GCMCNN: a geographically conditioned multiscale convolutional neural network for downscaling land surface temperature

Methods such as multi-factor geographically weighted machine learning (MFGWML), geographically neural network weighted regression (GNNWR), and its enhanced version—geographically convolutional neural network weighted regression (GCNNWR)—have improved spatial non-stationarity modeling. However, they are over-reliant on spatial proximity weighting and underutilize spatial neighborhood information. Therefore, this study proposes a novel Geographically Conditioned Multiscale Convolutional Neural Network (GCMCNN) that downscales MODIS land surface temperature (LST) data to 100 m resolution by integrating two- and three-dimensional surface features. GCMCNN enhances the capture of spatial heterogeneity through a geographically aware (geo-aware) mechanism conditioned by location and attributes, while improving the characterization of spatial correlation by developing a multiscale convolutional neural network (CNN). GCMCNN model outperformed four benchmark models (random forest, MFGWML, GNNWR, and GCNNWR) in LST downscaling, reducing RMSE by 15.4–26.16% and MAE by 15.05–23.59%, and increasing R2 by 39.81–148.09%. GCMCNN improved the model performance of CNN through its geo-aware mechanism and multi-level spatial feature extraction and fusion, reducing RMSE and MAE by 6.6% and 8%, respectively, and increasing R2 by 7.5%. Furthermore, spatial block cross-validation better reflects the model’s true generalization ability than random cross-validation. This study highlights the importance of accurately modeling spatial heterogeneity and correlation in LST downscaling.