Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts

Abstract

Spatiotemporal modeling is an innovative way of predicting soil moisture and has promising applications that support sustainable forest operations. One such application is the prediction of rutting, since rutting can cause severe damage to forest soils and ecological functions. In this work, we used ERA5-Land soil moisture retrievals and several topographic indices to model variations in the in situ soil water content by means of a random forest model. We then correlated the predicted soil moisture with rut depth from different trials. Our spatiotemporal modeling approach successfully predicted soil moisture with Kendall’s rank correlation coefficient of 0.62 (R2 of 64 %). The final model included the spatial depth-to-water index, topographic wetness index, stream power index, as well as temporal components such as month and season, and ERA5-Land soil moisture retrievals. These retrievals were shown to be the most important predictor in the model, indicating a large temporal variation. The prediction of rut depth was also successful, resulting in Kendall’s correlation coefficient of 0.61. Our results demonstrate that by using data from several sources, we can accurately predict soil moisture and use this information to predict rut depth. This has practical applications in reducing the impact of heavy machinery on forest soils and avoiding wet areas during forest operations.