A spatiotemporal-guided hybrid learning model for mapping surface air relative humidity

Accurately mapping the spatial distribution of surface air relative humidity (RH) is critically important for diverse fields including climate change, public health, and environmental conservation. Although machine learning has exhibited considerable advantages in RH mapping, factors such as its inherent bottom-up learning strategy, the unique spatiotemporal characteristics of geographical phenomena, and the scarcity of ground-based observations limit model performance. To address these limitations, this study proposes a novel spatiotemporal-guided hybrid learning (STGHL) model for precise RH mapping. The model establishes a hybrid learning paradigm that integrates principles of spatiotemporal autocorrelation and heterogeneity into deep learning architectures. The paradigm organically combines top-down guided learning with bottom-up spontaneous learning through the design of three specialized guided learning modules, which collectively enable neural networks to deeply represent the complex spatiotemporal patterns of RH. We conducted a comprehensive evaluation of the STGHL model by generating daily RH maps across the Chinese mainland during the period 2014–2018. The rigorous 10-fold spatial cross-validation results show that the STGHL model achieves exceptional performance, with an R2 of 0.92, a mean absolute error (MAE) of 3.93%, and a root mean square error (RMSE) of 5.24%. Compared with state-of-the-art machine learning-based mapping models, the proposed model demonstrates significant improvements in accuracy, generalization capability, and explainability, reducing MAE and RMSE by at least 13% and 14%, respectively. This study contributes to the field by establishing a novel spatiotemporal-guided hybrid learning paradigm that offering new insights and approaches for geospatial phenomena mapping, while promoting the profound integration of machine learning and geoscience disciplines.