## 6 Toward Operational Soil Moisture Mapping over Agricultural Areas  
From the perspective of operational soil moisture mapping, an operational approach for the estimation of soil moisture is developed in this study. This approach is based on the synergic use of the new free and open accesses radar (Sentinel-1) and optical (Sentinel-2) sensors. The developed methodology was applied for soil surface mapping over the French Occitanie region (area of 72,724 km\({}^{2}\)) located in southern France. The Occitanie region is totally covered by four S1A image footprints and five S1B image footprints. The objective is to operationally map the soil moisture over agricultural areas for each homogeneous spatial unit for each Sentinel-1 acquisition date: sub-plot, plot or set of plots defined by pixels having homogeneous NDVI values with a variation of \(\pm\)0.1. The data used for the _mv_ mapping are as follows:  
* A total of 186 calibrated Sentinel-1 SAR images covering the entire Occitanie region between September 2016 and May 2017.  
* Five NDVII mosaics derived from Sentinel-2 images and covering the entire Occitanie region between September 2016 and May 2017. The first NDVI mosaic reflects the vegetation conditions for the period of September and October (0.3 < NDVI < 0.6). The second one represents the vegetation conditions in November and December (0.3 < NDVI < 0.6). The third mosaic characterizes the vegetation conditions in January and February (0.3 < NDVI < 0.7). The fourth one represents the vegetation in March (0.4 < NDVI < 0.8). Finally, the fifth NDVI mosaic characterizes the vegetation conditions in April and May (0.6 < NDVI < 0.9).
* A land cover map was used to extract the agricultural areas ([[https://www.theia-land.fr](https://www.theia-land.fr)]([https://www.theia-land.fr](https://www.theia-land.fr))). This map is a thematic raster file with values between 11 and 222, where each value corresponds to a type of land cover [64].  
To operationally derive soil moisture maps, first, the agricultural areas for the Occitanie region are extracted from the land cover map. Next, for each period, the corresponding NDVI mosaic is used to partition these agricultural areas into homogeneous segments (within-plot scale) by means of segmentation algorithm called Mean-Shift [65]. Then, for each SAR acquisition date, the mean incidence angle, mean backscattering coefficients in the VV polarization, and the mean of the NDVI are computed for each homogenous segment. Finally, the developed neural networks with VV alone using a priori information on the _mv_ (NN1_dry, NN1_wet) are applied (results from the synthetic validation and real databases showed that the use of VV alone is better than the use of VH and provides an accuracy similar to the use of VV and VH together). Rainfall information from available and free in situ sensors are used to define the adequate neural network to apply (dry to slightly wet soil conditions or very wet soil conditions).  
Figure 10 shows three examples for a portion of the soil moisture map derived from S1 images acquired on 14 March, 20 March and 1 April 2017. On 14 March, the soil was moderately wet (low precipitation between 12 and 13 March), on 20 March the soil was dry (no precipitation for 7 days), and on 1 April, the soil was extremely wet due to heavy rain that occurred during one day and up to a few hours before SAR acquisition (cumulative rainfall of about 12 mm). Figure 10 shows that the soil moisture values on 14 March reach a maximum value of 20 vol %. On 20 March, the soil moisture values vary mostly between 5 and 15 vol %. Finally, the map of 1 April shows very wet soil, with moisture between 25 and 35 vol %.  
Figure 10: _Cont._