# A Review of Irrigation Information Retrievals from Space and Their Utility for Users
## 3. Irrigation Remote Sensing
### Visible- and Near-Infrared-Based Methods
#### 3.1.2 Quantification Methods  
Besides mapping irrigated areas, optical remote sensing can also offer some solutions for estimating the irrigation water use. Some of the approaches rely on calculating a crop coefficient and/or exploiting actual evapotranspiration (ETa) estimates from remote sensing (e.g., MODIS, Landsat, S2, S3, VIIRS, and AVHRR). For example, [PERSON] et al. [15]calculated the irrigation in the Tensift basin in Morocco as the difference between actual evapotranspiration and precipitation. They subtracted it from the known distributed water from dams to estimate the groundwater demand in an integrated modeling of demand and supply of water. This method exploited previous works on the determination of crop coefficients [51; 52; 53; 54; 55; 56; 57; 58; 59] to estimate actual evapotranspiration [60] by relying upon remote sensing-based vegetation indices [61; 62; 63; 64]. The crop coefficient was also used as a supplement in a parameterized water balance approach by [PERSON] et al. [65] in order to show growth conditions. Water and energy balance modeling approaches have also been investigated to assess irrigation water amounts using a combination of surface energy balance models and remote sensing data [66; 67; 68]. For instance, [PERSON] et al. [69] employed the surface energy balance algorithm for land (SEBAL, [70; 71]) for mapping total \(\text{ET}_{\text{a}}\), that is split into \(\text{ET}_{\text{a}}\) induced by precipitation (in rainfed agro-ecosystems) and \(\text{ET}_{\text{a}}\) induced by water withdrawals. Still based on ET, [PERSON] et al. [72] compared the estimated \(\text{ET}_{\text{a}}\) by the Noah LSM (not incorporating irrigation) with a remotely sensed \(\text{ET}_{\text{a}}\) product retrieved using the Atmosphere-Land Exchange Inverse (ALEXI) model over Contiguous United States. The excess \(\text{ET}_{\text{a}}\) estimated by ALEXI was attributed to the irrigation. While large-scale irrigation agriculture could be mapped by this approach, the inherent error in Noah LSM and ALEXI \(\text{ET}_{\text{a}}\) estimates hindered the reliability of the method in certain parts of the study area. Conceptually similar was the work of [PERSON] et al. [73], who used a combination of remotely sensed data and hydrological modeling outputs to estimate the volumes of water consumed in the form of evapotranspiration over irrigated lands in two sub-basins of the Murray-Darling basin (Australia). Alternatively, [PERSON] et al. [74] used Landsat 7-8 Land Surface Temperature (LST) to derive a crop water stress coefficient that was injected directly into a water balance model, and then retrieved irrigation at the pixel scale before aggregating it to the plot scale. The method was tested on wheat fields in Morocco, exhibiting good performance for estimates of seasonal amounts, but lower agreement at the daily scale. In an attempt at using only satellite-derived ET, [PERSON] et al. [75] compared the ET derived from the MODIS evapotranspiration product (MOD16A2) of natural and irrigated agricultural fields to calculate the excess water or recharge, which was used as an indicator for the water available for irrigation. Somewhat similar to the study of [PERSON] et al., and based on the research of [PERSON] et al. [69], advances have been made in developing irrigation quantification algorithms, specifically, the so-called \"ETLook\" algorithm [76]. Here, the similarity of natural and irrigated pixels is defined using multiple static sources, such as a land use map, a DEM, and soil type information. [PERSON] et al. [77] quantified irrigation amounts with the use of NDVI derived from S2 observations over a particularly complex mosaic of rainfed and irrigated crops in Central Italy, obtaining mean biases below 0.3 mm/day and 2.0 mm/week in two sites where irrigation reference data were available. Lastly, [PERSON] et al.