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Toward the development of a multidirectional vegetation index
Author(s) -
Chehbouni A.,
Kerr Y. H.,
Qi J.,
Huete A. R.,
Sorooshian S.
Publication year - 1994
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/93wr03063
Subject(s) - vegetation (pathology) , watershed , leaf area index , remote sensing , environmental science , shadow (psychology) , vegetation index , canopy , grassland , nadir , normalized difference vegetation index , atmospheric sciences , geology , geography , satellite , computer science , ecology , medicine , pathology , psychology , archaeology , machine learning , aerospace engineering , engineering , psychotherapist , biology
Vegetation‐related information is critical for modeling hydrological processes. Remotely sensed spectral data provide powerful means to characterize vegetation status. However, the non‐Lambertian behavior of most surfaces induces a large view/Sun angle dependence. Two approaches are possible to correct such contamination. One approach consists of using directional reflectance models; the other consists of normalizing the Sun/view angle effects directly on vegetation indices that have the advantage of being less sensitive to surface anisotropy than individual reflectances. Ground‐based multiple view direction/angle measurements made over a semiarid grassland canopy at the Walnut Gulch experiment watershed (Monsoon '90 experiment) were used to develop and to validate a semiempirical model to normalize the MSAVI (modified soil‐adjusted vegetation index) response to a nadir, regardless of view/direction angle. We further advanced this model to account simultaneously for both Sun and view angle variations by introducing a shadow parameterization. The results showed that this model can be used to monitor the vegetation status using a single view/Sun configuration throughout the growing season. We therefore believe that we have taken a further step toward the development of a multidirectional vegetation index.