Predicting Soil Temperature from Gridded Air Temperature to Support Sustainable Forest Management in Mountain Regions of Japan

The soil temperature controls soil biological activity, carbon and nutrient cycling, and plant regeneration in mountain forests, so its spatial distribution is needed for the sustainable management of forest soils under a changing climate; however, high-elevation monitoring is costly and scarce. This study evaluated whether the daily mean near-surface (2–7 cm) soil temperature in the snow-free season can be predicted from the air temperature alone, enabling low-cost monitoring from freely available data. A random forest model driven by the current and lagged (0–7 d) daily mean air temperature was trained on snow-free season (May–November) observations from 15 measurement points (11 in a single stand) at five forested sites in Nagano Prefecture, Central Japan (914–2082 m, 2017–2025). The pooled grouped cross-validation RMSE was 1.01–1.05 °C (R2 = 0.955–0.958), and the site-level RMSE was 1.07–1.53 °C when whole sites were withheld. Replacing the observed air temperature with nationwide 1 km gridded data (Agro-Meteorological Grid Square Data) increased the cross-site RMSE only from 1.05 to 1.18 °C. Lapse rate adjustment barely improved the accuracy, indicating that elevation also represents snow, canopy, and radiation effects. Demonstration maps of the snow-free season produced with a 10 m digital elevation model remain unvalidated away from the monitoring points. The approach could provide a resource-efficient basis for soil thermal monitoring to support sustainable forest management and climate change adaptation in mountain regions.