@article {bnh-3229, title = {A radiative transfer model-based method for the estimation of grassland aboveground biomass}, journal = {International Journal of Applied Earth Observation and Geoinformation}, volume = {54}, year = {2017}, month = {02/2017}, 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\ {\texttimes}\ 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 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.

}, doi = {http://dx.doi.org/10.1016/j.jag.2016.10.002}, url = {http://www.sciencedirect.com/science/article/pii/S0303243416301726}, author = {Xingwen Quan and Binbin He and Marta Yebra and Changming Yin and Zhanmang Liao and Xueting Zhang and Xing Li} }