A radiative transfer model-based method for the estimation of grassland aboveground biomass
2017
Quan, Xingwen | He, Binbin | Yebra, Marta | Yin, Changming | Liao, Zhanmang | Zhang, Xueting | Li, Xing
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.
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