MS Daughtry, CST Upchurch, DR Amer soc photogrammetry Bethesda
Keywords: water-stress index; spectral-biophysical data; soil heat-flux; infrared; aerial photography; laser-induced fluorescence; constant leaf; temperature; adjusted vegetation index; surface-energy balance; clover-seed production; gossypium-hirsutum l

GEMI: a non-linear index to monitor global vegetation from satellites - Pinty, B.; Verstraete, M. M. (Plant Ecology, 1992)
Knowledge about the state, spatial distribution and temporal evolution of the vegetation cover is of great scientific and economic value. Satellite platforms provide a most convenient tool to observe the biosphere globally and repetitively, but the quantitative interpretation of the observations may be difficult. Reflectance measurements in the visible and near-infrared regions have been analyzed with simple but powerful indices designed to enhance the contrast between the vegetation and other surface types, however, these indices are rather sensitive to atmospheric effects. The âcorrectionâ of satellite data for atmospheric effects is possible but requires large data sets on the composition of the atmosphere. Instead, we propose a new vegetation index which has been designed specifically to reduce the relative effects of these undesirable atmospheric perturbations, while maintaining the information about the vegetation cover.
Keywords: Biomedizin & Life Sciences

Comparative Analysis of EO-1 ALI and Hyperion, and Landsat ETM+ Data for Mapping Forest Crown Closure and Leaf Area Index - Pu, Ruiliang; Gong, Peng; Yu, Qian (Sensors, 2008)

A modified soil adjusted vegetation index - Qi, J.; Chehbouni, A.; Huete, A. R.; Kerr, Y. H.; Sorooshian, S. (Remote Sensing of Environment, 1994)
There is currently a great deal of interest in the quantitative characterization of temporal and spatial vegetation patterns with remotely sensed data for the study of earth system science and global change. Spectral models and indices are being developed to improve vegetation sensitivity by accounting for atmosphere and soil effects. The soil-adjusted vegetation index (SAVI) was developed to minimize soil influences on canopy spectra by incorporating a soil adjustment factor L into the denominator of the normalized difference vegetation index (NDVI) equation. For optimal adjustment of the soil effect, however, the L factor should vary inversely with the amount of vegetation present. A modified SAVI (MSAVI) that replaces the constant L in the SAVI equation with a variable L function is presented in this article. The L function may be derived by induction or by using the product of the NDVI and weighted difference vegetation index (WDVI). Results based on ground and aircraft-measured cotton canopies are presented. The MSAVI is shown to increase the dynamic range of the vegetation signal while further minimizing the soil background influences, resulting in greater vegetation sensitivity as defined by a âvegetation signalâ to âsoil noiseâ ratio.

Mapping and monitoring net primary productivity with AVHRR NDVI time-series: statistical equivalence of cumulative vegetation indices - Ricotta, Carlo; Avena, Giancarlo; De Palma, Alessandra (ISPRS Journal of Photogrammetry and Remote Sensing, 1999)
In the last two decades, numerous investigators have proposed cumulative vegetation indices (i.e., functions which encode the cumulative effect of NDVI maximum value composite time-series into a single variable) for net primary productivity (NPP) mapping and monitoring on a regional to continental basis. In this paper, we investigate the relationships among three of the most commonly used cumulative vegetation indices, expanding on the definition of equivalence of remotely sensed vegetation indices for decision making. We consider two cumulative vegetation indices as equivalent, if the value of one index is statistically predictable from the value of the other index. Using an annual time-series of broad-scale AVHRR NDVI monthly maximum value composites of the island of Corsica (France), we show that the pairwise linear association among the analysed cumulative vegetation indices shows coefficients of determination (R2) higher than 0.99. That is, knowing the value of one index is statistically equivalent to knowing the value of the other indices for application purposes.
Keywords: Corsica; cumulative vegetation indices; Fourier transform; NDVI; net primary productivity

Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation - Rouse, J.W., Jr.; Haas,R.H.; Schell, J.A.; Deering, D.W. (Prog. Rep. RSC 1978-1, 1973)

Remote sensing of forest biophysical variables using HyMap imaging spectrometer data - Schlerf, M.; Atzberger, C.; Hill, J. (Remote Sensing of Environment, 2005)

Sensitivity of vegetation indices to substrate brightness in hyper-arid environment: The Makhtesh Ramon Crater (Israel) case study - Schmidt, H.; Karnieli, A. (International Journal of Remote Sensing, 2001)
The influence of soil background on most vegetation indices (VIs) derived from remotely sensed imagery is a well known phenomenon, and has generated interest in the development of indices that would be less sensitive to this influence. Several such indices have been developed thus far. This paper focuses on testing and comparing the sensitivity of seven intensively used, Landsat Thematic Mapper (TM) derived, VIs (NDVI, SAVI, MSAVI, PVI, WDVI, SAVI 2 and TSAVI) to bare surface variation with almost no vegetation signal. The study was conducted on a terrain composed of a high variety of bare surface materials of which basalt and gypsum are two extremely dark and bright substrates respectively. It was found that SAVI and MSAVI respond to bare surface material very similarly. Such close similarity was also found between PVI and WDVI, and between SAVI 2 and TSAVI. NDVI tends to be overestimated on dark surfaces, while SAVI, PVI and TSAVI show more sensitivity to bright surfaces. Comparison between DeltaVI (the difference between pairs of VIs) and the brightness of the different surface materials showed a high correlation in each case, which underlines the fact that the response of different VIs to bare surface variation is mainly related to the surface brightness.

Remote Sensing of Biomass and Yield of Winter Wheat under Different Nitrogen Supplies - Serrano, Lydia; Filella, Iolanda; PeÃ±uelas, Josep (Crop Science, 2000)
Vegetation indices derived from reflectance data are related to canopy variables such as aboveground biomass, leaf area index (LAI), and the fraction of intercepted photosynthetically active radiation (fIPAR). However, under N stress the relationships between vegetation indices (VI) and these canopy variables might be confounded due to plant chlorosis. We studied the relationships between reflectance-based VI and canopy variables (aboveground biomass, LAI canopy chlorophyll A content [LAI Ã Chl A], and fIPAR) for a wheat (Triticum aestivum L.) crop growing under different N supplies. Nitrogen fertilization promoted significant increases in radiation interception (plant growth) and, to a lesser extent, in radiation use efficiency (RUE). The VI vs. LAI relationships varied significantly among treatments, rendering the VI-based equations unreliable to estimate LAI under contrasting N conditions. However, a single relationship emerged when LAI Ã Chl A was considered. Moreover, VI were robust indicators of fIPAR by green canopy components independently of N treatment and phenology. Aboveground biomass was poorly correlated with grain yield, whereas cumulative VI simple ratio (SR) was a good predictor of grain yield, probably because cumulative SR closely tracked the duration and intensity of the canopy photosynthetic capacity.

Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals - Serrano, Lydia; PeÃ±uelas, Josep; Ustin, Susan L. (Remote Sensing of Environment, 2002)
Remote sensing estimates of vegetation nitrogen (N) and lignin concentration are central to assess ecosystem processes such as growth and decomposition. Although remote sensing techniques have been proven useful to assess N and lignin contents in continuous green canopies, more studies are needed to address their capabilities, particularly in low and sparsely vegetated ecosystems. We investigated the possibility of estimating canopy N and lignin concentrations in chaparral vegetation using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) reflectance acquired over an area around Point Dume in the Santa Monica Mountains (Los Angeles, CA, USA). Two approaches were tested: multiple stepwise regression based on first difference reflectance (FDR) and reflectance (R) indices. Multiple stepwise regressions (of three or fewer wavelengths) accounted for a large variance in canopy biochemical concentration (r2∼0.9, P<0.01). Log transformed R indices [log (1/R)] formulated on the basis of previously known N and lignin absorption wavelengths also showed significant correlations (P<0.01) with canopy biochemical concentration (r2 ranging from 0.39 to 0.48). In addition, the contribution of structural and biochemical signals and background effects on the performance of these indices was evaluated. These indices accounted for a increased variance when adding information on canopy structural attributes (e.g., relative contribution of each species and biomass amount) to foliar biochemical concentration. The relative contributions of foliar biochemical concentration and canopy structure (biomass amount) on the spectral signal were further evaluated by analyzing the residuals from linear regressions: foliar N concentration accounted for 42% of the variance for a normalized difference index based on the 1510-nm N absorption feature, while the foliar lignin concentration accounted for 44% of the variance for a normalized difference index based on the 1754 nm lignin absorption feature. These percentages increased to 58% when stands with senescing vegetation were disregarded. We propose the two indices, Normalized Difference Nitrogen Index (NDNI=[log (1/R1510)−log (1/R1680)]/[log (1/R1510)+log (1/R1680)]) and Normalized Difference Lignin Index (NDLI=[log (1/R1754)−log (1/R1680)]/[log (1/R1754)+log (1/R1680)]) as indices to assess N and lignin in native shrub vegetation.

Forecasting wheat yield in a Mediterranean-type environment from the NOAA satellite - Smith, RCG; Adams, J; Stephens, DJ; Hick, PT (Australian Journal of Agricultural Research, 1995)
This paper reports the relationship between the spatial variation in mean wheat yield/ha of 50 Local Government Areas in Western Australia and satellite measures of the Normalized Difference Vegetation Index (NDVI). Yield/ha was based on estimates of the area harvested and actual grain received by the Cooperative Bulk Handling Ltd. The study area covered 16.3 million ha, in which 2.9 million ha of wheat were sown and 4.66 million tonnes of grain harvested. This was 78% of the total Western Australian wheat crop. Spatial variations in NDVI in early July, at around stem elongation, accounted for 46% of the spatial variation in final yield. This increased to 56% of yield variance around the onset of anthesis at the end of August. It remained high until early November (48%) when crops were senescing or senescent. A combination of NDVI from late August and early November accounted for 70% of the yield variance. In comparison, total rainfall during the 1992 growing season from April to October, the main determinant of yield variations, accounted for 28% of the yield variation. The significant correlation of NDVI with final yield by the middle of the growing season 3 to 5 months before harvest indicates the feasibility of making useful yield forecasts from this time onwards. In addition, the NDVI could provide useful spatial information on the significance of the yield/canopy development/water use relationship which underlies this correlation.

Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization - Thenkabail, P. S.; Smith, R. B.; De Pauw, E. (Photogrammetric Engineering And Remote Sensing, 2002)

Monitoring corn and soybean crop development with hand-held radiometer spectral data - Tucker, C. J.; Elgin Jr, J. H.; McMurtrey Iii, J. E.; Fan, C. J. (Remote Sensing of Environment, 1979)
Red and photographic infrared data were collected with a hand-held radiometer under a variety of conditions at 4- to 12-day intervals throughout the growing season and were used to monitor corn and soybean growth and development. The normalized difference transformation was used to effectively compensate for the variation in irradiational conditions. With these data, plotted against time, green-leaf biomass dynamics were compared between the crops. By this approach, based entirely upon spectral inputs, the crop canopies were nondestructively monitored. Five spectral stages were defined and were related to crop development for corn and soybeans.

Mapping nonnative plants using hyperspectral imagery - Underwood, Emma; Ustin, Susan; DiPietro, Deanne (Remote Sensing of Environment, 2003)
Nonnative plant species are causing enormous ecological and environmental impacts from local to global scale. Remote sensing images have had mixed success in providing spatial information on land cover characteristics to land managers that increase effective management of invasions into native habitats. However, there has been limited evaluation of the use of hyperspectral data and processing techniques for mapping specific invasive species based on their spectral characteristics. This research evaluated three different methods of processing hyperspectral imagery: minimum noise fraction (MNF), continuum removal, and band ratio indices for mapping iceplant (Carpobrotus edulis) and jubata grass (Cortaderia jubata) in California's coastal habitat. Validation with field sampling data showed high mapping accuracies for all methods for identifying presence or absence of iceplant (97%), with the MNF procedure producing the highest accuracy (55%) when the classes were divided into four different densities of iceplant.
Keywords: AVIRIS; Hyperspectral; Invasive plants; Nonnative plants; Iceplant; Carpobrotus edulis; Jubata grass; Cortaderia jubata; Mapping

Red Edge Spectral Measurements from Sugar Maple Leaves - Vogelmann, J.E.; Rock, B.N.; Moss, D.M. (International Journal of Remote Sensing, 1993)
Abstract Many sugar maple stands in the northeastern United States experienced extensive insect damage during the 1988 growing season. Chlorophyll data and high spectral resolution spectrometer laboratory reflectance data were acquired for multiple collections of single detached sugar maple leaves variously affected by the insect over the 1988 growing season. Reflectance data indicated consistent and diagnostic differences in the red edge portion (680-750 nm) of the spectrum among the various samples and populations of leaves. These included differences in the red edge inflection point (REIP), a ratio of reflectance at 740-720 nm (RE3/RE2), and a ratio of first derivative values at 715-705 nm (D715/D705), All three red edge parameters were highly correlated with variation in total chlorophyll content. Other spectral measures, including the Normalized Difference Vegetation Index (NDVI) and the Simple Vegetation Index Ratio (VI), also varied among populations and over the growing season, but did not correlate well with total chlorophyll content. Leaf stacking studies on light and dark backgrounds indicated REIP, RE3/RE2 and D715/D705 to be much less influenced by differences in green leaf biomass and background condition than either NDVI or VI.

New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice - Wang, Fu-min; Huang, Jing-feng; Tang, Yan-lin; Wang, Xiu-zhen (Rice Science, 2007)
Leaf area index (LAI) is an important characteristic of land surface vegetation system, and is also a key parameter for the models of global water balancing and carbon circulation. By using the reflectance values of Landsat-5 blue, green and red channels simulated from rice reflectance spectrum, the sensitivities of the bands to LAI were analyzed, and the response and capability to estimate LAI of various NDVIs (normalized difference vegetation indices), which were established by substituting the red band of general NDVI with all possible combinations of red, green and blue bands, were assessed. Finally, the conclusion was tested by rice data at different conditions. The sensitivities of red, green and blue bands to LAI were different under various conditions. When LAI was less than 3, red and blue bands were more sensitive to LAI. Though green band in the circumstances was less sensitive to LAI than red and blue bands, it was sensitive to LAI in a wider range. When the vegetation indices were constituted by all kinds of combinations of red, green and blue bands, the premise for making the sensitivity of these vegetation indices to LAI be meaningful was that the value of one of the combinations was greater than 0.024, i.e. visible reflectance (VIS)>0.024. Otherwise, the vegetation indices would be saturated, resulting in lower estimation accuracy of LAI. Comparison on the capabilities of the vegetation indices derived from all kinds of combinations of red, green and blue bands to LAI estimation showed that GNDVI (Green NDVI) and GBNDVI (Green-Blue NDVI) had the best relations with LAI. The capabilities of GNDVI and GBNDVI to LAI estimation were tested under different circumstances, and the same result was acquired. It suggested that GNDVI and GBNDVI performed better to predict LAI than the conventional NDVI.
Keywords: vegetation index; rice; leaf area index; reflectance spectrum; remote sensing

Detection of forest harvest type using multiple dates of Landsat TM imagery - Wilson, Emily Hoffhine; Sader, Steven A. (Remote Sensing of Environment, 2002)
A simple and relatively accurate technique for classifying time-series Landsat Thematic Mapper (TM) imagery to detect levels of forest harvest is the topic of this research. The accuracy of multidate classification of the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) were compared and the effect of the number of years (1â3, 3â4, 5â6 years) between image acquisition on forest change accuracy was evaluated. When Landsat image acquisitions were only 1â3 years apart, forest clearcuts were detected with producer's accuracy ranging from 79% to 96% using the RGB-NDMI classification method. Partial harvests were detected with lower producer's accuracy (55â80%) accuracy. The accuracy of both clearcut and partial harvests decreased as time between image acquisition increased. In all classification trials, the RGB-NDMI method produced significantly higher accuracies, compared to the RGB-NDVI. These results are interesting because the less common NDMI (using the reflected middle infrared band) outperformed the more popular NDVI. In northern Maine, industrial forest landowners have shifted from clearcutting to partial harvest systems in recent years. The RGB-NDMI change detection classification applied to Landsat TM imagery collected every 2â3 years appears to be a promising technique for monitoring forest harvesting and other disturbances that do not remove the entire overstory canopy.

AREA-AVERAGED VEGETATIVE COVER FRACTION ESTIMATED FROM SATELLITE DATA - Wittich, K. P.; Hansing, O. (International Journal of Biometeorology, 1995)
The relationship was analysed between the vegetation cover factor expressed as a percentage and the area-averaged normalized difference vegetation index (NDVI). On selected days the NDVI was calculated from channel 1 and 2 reflectance data of the National Oceanic and Atmospheric Administration (NOAA-11) satellite's advanced very high-resolution radiometer (AVHRR) for five test areas under agricultural and forestry use. No ground-based reflectance measurements could be made for validation of these data. Therefore the land surface NDVI, which varied with time, and percentage vegetation cover of the test areas were deduced from time-independent but site-specific statistical land use data updated by temporal phenological observations, and from surface-specific reflectance curves published in the literature. The result indicated that the area-averaged NDVI, as obtained from the NOAA-11 radiometer, was less than the value calculated from the land surface NDVI. After correction to reduce the offset of the data, the values would be a suitable indicator of the fraction of vegetation cover.

Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices - Wu, Chaoyang; Niu, Zheng; Tang, Quan; Huang, Wenjiang; Rivard, Benoit; Feng, Jilu (Agricultural and Forest Meteorology, 2009)
A number of recent studies have focused on estimating gross primary production (GPP) using vegetation indices (VIs). In this paper, GPP is retrieved as a product of incident light use efficiency (LUE), defined as GPP/PAR, and the photosynthetically active radiation (PAR). As a good correlation is found between canopy chlorophyll content and incident LUE for six types of wheat canopy (R2 = 0.87, n = 24), indices aimed for chlorophyll assessment can be used as an indicator of incident LUE and the product of chlorophyll indices and PAR will be a proxy of GPP. In a field experiment, we investigated four canopy chlorophyll content related indices (Red edge Normalized Difference Vegetation Index [Red Edge NDVI], modified Chlorophyll Absorption Ratio Index [MCARI710], Red Edge Chlorophyll Index [CIred edge] and the MERIS Terrestrial Chlorophyll Index [MTCI]) for GPP estimation during the growth cycle of wheat. These indices are validated for leaf and canopy chlorophyll estimation with ground truth data of canopy chlorophyll content. With ground truth data, a strong correlation is observed for canopy chlorophyll estimation with correlation coefficients R2 of 0.79, 0.84, 0.85 and 0.87 for Red Edge NDVI, MCARI710, CIred edge and MTCI, respectively (n = 24). As evidence of the existence of a relationship between canopy chlorophyll and GPP/PAR, these indices are shown to be a good proxy of GPP/PAR with R2 ranging from 0.70 for Red Edge NDVI and 0.75 for MTCI (n = 240). Remote estimation of GPP from canopy chlorophyll content Ã PAR is proved to be relatively successful (R2 of 0.47, 0.53, 0.65 and 0.66 for Red edge NDVI, MCARI710, CIred edge and MTCI respectively, n = 240). These results open up a new possibility to estimate GPP and should inspire new models for remote sensing of GPP.
Keywords: GPP; LUE; Vegetation indices; Canopy chlorophyll content; Sensitivity; Validation

Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies - Wu, Jindong; Wang, Dong; Bauer, Marvin E. (Field Crops Research, 2007)
Leaf area index (LAI) is a key biophysical variable that can be used to derive agronomic information for field management and yield prediction. In the context of applying broadband and high spatial resolution satellite sensor data to agricultural applications at the field scale, an improved method was developed to evaluate commonly used broadband vegetation indices (VIs) for the estimation of LAI with VIâLAI relationships. The evaluation was based on direct measurement of corn and potato canopies and on QuickBird multispectral images acquired in three growing seasons. The selected VIs were correlated strongly with LAI but with different efficiencies for LAI estimation as a result of the differences in the stabilities, the sensitivities, and the dynamic ranges. Analysis of error propagation showed that LAI noise inherent in each VIâLAI function generally increased with increasing LAI and the efficiency of most VIs was low at high LAI levels. Among selected VIs, the modified soil-adjusted vegetation index (MSAVI) was the best LAI estimator with the largest dynamic range and the highest sensitivity and overall efficiency for both crops. QuickBird image-estimated LAI with MSAVIâLAI relationships agreed well with ground-measured LAI with the root-mean-square-error of 0.63 and 0.79 for corn and potato canopies, respectively. LAI estimated from the high spatial resolution pixel data exhibited spatial variability similar to the ground plot measurements. For field scale agricultural applications, MSAVIâLAI relationships are easy-to-apply and reasonably accurate for estimating LAI.
Keywords: Leaf area index; QuickBird; Remote sensing; Sensitivity; Stability; Vegetation index

Airborne Hyperspectral Imagery and Yield Monitor Data for Mapping Cotton Yield Variability - Yang, Chenghai; Everitt, James H.; Bradford, Joe M. (Precision Agriculture, 2004)
Increased availability of hyperspectral imagery necessitates the evaluation of its potential for precision agriculture applications. This study examined airborne hyperspectral imagery for mapping cotton (Gossypium hirsutum L.) yield variability as compared with yield monitor data. Hyperspectral images were acquired using an airborne imaging system from two cotton fields during the 2001 growing season, and yield data were collected from the fields using a cotton yield monitor. The raw hyperspectral images contained 128 bands between 457 and 922 nm. The raw images were geometrically corrected, georeferenced and resampled to 1 m resolution, and then converted to reflectance. Aggregation functions were then applied to each of the 128 bands to reduce the cell resolution to 4 m (close to the cotton pickerâs cutting width) and 8 m. The yield data were also aggregated to the two grids. Correlation analysis showed that cotton yield was significantly related to the image data for all the bands except for a few bands in the transitional range from the red to the near-infrared region. Stepwise regression performed on the yield and hyperspectral data identified significant bands and band combinations for estimating yield variability for the two fields. Narrow band normalized difference vegetation indices derived from the significant bands provided better yield estimation than most of the individual bands. The stepwise regression models based on the significant narrow bands explained 61% and 69% of the variability in yield for the two fields, respectively. To demonstrate if narrow bands may be better for yield estimation than broad bands, the hyperspectral bands were aggregated into Landsat-7 ETM+ sensorâs bandwidths. The stepwise regression models based on the four broad bands explained only 42% and 58% of the yield variability for the two fields, respectively. These results indicate that hyperspectral imagery may be a useful data source for mapping crop yield variability.
Keywords: cotton, hyperspectral imagery, remote sensing, yield monitor, yield mapping

Land Cover Mapping at BOREAS using red edge spectral parameters from CASI imagery - Zarco-Tejada P.J.; Miller J.R. (J. Geophys. Res. Atmospheres., 1999)

REMOTE SENSING OF VEGETATION BIOPHYSICAL PARAMETERS FOR DETECTING STRESS CONDITION AND LAND COVER CHANGES - Zarco-Tejada P.J.; Sepulcre-CantÃ³, G. (Estudios de la Zona No Saturada del Suelo, 2007)

Evaluating the fraction of vegetation cover based on NDVI spatial scale correction model - Zhang, X.; Yan, G.; Li, Q.; Li, Z. L.; Wan, H.; Guo, L. (International Journal of Remote Sensing, 2006)
Vegetation index (VI) is an important variable for retrieving the vegetation biophysical parameters. With different kinds of remote sensing data sets, it is easy to get the VI at different spatial and temporal resolutions. However, the main concern is whether the relationship existing at some scale between the VI and biophysical parameters is still applicable to other scales. This paper first presents a method to correct the spatial scaling effect of NDVI by mathernatic analysis, and then analyses NDVI scale sensitivity with data front a spectral database. The result shows that the NDVI obtained by reflectance up-scaling is larger than the Up-scaled NDVI from NDVI itself in most situations. The NDVI scaling effect is more significant when water exists in a pixel, and increases with the increase in the difference of the sum of visible reflectance and near-infrared (NIR) reflectance between the vegetation and soil. Finally, a method is proposed to estimate the fraction or vegetation cover (FVC) on the basis of the NDVI spatial scaling correction model. The method is accurate enough to assess the FVC taking into account the scaling effect.

Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors - Zheng, Guang; Moskal, L. Monika (Sensors, 2009)
Keywords: LAI; Optical instruments; Radar

The IndexDatabase - Henrich, V.; Krauss, G.; Götze, C.; Sandow, C. (2011)