Within-season estimates of 10 m aboveground biomass based on Landsat, Sentinel-2 and PlanetScope data

Abstract

Abstract Monitoring within-season dynamics of aboveground biomass (AGB) is critical for adaptive decision-making in grasslands to respond to changing conditions caused by frequent disturbances (e.g., grazing activities, fires, climate variations). This study trained the random forest model based on intensive observations from harmonious remote sensing data (Landsat, Sentinel-2, Sentinel-1) and an accurate date grassland sample database. Then we used monthly average remote sensing data to estimate monthly AGB at 10 m across three distinct grassland types in China: temperate meadow steppe, temperate typical steppe, and temperate desert steppe. Considering all the grassland types, the mean predicted AGB values were 83.02 g/m2 in June, 103.02 g/m2 in July, 108.49 g/m2 in August, and 106.89 g/m2 in September. The overall prediction accuracy was evaluated as R2 = 0.68, RMSE = 47.87 g/m². Additionally, the comparison of our monthly AGB maps with 3 m PlanetScope images and the published 30 m yearly AGB map shows the significant advantages of monthly 10 m products to capture the spatial and within-season dynamics of grassland AGB.