# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 5 Discussion
### Strengths, Limitations and Future Directions  
The results of the study demonstrate that irrigation events could be detected using the Sentinel-1 satellite data with 6 days temporal resolution. Using the proposed method, the spatial distribution of the plots that encountered an irrigation event could be obtained at each available SAR acquisition. Currently, the Sentinel-1 satellite is the only operational satellite that provides SAR data with high  
Figure 13: Percentage of plots with no detected irrigation points a function of the value of \(NDVI_{li}\) used for the optical filter in Catalonia, Spain for irrigated (blue) and non-irrigated (orange) plots using (**a**) morning acquisition mode and (**b**) evening acquisition mode. revisit time (6 days). However, this revisit time could still affect the detection of irrigation events. The first limitation of the proposed method is the effect of the time lag between the irrigation episode and the satellite passage. In fact, the detection of irrigation can become difficult if the irrigation event takes place more than three days before the SAR acquisition. [PERSON] et al. [21] showed that irrigation could be detected if the irrigation took place three days and less before the radar acquisition. In their study using X-band SAR data, they showed that more than three days after the irrigation event, the surface soil moisture could attain the same value as that before the irrigation due to evaporation. Thus, a SAR acquisition more than three days after the irrigation event could not show any change in the surface soil moisture and the irrigation could not be detected. However, an analysis of the detected and non-detected irrigation events over Montpellier site using the C-band SAR data reveals that detection of irrigation events is not necessarily limited to the three-day time lag. For example, some detected irrigation points were found to be occurring four days before the SAR acquisition whereas few points were not detected even two days after the SAR acquisition. Thus, additional information such as the type of irrigation, the quantity of water, the evaporation rate and the plant water needs could be used to confirm the presence or absence of such irrigation events. In general, to overcome the limitation of the time lag between the SAR date and irrigation date, a dense SAR temporal series is required. Indeed, within the overlapping area of the different orbits of the Sentinel-1, four images (two morning and two evening images) could be obtained each 6 days. Thus for some sites, we could obtain up to 20 images each month (10 morning and 10 evening images) which could help easily detect all the irrigation events. This configuration mainly depends on the surface area of the studied site in order to be covered by the overlapping area of different Sentinel-1 overpasses. The dense temporal series of SAR acquisitions (20 images per month) could also help increase the number of possibly detectable irrigation events. The number of possibly detected irrigation events mainly depends on the availability of the SAR images. As shown in the results over Montpellier (Section 4.2), 69% of the irrigation events could be possibly detected by SAR data using the available SAR configuration over the study area (10 images each month). Thus, as the number of SAR images increases, the chance of detecting all the existing irrigation events increases. Another limitation for detecting irrigation events could be the existence of a very well developed vegetation cover. In fact, the value of \(\Delta VV_{P}\) over high vegetation cover could be lower than that for bare soil [18]. However, this difference in \(\Delta VV_{P}\) between bare soil and vegetated soil is a difficult aspect to quantify but can cause some uncertainty. The strength of the method resides in the near real-time detection of irrigation episodes. Despite the post-processing step required in the NDVI optical filter, the method could still be operationally applied in near real-time.