Predicting continuous environmental variables from sparse spatial observations remains challenging because global machine learning models can capture nonlinear relationships but often have limited ability to adapt to local spatial heterogeneity, whereas existing similarity-based methods still rely mainly on weighted-average prediction. This study proposes Pattern Similarity Learning (PSL), which uses pattern similarity as a sp...