Vegetation water and dry matter contents estimated from top-of-the-atmosphere reflectance data: A simulation study

Abstract

Leaf (PROSPECT), soil, canopy (SAIL), and atmosphere (6S) models were coupled and used to create a large set of simulated reflectance spectra with corresponding water content per unit leaf area (C W), dry matter content per unit leaf area (SLW, i.e., the specific leaf weight), leaf area index (LAI), whole canopy water content (C W.LAI), and whole canopy dry matter content (SLW LAI). Multiple linear regression was used to estimate these canopy variables from the simulated satellite reflectance spectra within the 880 2380-nm domain. Our data set was subdivided into calibration and validation subsets to evaluate the predictive power of the relations. Canopy-level variables (C,.LAI,SLWLAI,LAI) were retrieved with a good accuracy, whereas leaf-level variables (C W SLW) were less accurately retrieved. The radiometric resolution of the simulated sensor greatly affected the accuracy of the estimation. Conversely the spectral resolution between 10 and 20 nm was not critical, the largest spectral resolution providing the most accurate estimates because it smoothed the instrument noise. We used multiple linear regression to select between five and eight wave bands for each canopy variable. Several wave bands selected were common to different canopy variables. Therefore, a set of ten wavebands centered on about 890, 1080, 1210, 1290, 1535, 1705, 2035, 2205, 2260, and 2295 nm efficiently allowed reasonable estimates of the variables investigated with varying coefficients for each of the canopy variables. For each variable, neural networks were trained over the wave bands selected by the multiple regression. Results showed better performances than classical multiple linear regression. Shifting the wave bands by 10 or 20 nm when calibrating and testing the networks slightly decreased the accuracy of the estimation. The difference was more pronounced for C W and SLW. Conversely, when equations were generated with the use of the wave bands at their optimal position and validated by using wave bands shifted by 10 or 20 nm, the accuracy of estimation for all variables except LAI was low. These results are discussed with emphasis on the design of future sensors.