Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals

The work presented looks at methods to model field measured spatio-temporal variations of soil moisture content (SMC, [%vol]) – a crucial factor for soil strength and thus trafficability. We incorporated large-scaled maps of soil characteristics, high-resolution topographic information – depth-to-water (DTW) and topographic wetness index – and openly available temporal soil moisture retrievals provided by the NASA Soil Moisture Active Passive mission. Time-series measurements of SMC were captured at six study sites across Europe. These data were then used to develop linear models, a generalized additive model, and the machine learning algorithms Random Forest (RF) and eXtreme Gradient Boosting (XGB). The models were trained on a randomly selected 10% subset of the dataset.