# 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  
Data from optical sensors including Landsat, MODIS (Moderate Resolution Imaging Spectroradiometer), AVHRR (Advanced Very High-Resolution Radiometer), MERIS (Medium, Resolution Imaging spectrometer), and SPOT (Satellite pour l'Observation de la Terre) [4; 29; 30; 31; 32], and their combination with other model-based and ground-based ancillary data [33], have been extensively used to map irrigated areas, making use of the difference in the spectral responses of irrigated and non-irrigated croplands. One of the first explicit attempts to use visible- and near-infrared remote sensing information for the detection of irrigated areas at the global scale is the International Water Management Institute Global Irrigated Area Map [23]. This dataset represented the irrigated areas of the world in 1999 with a resolution of 10 km and 28 classes. The algorithm was applied to more than 20 years of resampled AVHRR monthly time series of reflectance, combined with one year of monthly Normalized Difference Vegetation Index (NDVI) from Spot Vegetation, mean annual rainfall, a forest cover map, and a Digital Elevation Model (DEM). Since then, there have been many other notable attempts to provide information on irrigation from the regional to the continental/global scale. For instance, [PERSON] and [PERSON] [32] used a supervised classification to map the irrigated area in 2001 over the contiguous United States by using a combination of Landsat and MODIS data. [PERSON] et al. [34] developed yearly maps (2000-2015) of irrigated areas in India based on a thresholding of 16-day composite NDVI time series from MODIS. The decision tree between irrigated and non-irrigated areas was applied on each crop class (previously produced on the basis of IRS-P6) inside each agroecological zone. The following works of [PERSON] et al. [35], [PERSON] and [PERSON] [36], [PERSON] et al. [37], and [PERSON] et al. [38] provided some updates with respect to previous works by working on census areas rather than on crop classes. A notable example is the method applied by [PERSON] et al. [31] on the Murray-Darling Basin in Australia, which used a supervised classification based on the random forest technique and data not only from vegetation (in this case, MODIS NDVI), but also from hydrometeorological data, such as monthly evapotranspiration (which was simply estimated by scaling Priestley-Taylor potential evapotranspiration, ETp, via a crop factor that was derived from other satellite observations) and monthly precipitation. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Technique**} & **Spatial Scale/Sampling** & \multirow{2}{*}{**Key Results**} & **Reference** \\  & **(Sensor Used)** & & \\ \hline  & 25 km & & \\  & AMSR2 & Quantification & [PERSON] et al. (2019) \\ \hline  & 1 km & & \\  & SMOS & Mapping & [PERSON] et al. (2018) \\ \hline  & 0.25\({}^{\circ}\) & & \\  & AMSR-E, AMSR2, ASCAT, ESA & Mapping & [PERSON] et al. (2018) \\  & CCI & & \\ \hline  & 1 km & & \\  & SMAP, SMOS & Quantification & [PERSON] et al. (2020) \\ \hline  & 500 m & & \\  & Sentinel 1 & Quantification & [PERSON] et al. (2021) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Later studies focused even more strongly on the synergistic use of machine learning methods and high-resolution remote sensing data. For instance, high spatio-temporal resolution NDVI data (30 \(\times\) 30 m) from the Chinese HJ-1A/B (HuanJing, HJ) satellite were used by [PERSON] et al. [39] to separate irrigated from rainfed areas in the semi-arid province of Shanxi in China, through a novel classification method based on a Support Vector Machine (SVM). They found that not only spatial but also temporal resolution may influence the classification results. A study by [PERSON] et al. [40] also focused on a more extended irrigation detection which includes extracting frequency and timing information through data fusion of MODIS time series, Landsat OLI data, and ancillary data.