Paper Info
Title
Sensitivity Analysis of Vegetation Reflectance to Biochemical and Biophysical Variables at Leaf, Canopy, and Regional Scales
Abstract
The objective of this paper is to investigate the sensitivity of reflectance to the variation in biochemical and biophysical variables at leaf, canopy, and regional scales using a modeling approach. The results show that, at the leaf scale, the variations in chlorophyll a+b content, the leaf structure parameter, and the water content dominate the reflectance variance in the visible light (VIS), near infrared (NIR), and short-wave infrared (SWIR) regions, respectively. At the canopy scale, the sensitivity of reflectance to variation in the leaf structure parameter is very slight. For sparse foliage cover (leaf area index ), LAI is the most important variable to the canopy reflectance. As LAI increases, the sensitivity of reflectance to variation in LAI is reduced to a very low value. Moreover, chlorophyll a+b, dry matter, and water content control the variation of canopy reflectance in the VIS, NIR, and SWIR regions, respectively. At the regional scale, the sensitivity of reflectance to variation in vegetation variables is highly influenced by the mixed pixels. Thirty-six vegetation indices (VIs) are chosen in this paper to illustrate the scale dependence of the estimation accuracy of vegetation variables. The results show that the relationships between the VIs and the variables highly depend on the observation scale. For chlorophyll a+b content estimation, transformed chlorophyll absorption in reflectance index (TCARI), Blue Green pigment Index, leaf chlorophyll index (LCI), modified Normalized Difference (mND705), and Plant Biochemical Index at the leaf scale and canopy scale of and TCARI at the canopy scale of are highly related. The correlation between the indices and chlorophyll content in the regional scale is, however, much lower. For water content estimation, disease water stress index (DSWI), leaf water vegetation index 2 (LWVI_2), moisture stress index (MSI), normalized difference infrared index (NDII), normalized difference water index (NDWI), hyperspectr- l perpendicular vegetation index (RVI), SWIR water stress index (SIWSI), SR water index (SRWI), and water index (WI) are good choices at the leaf scale and canopy scale of , while at the canopy scale of and the regional scale, the correlation between the indices and water content is very low. For LAI estimation, VIs, including the Greenness Index, simple ratio (SR), Normalized Difference VI, modified soil-adjusted vegetation index (MSAVI), modified triangular vegetation index 1 (MTVI1), modified triangular vegetation index 2 (MTVI2), optimized soil-adjusted vegetation index (OSAVI), modified chlorophyll absorption ratio index 1 (MCARI1), modified chlorophyll absorption ratio index 2 (MCARI2), Enhanced VI, LAI Determining Index, renormalized difference vegetation index (RDVI), Spectral Polygon VI, Wide Dynamic Range VI, and triangular vegetation index (TVI), have high correlation with LAI at the canopy scale of while a low correlation at the canopy scale of and the regional scale.
Year
DOI
Venue
2014
10.1109/TGRS.2013.2278838
IEEE T. Geoscience and Remote Sensing
Keywords
Field
DocType
leaf structure parameter,msavi,rdvi,vegetation reflectance,remote sensing,normalized difference water index,spectral polygon vi,ndwi,vegetation index (vi),leaf scale biochemical variation,nir canopy reflectance variation,scale,greenness index,modified triangular vegetation index 1,leaf water vegetation index,modeling approach,visible canopy reflectance variation,normalized difference infrared index,lai variation,moisture stress index,modified soil adjusted vegetation index,leaf chlorophyll index,ndii,modified normalized difference,tcari leaf scale,vegetation variable variation,organic compounds,mixed pixels,dswi,mcari2,modified triangular vegetation index 2,renormalized difference vegetation index,rvi,sr water index,disease water stress index,water,blue green pigment index,vegetation indices,osavi,water content,radiative transfer,chlorophyll content estimation,swir water stress index,regional scale biophysical variation,leaf area index,prospect + sail,canopy scale biochemical variation,visible reflectance variance,dry matter,short wave infrared reflectance variance,swir canopy reflectance variation,srwi,canopy scale biophysical variation,optimized soil adjusted vegetation index,tcari canopy scale,tvi,sparse foliage cover,estimation accuracy,transformed chlorophyll absorption in reflectance index,leaf scale biophysical variation,wide dynamic range vi,hyperspectral perpendicular vegetation index,modified chlorophyll absorption ratio index 2,mcari1,chlorophyll b content variations,siwsi,extended fourier amplitude sensitivity test (efast),sensitivity analysis (sa),plant biochemical index,modified chlorophyll absorption ratio index 1,vegetation,regional scale biochemical variation,chlorophyll a content variations,sensitivity analysis,near infrared reflectance variance,sensitivity,indexes,pigments,absorption,estimation
Moisture stress,Leaf area index,Vegetation,Chlorophyll a,Remote sensing,Enhanced vegetation index,Water content,Mathematics,Canopy,Chlorophyll
Journal
Volume
Issue
ISSN
52
7
0196-2892
Citations 
PageRank 
References 
11
0.73
1
Authors
4
Name
Order
Citations
PageRank
Yanfang Xiao1111.41
Zhao Wenji2122.28
Demin Zhou3110.73
Huili Gong48829.37