Estimation of aboveground biomass of senescence grassland in China's arid region using multi-source data

Aboveground Biomass (AGB) in the grassland senescence period is a key indicator for assessing grassland fire risk and autumnal pasture carrying capacity. Despite the advancement of remote sensing in rapid monitoring of AGB on a regional scale, accurately monitoring AGB during the senescence period in vast arid areas remains a major challenge. Using remote sensing, environmental data, and 356 samples of grassland senescence period AGB data, this study utilizes the Gram-Schmidt Pan Sharpening (GS) method, multivariate selection methods, and machine learning algorithms (RF, SVM, and BP_ANN) to construct a model for AGB during senescence grassland, and applies the optimal model to analyze spatio-temporal pattern changes in AGB from 2000 to 2021 in arid regions. The results indicate that the GS method effectively enhances the correlation between measured AGB and vegetation indices, reducing model error to some extent; The accuracy of grassland AGB inversion models based on a single vegetation index is low (0.03 ≤ |R| ≤ 0.63), while the RF model constructed with multiple variables selected by the Boruta algorithm is the optimal model for estimating AGB in arid regions during the senescence period (R