# A Review of Irrigation Information Retrievals from Space and Their Utility for Users
## 3. Irrigation Remote Sensing
### Microwave-Based Methods
#### 3.2.1 Mapping Methods  
In recent years, microwave satellite soil moisture products have been introduced as a tool for detecting irrigated areas. One of the first studies was carried out by [PERSON] et al. (2017), who compared the soil moisture distribution of a modeled dataset that does not incorporate irrigation information against various coarse spatial resolution satellite soil moisture products. Assuming that the satellite products do contain the irrigation signal, it can be expected that they will also exhibit wetter soil moisture conditions if irrigation is applied. The authors concluded that, even though promising results for the detection of irrigation were found in some specific areas, the spatial mismatch between model and satellite data as well as the confounding effects of topography, vegetation, frozen soils, and Radio Frequency Interference (RFI) led to substantial uncertainties in most regions. A similar approach has been used in several other studies in Spain (2018), Australia and Morocco (2018), and India (2018). [PERSON] et al. (2018) further demonstrated that not only the spatial signature but also the seasonal timing of irrigation over three vastly irrigated areas in the United States could be identified using the Soil Moisture Active Passive (SMAP) enhanced 9 km product. In contrast, [PERSON] et al. (2018) found that disaggregated satellite soil moisture data obtained via the Disaggregation based on Physical and Theoretical Scale Change algorithm (DISPATCH) (2018) is not sensitive to irrigation when it is applied locally, i.e., at spatial scales smaller than 1 km\({}^{2}\). Nevertheless, [PERSON] et al. (2018) showed the capability of DISPATCH downscaled Soil Moisture and Ocean Salinity (SMOS) and SMAP soil moisture data in detecting and mapping irrigation at 1 km spatial resolution over heavily irrigated portions of the Ebro River basin, in Spain. In this context, SAR data seem to provide a new opportunity for mapping and monitoring irrigation at the agricultural field scale. Indeed, radar measurements are very sensitive to the soil water content due to the sharp increase of the soil dielectric constant associated with soil wetting. [PERSON] et al. (2018) observed this effect on irrigated grassland, showing a 1.4 dB increase of the radar signal observed by the TERRASAR-X sensor (X band), caused by irrigation applied one day before the satellite acquisition. The most important game changer of the recent years, in particular, has been S1, which brought about a strong increase in land use mapping methods in different climate contexts. The S1 satellite constellation (S1-A and S1-B) offers an unprecedented (for non-commercial satellites) data coverage with a time resolution of 6 days and a pixel size of 10 \(\times\) 10 m in two polarizations, VV (vertically transmitted, vertically received) and VH (vertically transmitted, horizontally received), available free of cost. Different approaches have been proposed to map irrigated areas by considering the multi-temporal information from S1 backscatter observations to detect typical signal variations of irrigated areas (2018; 2  
including neural networks [88; 89], change detection techniques [90; 91], and also on direct inversion approaches of physical or semi-empirical models [92]. Typically, the estimates allow an accuracy of the order of 5% in volumetric soil moisture. [PERSON] et al. [96] exploited plot-scale S1 soil moisture to map irrigation over an area in central Italy where agricultural fields are highly fragmented using a methodology based on statistical indices characterizing the spatio-temporal dynamics of soil moisture. The ability of microwave observation to retrieve irrigation timing information was also explored in some studies. [PERSON] et al. [16] proposed to assess irrigation timing at the plot scale by comparing the surface soil moisture of a model forced by crop coefficients derived from S2 to local surface soil moisture measurements and a soil moisture product derived from S1 [88].