Field scale soil moisture retrieval and soil tillage change detection from Sentinel-1 for agricultural monitoring

Agriculture monitoring is of a huge importance to address potential food shortages and guide financial decision making. The capabilities of Copernicus Sentinel-1 (S-1) with short repeat frequency, large coverage and continuous provision of consistent datasets, provide an unprecedented contribution to agricultural monitoring, for issues of global concern such as food security. A number of agriculture applications from drought identification to irrigation scheduling, crop disease management, yield forecast would benefit from surface soil moisture (SSM) information at resolutions that correspond to the scale at which water is being used, e.g., "fields scale" [Tebbs et al., 2016]. On the other hand, monitoring of tillage practices has gain interest in agro-environmental sciences as tillage operations affect land processes such as soil erosion, surface evaporation, run-off and infiltration, nutrient uptake, carbon sequestration and loss of biodiversity [Foley et al., 2011]. Spaceborne Synthetic Aperture Radars (SARs) are the most appropriate systems to retrieve SSM at a high spatial resolution (e.g. field scale). Nevertheless SAR data are also sensitive to surface roughness and its change due to tillage practice, as for instance deep ploughing [Davidson et al., 2000]. In fact, surface roughness has been traditionally considered an important error source for the estimation of the SSM. In the context of the SENSAGRI H2020 project [SENSAGRI.eu], an evolution of the S1 SSM product at 1 km, developed within 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 evolution of the ESA product consists of integrating S-1 and Sentinel-2 (S-2) data in order to improve to "field scale" the resolution of the SSM product. 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. More precisely, S-2 NDVI together with S-1 is used to mask abrupt changes of the vegetation and/or soil roughness status that may affect SSM estimates at "field scale". In particular, the masking of tillage changes is based on a multiscale thresholding approach applied to the temporal change of cross-polarized (VH) backscatter in order to single out VH changes due to agricultural practices only [Satalino et al., 2018]. The method is applied to bare or scarcely vegetated fields, which are identified by thresholding the Normalized Difference Vegetation Index (NDVI) extracted from S-2 data. In addition, the S-1 VH/VV ratio is used as proxy of NDVI in case of cloud cover [e.g. Veloso et al., 2017]. In fact, what is considered an error source for SSM retrieval turns to be a useful information for tillage change detection. In this sense, the tillage change mask can be regarded as a SSM side-product. After the tillage change masking, the final step is to average at field scale the 40m pixel SSM estimates over the agricultural areas using the European LPIS (Land Parcel Information System) data base for parcel borders, wherever available. The advantage is twofold: the spatial average i) improves the radiometric resolution of the SSM product and ii) preserves as much as possible the spatial information (i.e. the field boundaries) relevant for many agricultural applications. Over the areas where parcel borders are not available SSM is averaged at a resolution of 1 km and no S-2 data are required. The paper presents examples of the developed S-1 & S-2 SSM and tillage change products over various sites in Europe, reports on the results of the validation activity and provides an outlook about possible applications at regional scale. Acknowledgements: The research leading to these results has received funding from the Scientific Exploitation of Operational Missions (SEOM) program of the European Space Agency, through the project "Exploitation of S-1 for Surface Soil Moisture Retrieval at High Resolution (Exploit-S-1)" (contract ESA/AO/1-8306/15/I-NB) and from the European Union's Horizon 2020 Research and Innovation Programme, under Grant Agreement nº 730074 through the project "Sentinels Synergy for Agriculture (Sensagri)" References: Tebbs, E., Gerard, F., Petrie, A., De Witte, E. (2016). Emerging and Potential Future Applications of Satellite-Based Soil Moisture Products. In: Satellite Soil Moisture Retrievals: Techniques & Applications, G.P. Petropoulos, P. Srivastava, Y. Kerr (eds.), Elsevier, Amsterdam, Chapter 19, 379-400. Foley, J.A., Ramankutty, N., Brauman, K.A., Cassidy, E.S., Gerber, J.S., Johnston, M., Mueller, N.D., O'Connell, C., Ray, D.K., West, P.C., Balzer, C., Bennett, E.M., Carpenter, S.R., Hill, J., Monfreda, C., Polasky, S., Rockstro¨m, J., Sheehan, J., Siebert, S., Tilman, D., Zaks, D.P.M. (2011). Solutions for a cultivated planet", Nature 478, 337-342. Davidson, M. W., Le Toan, T., Mattia, F., Satalino, G., Manninen, T., & Borgeaud, M. (2000). On the characterization of agricultural soil roughness for radar remote sensing studies. IEEE Transactions on Geoscience and Remote Sensing, 38(2), 630-640. 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. Satalino, G., Mattia, F., Balenzano, A., Lovergine, P.L., Rinaldi M., De Santis A. P., Ruggieri S., Nafría García D. A., Paredes Gómez V., Ceschia E., Planells M., Le Toan T., Ruiz A. and Moreno J.F. (2018) Sentinel-1 and Sentinel-2 data for soil tillage change detection. 2018 IGARSS proceedings, July, 22-27, Valencia (Spain). Veloso, A., Mermoz, S., Bouvet, A., Le Toan, T., Planells, M., Dejoux, J. F., & Ceschia, E. (2017). Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sensing of Environment, 199, 415-426.