From Point to Pixel Scale: An Upscaling Approach for In Situ Soil Moisture Measurements

Abstract

Core Ideas Soil moisture can be remotely sensed with active radar. A new method allows spatial upscaling of in situ and remotely sensed data. A machine learning based method is used for soil moisture retrieval. Given the high spatial variability of soil moisture content (SMC), direct comparison and integration of observations from different sources and measurement scales is becoming a major challenge. We have developed a spatial upscaling method for SMC that enables the direct combination of in situ measurements and remotely sensed data. The approach is based on the fact that spatial soil moisture patterns are related to ancillary features like topography, land cover, and soil type. This study used in situ data from a well‐equipped research site in the northern Italian Alps. One of the main goals was to enable the use of these data for the validation of the NASA Soil Moisture Active Passive (SMAP) products. Dealing with medium‐ to coarse‐resolution satellite imagery, especially in mountain areas, requires compensating for different measurement scales. The study approach was assessed based on Envisat advanced synthetic aperture radar (ASAR) data, which were resampled to reproduce the spatial scale of the SMAP data. Results show that the representativeness of in situ data, with respect to the 3‐ by 3‐km SMAP pixel scale, can be improved significantly—direct correlation between SMC and satellite backscatter was improved from R = 0.05 to 0.28; furthermore, the error of the estimated SMC was improved from RMSE = 0.12 to 0.03 m3 m−3. This leads to more accurate reference data, which can help to improve the retrieval of SMC from remotely sensed imagery.