# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 3 Methodology
### Surface Soil Moisture Filter  
As discussed in Section 3.2, irrigation activities obviously cause an increase in the SSM values at plot scale. Thus, an important factor that can describe the presence or absence of irrigation events is the SSM value. We consider here that low soil moisture values are not correlated with an occurring irrigation event since irrigation must increase the SSM values especially over plots with small vegetation cover. However, the integration of any proposed SSM filter requires proper estimation of SSM values. Recently, an algorithm using the neural network (NN) technique was developed by [PERSON] et al. (2018) to estimate SSM values at plot scale over agricultural areas with vegetation cover. In their study, SSM values were estimated with an accuracy of 5 vol.% (volumetric water content in percent = 0.05 cm\({}^{3}\)cm\({}^{-3}\)) over agricultural areas which is considered a pleasant accuracy for any hydrological application at plot scale. In addition, the NN developed by [PERSON] et al. (2018) has showed the most accurate SSM estimations when evaluated against several SSM products such as the Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP) ([PERSON] et al., 2018) and Copernicus Surface Soil Moisture (C-SSM) ([PERSON] et al., 2018).  
In this study, the NN developed by [PERSON] et al. (2018) has been used to estimate SSM values at plot scale. The NN requires as input data: SAR signal in VV polarization \(\left(\sigma^{0}\right)\), the SAR incidence angle \(\left(\theta\right)\), and an NDVI value. As a result, SSM values were estimated at each plot and for each SAR date denoted by \(\left(SSM_{P}\right)\). However, in another study, [PERSON] et al. (2018) recommend that the SSM estimation could be limited in the presence of very dense vegetation cover due to the high vegetation attenuation on SAR signal. Thus, \(SSM_{P}\) has only been considered for NDVI values less than 0.5. Finally, an additional filter is applied in which irrigation points have been restricted to SSM estimation \(\geq\)15 vol.% when the NDVI value is less than 0.5. For NDVI values greater than 0.5, the SSM filter was not applied.  
SSM values at plot scale were also used to help confirm whether a point is an irrigation point or not in the case of slight change in \(\Delta VV_{P}\left(-0.5\text{ dB}\leq\Delta VV_{P}\leq 1\text{ dB}\right)\). As presented in Section 3.2, the stability or slight decrease of the SAR signal at plot scale between \(t_{i}\) and \(t_{i-1}\) could be interpreted as an irrigation event if and only if the \(\sigma^{0}\) at time \(t_{i-1}\) already attains high values (due to irrigation or rainfall). To ensure this situation, we say that at time \(t_{i-1}\), SSM estimation should be greater than or equals to 20 vol.% in order to guarantee that humid soil conditions at time \(t_{i-1}\) have continued to time \(t_{i}\).  
Over the grid scale, the SSM estimation for the \(\sigma^{0}_{G}\) obtained for bare soil plots with low vegetation cover (NDVI <0.4) at grid scale (Section 3.3) was also performed at each grid cell and for each SAR date \(\left(SSM_{G}\right)\). This estimation presents the surface water content over bare soil plots on the basin scale. In fact, we assume that high soil moisture values at grid scale (10 km \(\times\) 10 km) are more likely to be linked to possible rainfall events rather than irrigation events (humid soil conditions at grid scale). For this reason, we propose to eliminate all the points where the SSM estimation at grid scale is greater than 20 vol.%. For the grid scale, the effect of vegetation attenuation on SSM estimations (high NDVI values) does not exist since the \(\sigma^{0}_{G}\)-values were only calculated for bare soil plots with low vegetation cover.