# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
## 3 Results
### Validation and Clay Fraction Mapping  
A three-fold cross-validation procedure is used to obtain predictions from the different classifiers (SVM and RF) and the different indicators. Table 5 lists the results obtained from the confusion matrices with these simulations. Low accuracies are obtained with optical data in the B11 and B12 bands, with an OA of 0.41 and an Kc of 0.15. On the other hand, the soil moisture indicator produces good results, with an OA as high as 0.65 obtained with the RF algorithm. The results obtained with optical data (bands B11 and B12) are clearly enhanced when they are combined with the soil moisture indicator, and the RF is found to be a more robust classifier than the SVM, for all of the simulations carried out during this study. The clay maps generated by both algorithms are shown in Figure 10. All non-agricultural areas (urban areas, reliefs, water bodies) have been masked in these maps, using a GIS (Geographic Information Systems) shape file. Three classes of clay content are presented according to the following partition: Class 1: clay content between 15% and 30%, Class 2: clay content between 30% and 45%, and Class 3: clay content between 45% and 60%. These two maps are very similar in appearance, showing the same distribution of soil texture across the region. The third class, representing the highest value of clay content, can be seen to dominate the entire area, and to be strongly present at the center of this site, where irrigated and winter/summer vegetables are cultivated. Sandy soils are present mainly in the south-western portion of the site, where olive orchards are widely cultivated. Due to a difficulty of estimation soil fraction, the results of this study illustrate the potential advantages of using combined optical and radar data for soil texture monitoring. These results are within expected ranges of variability compared to those obtained using optical data. In the context of our study site, the use of optical data only (corresponding to the texture analysis technique generally reported in the literature) leads to a lower accuracy in the estimation of clay content. This outcome could be related initially to weather conditions reducing the performance of optical data. Local tillage conditions, with frequent residues occurring after the cereal harvest, precisely during early summer season (June-July), is also affecting reflectance. For instance, [PERSON] et al. [27] have found similarly modest correlations between observed and predicted clay content, using PLSR model, with an R\({}^{2}\) between 0.39 and 0.5. In [PERSON] et al. [55], clay spectral indices entail a decrease of accuracy according to soil moisture classes (a coefficient of correlation less than 0.65). The investigation on the wavelengths most correlated with clay content at different moisture levels, showed that the most suitable spectral features are strongly affected by soil moisture. This showed that the knowledge of soil moisture is essential for a better estimation of clay content. In the same investigation, others studies showed that multispectral data provide modest performances of clay estimations, controlled by others soil's parameters (soil roughness and crop residues) and due to atmospheric effects [21; 56; 57]. Radar data thus appears to contribute an improvement in overall accuracy, although scientific studies are very limited in this area. In practice, soil moisture conditions can be derived from radar data independently of local weather conditions, and even in the presence of a vegetation cover. Therefore, soil moisture estimation could hamper the estimation of soil component such as clay content from remote sensing data, reducing the strength of the relevant spectral absorption features. This capability broadens the potential for characterizing soil texture in areas where optical satellite sensors are unable to observe the ground, due to the presence of a vegetation cover.