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

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.

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

Retrieving wheat Green Area Index during the growing season from optical time series measurements based on neural network radiative transfer inversion - Duveiller, Grégory; Weiss, Marie; Baret, Frédéric; Defourny, Pierre (Remote Sensing of Environment, 2011)
Keywords: Winter wheat; Leaf Area Index; Green Area Index; Radiative transfer inversion; Neural networks; Saturation; SPOT/HRV; SPOT/HRVIR; Time series

Intercomparison and sensitivity analysis of Leaf Area Index retrievals from LAI-2000, AccuPAR, and digital hemispherical photography over croplands - Garrigues, S.; Shabanov, N. V.; Swanson, K.; Morisette, J. T.; Baret, F.; Myneni, R. B. (Agricultural and Forest Meteorology, 2008)
Keywords: Effective Plant Area Index; Gap fraction; Optical techniques; LAI-2000; AccuPAR; Digital Hemispherical Photograph; LAI validation

High spectral resolution: Determination of specral shifts between the red and the near infrared - Guyot, G.; Baret, F.; Major, D. J. (International Archives of Photogrammetry and Remote Sensing, 1988)

Investigation of the Relationship between Chlorophyll Concentration and High Spectral Resolution Data of Phragmites australis in Heavy Metal Contaminated Sites - Hecker, Jeanna Hyde (International Institute for Geo-Information Science and Earth Observation, 2003)

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.

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

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.

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)

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

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

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.

Experimental assessment of the Sentinel-2 band setting for RTM-based LAI retrieval of sugar beet and maize - Richter, K.; Atzberger, C.; Vuolo, F.; Weihs, P.; DâUrso, G. (Canadian Journal of Remote Sensing, 2009)

Optimization of soil-adjusted vegetation indices - Rondeaux, Geneviève; Steven, Michael; Baret, Frédéric (Remote Sensing of Environment, 1996)
The sensitivity of the normalized difference vegetation index (NDVI) to soil background and atmospheric effects has generated an increasing interest in the development of new indices, such as the soil-adjusted vegetation index (SAVI), transformed soil-adjusted vegetation index (TSAVI), atmospherically resistant vegetation index (AR VI), global environment monitoring index (GEMI), modified soil-adjusted vegetation index (MSAVI), which are less sensitive to these external influences. These indices are theoretically more reliable than NDVI, although they are not yet widely used with satellite data. This article focuses on testing and comparing the sensitivity of NDVI, SAVI, TSAVI, MSAVI and GEMI to soil background effects. Indices are simulated with the SAIL model for a large range of soil reflectances, including sand, clay, and dark peat, with additional variations induced by moisture and roughness. The general formulation of the SAVI family of indices with the form VI = (NIR - R) / (NIR + R + X) is also reexamined. The value of the parameter X is critical in the minimization of soil effects. A value of X = 0.16 is found as the optimized value. Index performances are compared by means of an analysis of variance.

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)

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)

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