# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 3 Methodology
### Reducing Vegetation Contribution  
Over vegetated areas, \(\sigma^{0}_{P}\) is not only affected by the soil water content but also by the characteristics of the vegetation cover. Indeed, for certain agricultural crops, the SAR backscattering signal is attenuated by the existing vegetation cover [32; 35]. For example, for crop types such as soya, sorghum and sunflower the SAR backscattering signal between two dates could increase due to the development of the vegetation cover. The direct effect of vegetation cover on the SAR signal should be also considered to ensure accurate detection of irrigation events.  
Recently, [PERSON] et al. [36] used a smoothed Gaussian filter on SAR temporal series in order to describe the phenology stages of the vegetation growth over wheat crops. They demonstrated that the smoothed Gaussian could be used to describe the vegetation contribution in the SAR temporal series over wheat plots. Therefore, to minimize the effect of vegetation growth on the radar signal, we propose to smooth the temporal series of the SAR backscattering signal. This smoothing gives the general behavior of the vegetation contribution in the SAR signal. However, in order to ensure the near real-time detection of irrigation, we propose to smooth the signal only using SAR dates existing before the examined SAR date. This means that at each SAR date, a smoothing value \(\sigma^{0}_{smooth}\) is obtained by applying a Gaussian smooth on all points before this date (from \(t_{0}\) to \(t_{i}\)). In this study, the multidimensional Gaussian smooth has been used and the standard deviation for the used Gaussian kernel was set equal to 4. Finally, we obtain the index \(S\) considered as a vegetation descriptor where:  
\[S=\sigma^{0}_{Pi_{i}}-\sigma^{0}_{smooth} \tag{3}\]  
Therefore, if \(\sigma^{0}_{Pi_{i}}\) is less than \(\sigma^{0}_{smooth}\) (i.e., \(S<0\)), then the point should not be further considered for irrigation detection. This smoothing allows us to determine a vegetation indicator capable of reducing the vegetation effects in the SAR signal (at each date) without losing possible irrigation events.  
Another vegetation contribution filter has been suggested particularly for winter cereals usually grown in the period between September and July of each year (wheat, barley and oats). In their study over wheat crops, [PERSON] et al. [36] showed that the C-band SAR backscattering signal in VV polarization decreases gradually between the germination phase occurring by the beginning of January and the heading phase occurring between mid-March and mid-April. In the heading phase, the C-band SAR backscattering signal attains extremely low values due to extreme vegetation attenuation (less than \(-15\) dB for incidence angle between \(32^{\circ}\) and \(34^{\circ}\)). The \(\sigma^{0}_{P}\) gradually increases then, between mid-April and the end of May, when cereals move from the heading phase to the soft dough phase. This increase in the C-band SAR signal is mainly due to the change in the phenology phase of the cereals and is not linked to irrigation episodes. To reduce this ambiguity, we propose to eliminate detected irrigation points between mid-April and the end of May if and only if \(\sigma^{0}_{P}\) attains extremely low value (less than \(-15\) dB) between mid-March and mid-April. In this way, we can ensure that these detected points are most probably a phenology change of cereals where \(\sigma^{0}_{P}\) increases from extremely low values between March and April (\(<-15\) dB) to higher values in May. Practically, for any plot, if a point is detected between mid-April and the end of May, we find the minimum of all \(\sigma^{0}_{P}\) values (denoted by _MIN_) acquired between mid-March and mid-April. If the _MIN_ value is less than \(-15\) dB (most probably heading phase of cereals), then we eliminate the detected point.