## 2 Review of Existing Studies
### Global Studies  
In recent years, there have been several attempts to determine the spatial extent of irrigation at global scales. These attempts stem particularly from the crucial need for accurate estimates of irrigated areas for water use assessments and food security studies. One such attempt is the USGS Global Land Cover Map [22] that was generated based on 1 km AVHRR observations between April 1992 and September 1993. Among several land-cover legends that the database contains, the Global Ecosystems Legend identifies four irrigated classes: irrigated grasslands, rice paddies and fields, hot irrigated cropland and cool irrigated cropland. When combined, these classes provide one of the few sources of remotely-sensed information on spatial distribution at the global scale. The major shortcoming of the USGS map, however, is that irrigated areas were determined as part of a broader classification scheme. Thus the emphasis was primarily on other land-cover types while irrigated classes received less attention and thus lower classification accuracy. [PERSON] and [PERSON] [14] compared irrigatedlands depicted by the USGS map to the country level reports of irrigated areas [54] and pointed to major uncertainties in classification and inventory due to the highly politicized nature of FAO data reports, as well as to the technical limitations of the more objective datasets made from remote sensing. More recently, the European Space Agency (ESA) produced a moderate spatial resolution (300 m) global land cover product (GlobeCover) using Medium Resolution Imaging Spectrometer (MERIS) data acquired between mid-2005 and mid-2006 [55]. Several thematic classes within the GlobeCover legend identify irrigated crops, including flooded rice. The classification process includes unsupervised clustering of selected mosaics that are then temporally characterized based on computation of phenological parameters (start, end and duration of the vegetation period), using the time series of MERIS mosaics. In the last step, the classes were labeled with help from regional land cover experts, who advised on the interpretation of the spectral-temporal classes, and defined the improved labeling rules. [PERSON] _et al._[56] evaluated the utility of spectral matching techniques to determine historical land use and land cover (LULC) and irrigated area classes using the historical time series 0.1-degree AVHRR data without ground truth comparison. By using monthly continuous time series AVHRR data, they established the LULC for the period 1982-1985 and for the period 1996-1999. They used quantitative and qualitative spectral matching methods to identify and label LULC classes, including spectral similarity value, spectral correlation similarity, Euclidian distance, and modified spectral angle. The results show that spectral similarity was the best method, followed by spectral correlation. The other methods were more complex, and provided results that were either uncertain or no better. They used data from India's Central Board of Irrigation and Power for validation. The 1982-1985 irrigated area was 2,975,800 hectares, which was 8.5 percent higher than the non-remote-sensing-based area estimate by the Central Board for 1984 (2,743,638 hectares). These results helped establish the strengths of the spectral matching techniques in identifying and labeling LULC and irrigated area classes from the historical satellite sensor data for which little or no ground truth data are available. The spectral matching methods can be further strengthened by additional research involving a rich ground-based knowledge base. The study also implied that future application of this method at global scale is promising but may require a better potential irrigation index and the use of remotely-sensed skin temperature measurements. [PERSON] _et al._[57] also produced a Global Irrigated Area Map (GIAM) for the end of the last millennium.