Application of Multi-Source Remote Sensing Data and Machine Learning for Surface Soil Moisture Mapping in Temperate Forests of Central Japan

Abstract

Surface soil moisture (SSM) is a key parameter for land surface hydrological processes. In recent years, satellite remote sensing images have been widely used for SSM estimation, and many methods based on satellite-derived spectral indices have also been used to estimate the SSM content in various climatic conditions and geographic locations. However, achieving an accurate estimation of SSM content at a high spatial resolution remains a challenge. Therefore, improving the precision of SSM estimation through the synergies of multi-source remote sensing data has become imperative, particularly for informing forest management practices. In this study, the integration of multi-source remote sensing data with random forest and support vector machine models was conducted using Google Earth Engine in order to estimate the SSM content and develop SSM maps for temperate forests in central Japan. The synergy of Sentinel-2 and terrain factors, such as elevation, slope, aspect, slope steepness, and valley depth, with the random forest model provided the most suitable approach for SSM estimation, yielding the highest accuracy values (overall accuracy for testing = 91.80%, Kappa = 87.18%, r = 0.98) for the temperate forests of central Japan. This finding provides more valuable information for SSM mapping, which shows promise for precision forestry applications.