Generating Spatiotemporally Continuous Grassland Aboveground Biomass on the Tibetan Plateau Through PROSAIL Model Inversion on Google Earth Engine
Jiangliu Xie, Changjing Wang, Dujuan Ma, Rui Chen, Qiaoyun Xie, Baodong Xu, Wei Zhao, Gaofei Yin
Abstract
Spatiotemporally continuous monitoring of aboveground biomass (AGB), an important indicator of grassland productivity, is crucial for achieving sustainable grassland development. Most existing grassland AGB estimation methods are empirical, and their temporally and spatially specific nature hinders operational application at large scales. Grass is herbaceous, so its AGB can be represented as the product of leaf area index (LAI) and dry matter content ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C_{m}$ </tex-math></inline-formula> ), both are the inputs of PROSAIL model. We, therefore, proposed a novel physical-based method through PROSAIL model inversion. Results showed that the estimated AGB presented good consistency with field-measured one, with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}= 0.87$ </tex-math></inline-formula> and RMSE = 14.29 g/m2. We then implemented our method on the Google Earth Engine platform and generated daily and monthly AGB products covering the Tibetan Plateau (TP) and spanning from 2000 to 2021. These products characterized the spatiotemporally continuous dynamics of AGB on the TP. For example, it captured the decrease in dry matter caused by grazing during grassland dormancy, which is impossible for other existing AGB retrieval methods. Our method provides a promising tool to generate spatiotemporally continuous grassland AGB, which would inform the decision making for the conservation and restoration of grassland.