Identifying irrigated areas using land surface temperature and hydrological modelling: application to the Rhine basin

Abstract. Information about irrigation with relevant spatiotemporal resolution for understanding and modelling irrigation dynamics is important for improved water resource management. However, achieving a frequent and consistent characterization of areas where signals from rain-fed pixels overlap with irrigated pixels has been challenging. Here, we identify irrigated areas using a novel framework that combines hydrological modelling and satellite observations of land surface temperature (LST). We tested the proposed methodology on the Rhine basin covering the period from 2010 to 2019 at a 1 km resolution. The result includes multiyear irrigated maps and irrigation frequency. Temporal analysis reveals that an average of 159 000 ha received irrigation at least once during the study period. The proposed methodology can approximate irrigated areas with R2 values of 0.79 and 0.77 for 2013 and 2016 compared to irrigation statistics, respectively. In dry regions, the method performs slightly better than in wet regions with R2 values of 0.90 and 0.87 in respective years, with an average improvement in R2 by 0.14. The method approximates irrigated areas in regions with large agricultural holdings better than in regions with small fragmented agricultural holdings, due to binary classification and the choice of spatial resolution. The irrigated areas are mainly identified in the established areas indicated in the existing irrigation maps. A comparison with global datasets reveals different disparities due to spatial resolution, input data, reference period, and processing techniques. From the multiyear results, the largest irrigated area was found in the Alsace region in the Rhine valley, where the irrigation extent is negatively correlated with precipitation (r=-0.82, p value = 0.004) and less with potential evapotranspiration (ET).