Exploitation of Sentinel-1 & Sentinel-2 time series for the detection of soil tillage change

In Europe, conventional tillage involves approximately 66% of the arable land [https://ec.europa.eu/eurostat]. This practice has negative impact on soil surfaces as, for instance, it enhances losses of water, nitrogenous compounds and phosphorus, and it also increases surface runoff and soil erosion (e.g. Foley et al., 2011). For these reasons, the Common Agricultural Policy (CAP) promotes the Conservative Agriculture (CA), e.g. zero tillage practices, that can prevent and mitigate soil degradation processes [https://ec.europa.eu/agriculture/envir/soil_en]. It is, however, also important to set up appropriate procedures to periodically assess the level of adoption of CA at European scale. Copernicus Sentinel-1 (S-1) and Sentinel-2 (S-2) data, systematically acquired at high spatial and temporal resolution, can enable cost effective tools to monitor tillage/no-tillage practices at large scale. Indeed, S-1 SAR data are particularly sensitive to surface roughness and its change due to, for instance, ploughing, arrowing, etc. [e.g. Mattia et al., 1997; Davidson et al., 2000]; while S-2 optical data are suitable to identify bare or scarcely vegetated soils [e.g. Serbin et al., 2009], which are exposed to tillage operations. In the context of the SENSAGRI H2020 project [SENSAGRI.eu], an algorithm to retrieve from S-1 and S-2 data time series of tillage change maps at high resolution (e.g. ?100m) is proposed. The algorithm is based on a multiscale approach applied to the temporal change of S-1 cross-polarized (VH) backscatter in order to single out changes due to agricultural practices only. The method is applied to bare or scarcely vegetated fields, which are identified through S-2 Normalized Difference Vegetation Index (NDVI) [Satalino et al., 2018]. 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]. The paper presents algorithm and its validation over the SENSAGRI European sites in Italy, Spain and France. Ground data collected in 2017 and 2018 are used to assess the performance of the monitoring tool. Examples of tillage change maps at regional scale are presented and discussed. References: 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. 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., "Solutions for a cultivated planet", Nature 478, 337-342, 2011. Mattia, F., Le Toan, T., Souyris, J. C., De Carolis, C., Floury, N., Posa, F., & Pasquariello, N. G. (1997). The effect of surface roughness on multifrequency polarimetric SAR data. IEEE transactions on geoscience and remote sensing, 35(4), 954-966. Satalino, G. A. Balenzano, F. Mattia, and M. W. J. Davidson, "C-band SAR Data for Mapping Crops Dominated by Surface or Volume Scattering", Geosci. and Remote Sensing Letters, Vol. 11, Issue 2, pp. 384-388, Feb. 2014. Serbin, G., Daughtry, C.S.T., Hunt Jr., E.R., Brown, D.J., McCarty, G.W., "Effect of soil spectral properties on remote sensing of crop residue cover", Soil Science Society of America Journal 73, 1545-1558, 2009. 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.