MODELLING AND PREDICTION OF PRECIPITATION AND SOIL MOVEMENT BASED ON ADINSAR

Abstract

Evaluating soil movement related to precipitation is needed for geologic and hydrologic applications. In principle, the soil body swells and shrinks depending on soil type, precipitation rate, moisture content, and drainage rate. The precipitations are normally measured at weather stations. Measuring the soil movement by using ground-based sensors and hydrologic models across a large area is costly and time-consuming. Also the weather observations were not fully involved in modelling. A long-term monitoring using remote sensing is a cost-effective alternative. For this purpose, we developed a new approach in this study to model the transformation between precipitation and soil movement. The time-series soil movement over a large area is evaluated by ADInSAR at mm/yr level. As a result, the predictive model can compute the precipitation at a location from its ADInSAR-derived movement, and vice versa. Our test using Sentinel-1 images shows that the prediction accuracy for precipitation is 14 mm (mean error rate 12%) and it amounts 12 mm/yr for soil movement. The accuracies indicate that our modelling is relevant to the reality. We also discuss the influences of different parameters on the modelling. In the future, we will proceed with tests considering other areas of interest, time spans, and image sources. More target points will be analysed in detail. Last but not least, we will work on the applications related to geology and hydrology.