# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
## 5 Conclusions  
The aim of this study is to develop an approach for topsoil clay content estimations, based on the interpretation of multi-sensor satellite data. Our results show that remote sensing is an efficient tool,which can be used to generate soil texture maps at the field scale. This study was carried out over the Kairouan Plain, in central Tunisia. Satellite acquisitions were accompanied by field measurements, from which each soil sample was assigned to a predefined class, according to its measured clay content. Three main classes are defined, with clay content values ranging between 15% and 60%.  
The classical RF and SVM classifiers are used with different indicators, derived from the Sentinel constellations (S-1 and S-2). Simulations were carried out with several datasets, thus allowing three-fold cross-validation to be applied. Several studies have shown that optical data have considerable potential for the retrieval of soil texture. We thus tested several indices and SWIR bands for their ability to estimate clay content. As the results were relatively poor, with an OA below 0.4, a second simulation was performed by testing various soil moisture products, which have a significant correlation with clay content, as input data for the classification process. This indicator improves the classification performance. The original aspect of this study lies in the fact that soil moisture products derived from the WCM could be used to estimate clay content, even in areas covered by vegetation, since this approach has been validated on several sites under different climatic conditions.  
The behaviors of the SVM and RF have been analyzed, showing that both algorithms can be used as robust classifiers for clay content estimations, even when relatively small volumes of training data are used. The RF is more accurate than the SVM. A small drawback of this algorithm is that it is difficult to parameterize.  
Future studies should focus on collecting a larger number (at least 100) of soil samples, to improve the results, and allow a much finer classification to be achieved. These techniques could also be tested over other areas, characterized by different climatic conditions, and could also be used to produce texture maps of sand content. It is important to mention the critical impact of irrigation on soil color: meaningful optical observations can be made only at certain times of the year, to avoid the influence of irrigation on soil moisture. Another limitation of the use of optical data arises from the prevailing atmospheric conditions, which can restrict the number of useful images taken over a given area of interest. The fact that the quality and performance of optical data can be perturbed by several different factors highlights the importance of SAR product use.  
[PERSON] and [PERSON] designed and developed the experimental procedures; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] analyzed the data; [PERSON]-[PERSON], [PERSON] and [PERSON] contributed to the materials and analysis tools; [PERSON] and [PERSON] wrote the paper.  
This study was funded by the CHAAMS (ERANET-MED 03-62 CHAAMS) and TOSCA/CNES projects.  
We extend our warm thanks to the technical teams at the IRD and INAT (Institut National Agromomique de Tunisie), for their invaluable collaboration and support during the ground-truth measurement campaigns and data processing activities.  
The authors declare no conflict of interest.