TSAVI: A Vegetation Index Which Minimizes Soil Brightness Effects On LAI And APAR Estimation

Abstract

Soil optical properties affect spectral response of crop canopies and induce noise onto the relationships between reflectance data and crop characteristics such as leaf area index (LAI) or absorbed PAR (APAR). Different combinations of red and near infrared bands have been proposed but still suffered from a high sensitivity to soil brightness. As ideal vegetation index does not exist, we describe an improved vegetation index, the transformed soil adjusted vegetation index (TSAVI). It is based on similar principles as those stated by Huete (1988) and Major et al. (1988). We have used the SAIL model to simulate in different conditions the relationships between TSAVI and LAI or APAR. Experimental data recorded on wheat crops during the growing season are in good agreement with previous theoretical results.The W -TSAVI relationship can be set in the form oE TSAVI=TSAVI,(l-exp( -KTSAVI. LAI)) TSAVI, depends slightly on leaf angle distribution. The extinction coefficient, KTSAVI, depends on solar zenith angle and leaf angle distribution. The relationship between TSAVI measured at solar noon and MAR can be approximated to the linear simple form: MAR= c.TSAVIThis relationship is also in good agreement with experimental results with c = 1.205.

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

“…To reduce or remove soil background influence in sparse green vegetation cover situation, substantial studies have been conducted to improve soil-unadjusted vegetation indices and to develop soil-adjusted vegetation indices [Richardson and Wiegand, 1977;Huete, 1988;Baret et al, 1989;Baret and Guyot, 1991;Qi et al, 1994;Rondeaux et al, 1996]. As shown in Table 2, these soil-adjusted vegetation indices, except the GSAVI, yielded better performance than the soil-unadjusted NDVI for green aboveground biomass estimation in our study site.…”

“…Based on the algorithm of NDVI, Huete [1988] therefore proposed soil-adjusted vegetation index (SAVI) to minimize soil background influence by incorporating a correcting factor. Further evolutions of the SAVI are the modified soil-adjusted vegetation index (MSAVI) [Qi et al, 1994], optimised soil-adjusted vegetation index (OSAVI) [Rondeaux et al, 1996], transformed soil-adjusted vegetation index (TSAVI) [Baret et al, 1989], adjusted transformed soil-adjusted vegetation index (ATSAVI) [Baret and Guyot, 1991], perpendicular vegetation index (PVI) [Richardson and Wiegand, 1977], and green-adjusted vegetation index (GSAVI) [Tian et al, 2005]. Among these soil-adjusted vegetation indices, the TSAVI, ATSAVI, and PVI were developed to minimize soil background influence by incorporating the slope and intercept of the soil line, which was established by linear regression of soil reflectance in red-NIR spectral portions.…”

Responsive in-season nitrogen management for cereals

“…This is because of the confounding effect of soil background in aerial imagery data collected before canopy closure. Other researchers (Huete, 1988;Rondeaux et al, 1996;Baret et al, 1989) have attempted to remove the soil background effect through mathematical manipulations of the various reflectance bands (i.e., soil-adjusted vegetative index (SAVI) or transformed soil-adjusted vegetative index (TSAVI)). However, Shanahan et al (2001) indicated that the TSAVI equation was no better, and often worse, than the GNDVI in detecting variation in canopy vigor or greenness during early season growth.…”

Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

“…The foreground classes have been chosen within a set of key structuring landscape objects described in Section 2.1.1. The separability analysis, described in Section 2.1.2, has been performed for the spectral bands, for common spectral indices used to enhance the discrimination as well as for new indices based on the less common spectral bands available from Sentinel-2 (Table 2 [ [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58]). For this purpose, pure spectral signatures of objects have been extracted from the images at well known locations (see Section 3).…”