High-resolution soil moisture mapping in northern boreal forests using SMAP data and downscaling techniques

Abstract. Soil moisture plays an important part in predicting different forest-related phenomena, such as tree growth or forest fire risk. As these phenomena influence the carbon storage capacity of boreal forest ecosystems, it is crucial to provide soil moisture information at high temporal and spatial scales. Current satellite-based soil moisture products often have high temporal resolution at the expense of spatial resolution. Therefore, we developed a machine-learning-based model to estimate soil moisture at high temporal and high spatial resolution over boreal forested areas for the annual time period from May to October. The basis data of the model is the enhanced 9 km spatial resolution soil moisture data from the Soil Moisture Active Passive (SMAP) mission. Additionally, soil and vegetation properties, reanalysis-based parameters, and measured in situ soil moisture data are used to guide the model construction process. The analysis of the developed model shows that the model retains the temporal and large-scale spatial variability of SMAP soil moisture. Furthermore, comparisons with the independent in situ soil moisture data show that the soil moisture values predicted by the developed model have a better agreement with in situ values than SMAP soil moisture, as RMSE decreases from 0.097 m³/m³ to 0.065 m³/m³, and correlation increases from 0.30 to 0.52 over forest sites. Therefore, this machine-learning-based model can be used to predict high-resolution soil moisture over boreal forested areas.