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.

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.

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.

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

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)