Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands - Wang, Shaohua; Fu, Gang (Frontiers in Environmental Science, 2023)
Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (tem-perature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evalu-ated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) var-iation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest mod-els led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differenti-ating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region.