High resolution soil moisture mapping in 3D space and time using machine learning and depth functions

Soil moisture is a key factor in hydrological, biological, and chemical processes, and plays a critical role in maintaining ecosystem balance. To generate high-resolution soil moisture maps at regional scales, researchers primarily employed in-situ observation-based spatial interpolation and remote sensing-based downscaling methods. However, direct comparisons between these methods are scarce. Additionally, remote sensing techniques are limited to the topsoil layer, and in-situ observations often have large depth intervals, thereby constraining the vertical resolution of subsurface soil moisture mapping. To address these challenges, we utilized an equal-area spline depth function combined with machine learning to map high spatial-vertical-resolution daily soil moisture across the Qinghai-Tibet Plateau. The performance of spatial interpolation and downscaling methods in mapping surface soil moisture at 0-5 cm depth were also compared. The results revealed that both spatial interpolation and downscaling methods produced unbiased predictions. However, prediction accuracy was lower in the peripheral subareas of the study area which had lower sampling density. Maps generated through the spatial interpolation method better captured detailed environmental covariates, whereas those obtained with downscaling methods were smoother. The fitting of depth functions introduced only small errors, but caution is still needed when predicting at unobserved depths. For subsurface soil moisture mapping using depth functions combined with spatial interpolation, validation results at two depth intervals showed improvements over surface predictions, with a root mean squared error (RMSE) reduced by 6.45 % to 17.2 % and unbiased RMSE by 5.95 % to 19.04 %. Furthermore, the analysis of variable importance highlighted the critical role of time-varying covariates. Future research should focus on optimizing depth functions and combining datadriven with knowledge-driven approaches. This study serves as a reference for mapping soil moisture with fine spatial-vertical-resolution in large-scale study areas.