Quan, XingwenHe, BinbinYebra, MartaYin, ChangmingLiao, ZhanmangZhang, XuetingLi, Xing2021-04-301569-8432http://hdl.handle.net/1885/231163This 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 = 42.67 gm−2)than the exponential regression (R2 = 0.48 and RMSE = 41.65 gm−2) and the ANN (R2 = 0.43 and RMSE = 46.26 gm−2). However, the proposed method offered similar performance than PLSR as presented better determination coefficient than PLSR (R2 = 0.55) but higher RMSE (RMSE = 37.79 gm−2). Although it is still necessary to test these methodologies in other areas, the RTMbased 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.This work was supported by the National Natural Science Foundation of China (Contract No. 41471293 & 41671361),the Fundamental Research Fund for the Central Universities (Contract No. ZYGX2012Z005) and the National High-Tech Research and Development Program of China (Contract 2013AA12A302)application/pdfen-AU© 2016 Elsevier B.VGrassland aboveground biomassLandsat 8 OLI productLeaf area indexPROSAILHIll-posed inversion problem201710.1016/j.jag.2016.10.0022020-11-23