Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands

Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, 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 evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R (2) >= 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., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region.

Automated summary

In this study, the ability of nine algorithms to estimate soil moisture variation using climate data and the normalized difference vegetation index (NDVI) was evaluated. The constructed random forest models based on climate data and NDVI showed the best performance in estimating soil moisture variation. Therefore, these models can be applied to estimate spatiotemporal variations in soil moisture and for other related scientific research.