A radiative transfer model-based method for the estimation of grassland aboveground biomass

Abstract

This paper presents a novel method to derive grassland aboveground biomass (AGB) based on the PROSAILH (PROSPECT+SAILH) radiative transfer model (RTM). Two variables, leaf area index (LAI, m2m−2, defined as a one-side leaf area per unit of horizontal ground area) and dry matter content (DMC, gcm−2, defined as the dry matter per leaf area), were retrieved using PROSAILH and reflectance data from Landsat 8 OLI product. The result of LAI×DMC was regarded as the estimated grassland AGB according to their definitions. The well-known ill-posed inversion problem when inverting PROSAILH was alleviated using ecological criteria to constrain the simulation scenario and therefore the number of simulated spectra. A case study of the presented method was applied to a plateau grassland in China to estimate its AGB. The results were compared to those obtained using an exponential regression, a partial least squares regression (PLSR) and an artificial neural networks (ANN). The RTM-based method offered higher accuracy (R2=0.64 and RMSE=42.67gm−2) than the exponential regression (R2=0.48 and RMSE=41.65gm−2) and the ANN (R2=0.43 and RMSE=46.26gm−2). However, the proposed method offered similar performance than PLSR as presented better determination coefficient than PLSR (R2=0.55) but higher RMSE (RMSE=37.79gm−2). Although it is still necessary to test these methodologies in other areas, the RTM-based method offers greater robustness and reproducibility to estimate grassland AGB at large scale without the need to collect field measurements and therefore is considered the most promising methodology.