Global Solar Radiation (Rs) is essential for ecological and climatic modeling, yet its spatialization is often hampered by sparse observation networks. Conventional methods demand a well-distributed set of stations with global representativeness—a requirement rarely met in practice. To address this gap, we propose a spatialization method based on environmental similarity and spatial proximity (ES-SP), which integrates the Law of Geographic Similarity and Tobler’s First Law of Geography. Using monthly Rs data from 11 stations in Tropical China (2015), we evaluated ES-SP against Ordinary Kriging (OK) and Local Polynomial Interpolation (LP) through leave-one-out cross-validation (LOOCV), with root mean square error (RMSE), relative RMSE, and mean absolute percentage error (MAPE) as accuracy metrics. Topographic and monthly meteorological covariates were selected dynamically via random forest (RF), and the performance differences among the three methods were tested statistically using the Wilcoxon signed-rank test. Results show that ES-SP outperforms both OK and LP in accuracy and stability, achieving the lowest error metrics in most months—e.g., RMSE as low as 37.23 MJ·m⁻² in December and MAPE as low as 4.34% in August—along with a narrow interquartile range, indicating consistent performance across seasons. Spatially, ES-SP accurately reproduces the coastal–inland gradient during the rainy season (May) and the latitudinal gradient in the dry season (January), whereas OK yields overly smooth distributions that obscure local details, and LP exhibits extreme instability and unrealistic spatial discontinuities. The study demonstrates that the ES-SP method effectively overcomes the reliance on globally representative station samples, providing a robust technical pathway for generating continuous Rs datasets in data-sparse regions such as Tropical China. Further research should focus on extending the geographic scope and refining the covariate set to enhance generalizability.

Rapid urbanization and complex topography complicate Urban Heat Island (UHI) spatio-temporal dynamics. Traditional models and coarse-resolution imagery often fail to capture fine-scale, spatially non-stationary seasonal driving mechanisms. This study investigates the multi-dimensional drivers of surface thermal dynamics in Kunming, a typical low-latitude plateau city, using seasonal median LST composite (2018–2025). Integrating eXtreme Gradient Boosting (XGBoost) with eXplainable Artificial Intelligence (XAI) models decoupled the nonlinear impacts of these drivers. Results reveal a seasonal thermal dichotomy: Summer exhibits the most intense UHI effect with extreme peak temperatures, while Spring presents an anomaly where natural and vegetated Local Climate Zones (LCZs) show pronounced warming. SHapley Additive exPlanations (SHAP) analysis identified a seasonal rotation: anthropogenic and structural factors dominate Summer and Autumn warming, whereas natural and topographic regulators govern Spring and Winter. GeoShapley deconstruction demonstrated strong spatial non-stationarity. Building-density warming is amplified in poorly ventilated urban cores, and fragmented vegetation’s cooling is offset by anthropogenic heat during peak summer. This study provides new insights into the seasonal drivers of urban thermal environments in plateau cities.

Spatial prediction requires determining how sampled observations support estimates at unsampled locations. Existing methods commonly represent these relationships in geographic space, environmental feature space, or through a global statistical relationship between predictors and the target property. We propose a dual-space comprehensive similarity (CS) framework that combines target-oriented environmental similarity (ES) with geographic spatial proximity (SP). ES uses partial least squares screening and principal component transformation, whereas SP derives from scaled geographic distance. Their product gives high influence only to observations supported in both spaces. We embedded CS in individual predictive soil mapping (iPSM), creating iPSM-CS, and evaluated it using 547 soil organic carbon density observations from the Tibetan Plateau. Repeated experiments were conducted using training datasets of 100, 200, and 400 samples, with an independent 27-sample validation set and a 20-sample test set. The 500-sample scenario was evaluated separately using fixed neighbourhood sizes determined before final testing. Across the three repeated sample-size scenarios, iPSM-CS generally produced higher correlation coefficients (r) and lower root mean square error (RMSE) and mean absolute error (MAE) values than iPSM-ES and the benchmark models. In the 500-sample evaluation, iPSM-CS achieved r = 0.79, RMSE = 2.44 kg·m⁻², and MAE = 1.93 kg·m⁻², compared with r = 0.55, RMSE = 3.34 kg·m⁻², and MAE = 2.52 kg·m⁻² for iPSM-ES. Top-1 CS and Top-1 ES were significantly negatively associated with relative prediction error in the independent test set, while CS provided a clear stratification of errors across the predefined similarity intervals. The results support the use of dual-space similarity for spatial prediction, although its transferability requires evaluation across additional regions, attributes, and spatial validation designs.