# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 5 Discussion  
### Change Detection in \(\sigma^{\circ}\) SAR Backscattering  
In this study, a new methodology is presented for detecting irrigation events at plot scale using the change detection in the SAR backscattering signal. The first important fact is that the change of the surface soil moisture due to irrigation events (artificial application of water) between two SAR dates may cause an increase in the SAR backscattering coefficient between these two dates. However, this assumption remains limited with the changes in SAR backscattering signal due to a rainfall event or vegetation development. Therefore, the separation between the changes in SAR backscattering signal due to irrigation events from similar changes, which could be caused by rainfall or vegetation development, was challenging.  
Figure 12: Distribution of the irrigation points detected over irrigated plots in Tarbes, France for (**a**) morning SAR acquisition, (**b**) evening SAR acquisition, (**c**) intersection between morning and evening acquisitions and (**d**) combination between morning and evening acquisitions.  
To remove the uncertainty with rainfall events, the method proposes using the SAR backscattering signal at basin scale (10 km \(\times\) 10 km) obtained from bare soil plots with low vegetation cover as a descriptor of rainfall events. The assumption stipulates that if the mean SAR signal within 10 km \(\times\) 10 km grid cell increased between two consecutive dates then a rainfall event took place and thus irrigation, if it had occurred, could not be detected. This dependency between rainfall and \(\sigma_{G}^{0}\) was presented in Figure 4. For this reason, we judge that any increase in the SAR signal at 10 km grid scale by more than 1 dB is linked to rainfall events. In addition, the surface soil moisture estimation performed at grid scale (\(SSM_{G}\)) was used to enhance the detection of rainfall events. Thus, a threshold of 20 vol.% for \(SSM_{G}\) was proposed to describe humid soil conditions at basin scale which is probably linked to a rainfall event that occurred couple of days before the SAR acquisition. The combination of these two conditions at grid scale allowed a good separation between rainfall and irrigation episodes occurring at each SAR acquisition.  
The second main contributor in the SAR backscattering signal at plot scale was the development of the vegetation cover. This development could cause an increase in the SAR backscattering signal at plot scale without any change in the surface soil moisture content and thus without any irrigation or rainfall events. In this study, a descriptor of the vegetation growth is suggested. This descriptor \((S)\) is a Gaussian-smoothing filter applied to the SAR temporal series. The \(S\) values allow describing the vegetation growth pattern at the plot scale and thus permit the separation of irrigation events from vegetation growth events. This contribution of the smoothed-Gaussian is shown in Figure 6. For the soya plot (P2) of Figure 6, the vegetation descriptor \((S)\) helped eliminate two points considered as vegetation development points. Another vegetation development filter was proposed for cereal plots in order to remove the increase in the SAR backscattering signal due to the transition between the heading and the soft dough phenology phases. The importance of this filter was demonstrated in Figure 7c for a non-irrigated winter wheat plot. However, the date limits present in this filter (mid-March until May) could be adjusted for other geographical contexts in order to follow the cereal growth cycle where cereals could be cultivated in other months.