# A Review of Irrigation Information Retrievals from Space and Their Utility for Users
## 3. Irrigation Remote Sensing
### Microwave-Based Methods
#### 3.2.2 Quantification Methods  
The quantification of the amount of water used for irrigation is generally more challenging than mapping the extent of irrigated areas alone. The first study trying to estimate the irrigation water amount from satellite soil moisture data has been carried out by [PERSON] et al. [97], who quantified irrigation at 9 pilot sites in the USA, Europe, Africa, and Australia, with 4 satellite soil moisture products. This study demonstrated the feasibility of such methods for the quantification of irrigation water amounts, especially when using satellite observations with low retrieval errors (<0.04 m\({}^{3}\)/m\({}^{3}\)) and short revisit times (<3 days). Good results were also found in regions with dry summers where the irrigation signal is more clearly pronounced (see Figure 2). The same approach was further applied by [PERSON] et al. [98], who obtained good agreement with ground-based irrigation data in a semi-arid region in Iran. [PERSON] et al. [99] qualitatively assessed the potential of various microwaved-based satellite soil moisture products for the detection of irrigation in China.  
Figure 2: Comparison of observed (statistical survey) and estimated (through satellite soil moisture) annual irrigation volumes for three regions in Spain (adapted from [PERSON] et al. [97]).  
An alternative method, but based on the same premise, i.e., that soil moisture predictions from (some) land surface models do not contain irrigated water, whereas satellite soil moisture retrievals do, was proposed by [PERSON] et al. [100], who estimated the irrigation water use over the Contiguous United States from various coarse-resolution sensors (ASCAT, SMAP, and AMSR2). This approaches the aggregated deviation between the modeled and satellite-observed soil moisture climatology as an irrigation water use estimate. A similar approach was employed by [PERSON] and [PERSON] [101] to identify trends of irrigation water amounts worldwide. Irrigation estimates from both studies showed a good correlation with country-level reported irrigation data, though irrigation was systematically underestimated. Notwithstanding the potential of currently available microwave-based soil moisture products, their biggest limitation to the retrieval of accurate irrigation water amounts is the coarse spatial resolution of (most) sensors, i.e., pixel sizes in the order of tens of km. To address this issue, [PERSON] et al. [102] recently exploited 1 km DISPATCH downscaled versions of SMOS and SMAP soil moisture to estimate almost 7 years of irrigation water amounts over an intensely irrigated area in the North East of Spain. Irrigation was retrieved through an improved version of the SM2 RAIN algorithm, in which the crop evapotranspiration was estimated according to the FAO model. Comparisons with district-scale benchmark irrigation volumes showed the suitability of the method to estimate actual irrigation amounts, as well as the skill in reproducing the temporal dynamics of irrigation. [PERSON] et al. [103] developed a framework for the detection and quantification of irrigation based on the TU-Wien S1 soil moisture product at 500 m resolution [91]. Good agreement was found against field-scale irrigation reference data in Northern Germany, both in terms of spatial patterns and temporal dynamics. However, the overall irrigation volumes were generally underestimated as a result of field-specific irrigation systems and management practices, and the longer revisit time of the S1 images (up to a few days) compared to coarse-scale products.