Spatially adaptive estimation of multi-layer soil temperature at a daily time-step across China during 2010–2020

Abstract

Soil temperature (Ts) is critical in regulating agricultural production, ecosystem functions, hydrological cycling and climate dynamics. However, the inherent spatial and temporal heterogeneity of soil thermal regimes constitutes a persistent challenge in obtaining high-resolution, continuous gridded Ts datasets along vertical profiles. To address this issue, we propose a spatially adaptive layer-cascading Extreme Gradient Boosting (XGBoost) algorithm to generate daily multi-layer Ts data (0, 5, 10, 15, 20, and 40 cm) at a spatial resolution of 1 km in China from 2010 to 2020. The methodology dynamically partitions non-uniformly distributed measuring sites (2093 sites across the country) to quadtrees and incorporates thermal coupling effects propagated between neighbor soil layers. Multi-source data, including satellite retrievals of land surface temperature and vegetation index, and ERA5 reanalysis climate variables were used as inputs. Validation using both spatially independent test sets and flux-tower observations demonstrated the robustness and accuracy of the product. It is noted the model's performance was lower in summers and winters than in springs and autumns. Compared to existing global or regional Ts products, the dataset developed here is characterized by its fine spatio-temporal patterns and high reliability, enabling it to provide supports for precision agriculture, ecosystem modeling and understanding climate-land feedback.