# A Review of Irrigation Information Retrievals from Space and Their Utility for Users
## 3. Irrigation Remote Sensing
### Visible- and Near-Infrared-Based Methods
#### 3.1.1 Mapping Methods
The obtained fused Greenness Index (GI) estimates with an improved spatial resolution of 30 m were found to be effective in increasing the accuracy of irrigation mapping in the Gansu Province (China). Similarly, [PERSON] et al. [41; 42; 43] have used a random forest classifier combining Sentinel-1 (S1) and Sentinel-2 (S2) data over southern India to investigate the advantages of high-spatial resolution and a multi-sensor approach. The input resolution allowed to retrieve irrigated areas for the two Indian climatic seasons at a resolution of 10 and 20 m.  
Further studies highlighted both the need of sub-kilometric spatial resolutions to differentiate very fragmented irrigated areas and the need for combining multi-sensory information (in this case, radar for soil moisture and optical for vegetation) to better separate irrigated from non-irrigated land. For instance, [PERSON] et al. [44] applied an incremental random forest using a combination of optical (6 Landsat-8 bands each 16 days) and Synthetic Aperture Radar (SAR) data, revealing that the combined input improves the accuracy of the classification with respect to that performed with only one source. Similarly, [PERSON] et al. [45] used an approach that leveraged precipitation data, reaching a similar conclusion. They also revealed that the use of precipitation data improved the performance and that the aggregation of the high temporal resolution to monthly composites resulted in similar performances while reducing the calculation time. The increasing availability of data, processing facilities, and novel classifiers also fostered data merging approaches, including modeled data from land surface and energy balance models for the detection of irrigated land. In the work of [PERSON] et al. [29; 46], the authors used a large number of variables derived from Landsat (9 in the first version, 11 in the second version) and more than 10 covariables, such as precipitation and slope, in a supervised classifier (CART and random forest) for classifying irrigated and non-irrigated areas. The different datasets were processed in the Google Earth Engine (GEE) computing platform, producing a classification at 30 m resolution claiming an overall accuracy above 95% of detected irrigation areas. [PERSON] et al. [47] applied a similar approach in the subhumid temperate state of Michigan. Besides machine learning, also more simple and \"conceptual\" approaches were developed based on the use of a mix of land surface and energy-balance model estimates and remote sensing variables. For instance, [PERSON] et al. [48] looked at the biases between three modeled variables from the ERA-interim dataset (vegetation skin temperature, surface albedo, and soil moisture) and their corresponding variables derived from MODIS and ESA's Climate Change Initiative (CCI). [PERSON] et al. [49] exploited the fact that irrigated crops are supposed to be well-developed and colder than non-irrigated crops, and thus applied a simple threshold on NDVI and the difference of surface temperature to air temperature to detect irrigated areas. Similarly, [PERSON] et al. [50] supposed that irrigated areas evaporate more than non-irrigated areas. Based on the Surface Energy Balance System (SEBS) model, they computed an evaporative fraction, and applied a simple threshold on vegetation indices and the evaporative fraction to discriminate irrigated areas.