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Potential of Sentinel-2 images for estimating of soil resistivity over agricultural fields
* 1 , 2, 3 , 3 , 2
1  Centre d’Études de la BIOsphère (CESBIO), Université de Toulouse, CNES/CNRS/INRAe/IRD/UT3, Toulouse, France
2  Centre d’Études de la BIOsphère (CESBIO), Université de Toulouse, CNES/CNRS/INRA/IRD/UPS, Toulouse, France
3  IUT Paul Sabatier, 24 rue d’Embaquès, Auch, France


Mapping of soil properties, especially electrical resistivity measurements that provide integrative information of the physical and chemical properties at different depths, is a key issue in precision farming. Effective management of agricultural fields by delineating areas with comparable status passes through a description of the soil variability at the intra-field spatial scale. In this context, the on-going Sentinel-2 satellite mission provides regular surface observations at a decametric resolution, useful for the monitoring of sub-surface parameters. The aim of this study is thus to assess the possibilities of the VNIR (Visible and Near InfraRed) and SWIR (Short Wavelength InfraRed) satellite data for retrieving intra-plot patterns consistent with soil resistivity measurements. Over a study site located in southwestern France, intra-field soil resistivity measurements were collected at three depths (representing the following soil layers: 0-50, 0-100 and 0-170), by a towed automatic resistivity profiler and equipped with GPS system, together with Sentinel-2 satellite images. The methodology aims at: (i) estimating the electrical resistivity at different depths using a statistical algorithm (optical reflectances constituting the input variables of random forest, alone or in combination with parameters derived from a digital elevation model), and (ii) analyzing the contribution of input variables on the targeted electrical resistivity. The preliminary results only based on optical reflectances and obtained on one monitored field show interesting level of accuracy for the 0-50 and 0-100 layers (with R² of 0.69 and 0.59, and a relative RMSE of 18% and 16% respectively), the performances being lower for the 0-150 layer (R² of 0.39, relative RMSE of 20%). The combined used of optical reflectances with parameters derived from the digital elevation model slightly improves the performances whatever the considered layer (R² of 0.74, 0.64 and 0.47 for the 0-50, 0-100 and 0-150 layers, respectively).

Keywords: Soil electrical resistivity; Sentinel-2; optical reflectance; mapping soil variability; random forest