# A Review of Irrigation Information Retrievals from Space and Their Utility for Users
## 3. Irrigation Remote Sensing  
Since the 1970s, studies have started using remotely sensed images from Landsat-1 to map irrigated areas and estimate water use [28]. While first images were analyzed visually, research has advanced rapidly in the last decades, where now a multitude of methods are available. Figure 1. Mapping of irrigation in India according to (**a**) GMIA 5.0 [9], (**b**) Global Land Cover Characteristic (GLCC) USGS Land Use/Land Cover System [21], (**c**) GLC2000 for South Asia [22], (**d**) GIAM v2.0 [23], and (**e**) ESA CCI for the year 2000 [24]. We reviewed the status of research in methods for retrieving irrigation information from space by systematically reviewing relevant literature in the topic resulting from the most important databases, such as Scopus, Web of Science, and Google Scholar. Only papers after 2000 were taken into consideration and we selected those (i) introducing innovative techniques to retrieve irrigation information, and (ii) using new type of Earth observation in relation to the publication period. More than 50 manuscripts were reviewed, from which we extracted details on the type of information retrieved (i.e., mapping or quantification), study area, method and type of Earth observation used, spatial scale at which this information was provided, and, when available, the associated accuracy (see Table 1 for a summary of the results). Below, we provide a synthesis of the methodological approaches reviewed, organized based on the main Earth observation technique. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & **Spatial Scale/Sampling** & **Key Results** & **Reference** \\ \hline \hline  & & & [PERSON] et al. (2006), \\  & 30 m & & [PERSON] et al. (2014), \\  & Landsat & Mapping & [PERSON] et al. (2017), [PERSON] et al. (2019) \\ \cline{2-4}  & 250 m, 500 m & & [PERSON] et al. (2016), [PERSON] and [PERSON] (2008), [PERSON] et al. (2010), [PERSON] et al. (2015) \\ \cline{2-4}
**VIS/NIR** & \(\sim\)1 km & & \\  & AVHHR & Mapping & [PERSON] et al. (2006) \\ \cline{2-4}  & 30 m & & \\  & HJ-1A/B & Mapping & [PERSON] et al. (2016) \\ \cline{2-4}  & 30 m & & \\  & MODIS + Landsat OLI & Mapping & [PERSON] et al. (2018) \\ \cline{2-4}  & 500 m & & \\  & MODIS & Quantification & [PERSON] et al. (2020) \\ \cline{2-4}  & 20 m—plot scale & & \\  & Sentinel 2 & Quantification & [PERSON] et al. (2020) \\ \hline \hline  & \(\sim\)30 min & & \\  & Meteosat-9 ET + Water balance & Quantification & [PERSON] et al. (2014) \\ \cline{2-4}  &
\begin{tabular}{c} Basin-scale \\ MODIS plus WEAP and \\ MODFLOW models \\ \end{tabular} & Quantification & [PERSON] et al. (2012) \\ \cline{2-4}
**Mix of** & \(\sim\)25 km & & \\  & ERA5 + MODIS & Mapping & [PERSON] et al. (2019) \\ \cline{2-4}
**VIS/NIR, MW, LSM and** & 30 m & & \\  & SEBS + Landsat & Mapping & [PERSON] et al. (2017) \\ \cline{2-4}  & 30 m & & \\  & Landsat data + SWAP & Quantification & [PERSON] et al. (2010), \\  & & & \\  & 500m & & \\  & Noah-MP + MODIS & Mapping and quantification & [PERSON] et al. (2010) \\ \cline{2-4}  & Basin-wide results & & \\  & MODIS, MeteoSat Second & Quantification & [PERSON] et al. (2015) \\ \cline{2-4}  & Generation (MSG), SEBAL & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of relevant literature on the irrigation detection and quantification using satellite amount. Key results indicate the ability of the technique to map (“mapping”, i.e., detect irrigated areas) and quantify applied irrigation volumes (“quantification”). The technique column indicates the main method used among visible- and near-infrared sensors (VIS/NIR), Microwave (MW), Land Surface models (LSM), and Energy balance models (EBM). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Technique**} & **Spatial Scale/Sampling** & \multirow{2}{*}{**Key Results**} & **Reference** \\  & **(Sensor Used)** & & \\ \hline \multirow{4}{*}{ALEXI based on GOES satellite + Noah LSM} & \multirow{2}{*}{Quantification} & [PERSON] et al. (2014) \\  & & & \\ \cline{2-3}  & \(\sim\)4 km & & \\ \cline