Sentinel-1 & Sentinel-2 for field scale soil moisture retrieval at continental scale

Currently, within the Copernicus Global Land Service (https://land.copernicus.eu/global/products), daily Soil Water Index (SWI) information is distributed as gridded product with a resolution of approximately 25 km (Wagner et al., 1999). A surface soil moisture (SSM) product at a resolution equal or lower than 1 km is also under development. Nevertheless, a number of applications, e.g. agriculture, require SSM information at resolutions that correspond to the scale at which water is being used, e.g., "fields scale". For instance, in irrigated areas, field-to-field variability in SSM, also during early stages of the growing season when fields are still under conditions of low vegetation, is crucial information for improving water management. For this reason, in the context of the SENSAGRI H2020 project [SENSAGRI.eu], an evolution of the Sentinel-1 (S1) SSM product at 1 km, developed in the contest of the ESA SEOM Exploit-S-1 study [http://seom.esa.int/page_project034.php], has been implemented. The ESA product exploits a change detection approach that requires a short revisit time of SAR observations [Balenzano et al., 2013] and, for this reason, it is referred to as a short-term change detection (STCD) approach. The strength of the algorithm is its conceptual simplicity and robustness. On the other hand, STCD is prone to the occurrence of abrupt changes of the vegetation and/or soil roughness status that can be wrongly interpreted as SSM changes. Such changes, may have a limited impact at a resolution equal or above ~1 km, but they usually produce significant errors at "field scale" resolutions. The evolution of the ESA product consists of integrating S1 and Sentinel-2 (S2) data in order to improve to "field scale" the resolution of the SSM product. More precisely, S2 NDVI together with S1 is used to mask abrupt changes of the vegetation and/or soil roughness status that may affect SSM estimates at "field scale". If NDVI is not available (e.g. cloud cover), then the ratio of VH/VV is adopted as a proxy. The SENSAGRI product transforms dense time series of N co-registered S-1 VV & VH & S-2 NDVI images at 40m pixel size into N-SSM maps. The final step in the SSM retrieval is to apply a low pass filter in order to improve the radiometric accuracy of the final product. In the SENSAGRI product, an adaptive strategy is implemented so that the final product is characterized by a fairly high spatial resolution (~100m) over agricultural areas, where SSM averages and standard deviations at field scale are performed using the European LPIS (Land Parcel Information System) data base for parcel borders, wherever available. Over the areas where parcel borders are not available SSM is averaged at a resolution of 1 km and no S2 data are required. The paper presents examples of the developed S1 & S2 SSM product over various sites in Europe, reports on the results of the validation activity and provides an outlook about possible applications at regional scale. Wagner, W., Lemoine, G. & Rott, H. (1999). A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data. Remote Sensing of Environment. vol.70 (2). pp.191-207. Balenzano, A., Satalino, G., Lovergine, F., Rinaldi, M., Iacobellis, V., Mastronardi, N. & Mattia, F. (2013). On the use of temporal series of L-and X-band SAR data for soil moisture retrieval. Capitanata plain case study. European Journal of Remote Sensing. vol.46 (1). pp.721-737.