The detection of irrigation through remote sensing soil moisture and a land surface model: a case study in Spain

Abstract

<p>Irrigation practices introduce imbalances in the natural hydrological cycle at different spatial scales and put pressure on water resources, especially under climate changing and population increasing scenarios. Despite the implications of irrigation on food production and on the rational management of the available freshwater, detailed information about the areas where irrigation actually occurs is still lacking. For this reason, the comprehensive knowledge of the dynamics of the hydrological cycle over agricultural areas is often tricky.</p><p>The first aim of this study is to evaluate the capability of five remote sensing soil moisture data sets to detect the irrigation signal over an intensely irrigated area located within the Ebro river basin, in the North of Spain, during the biennium 2016-2017. As a second objective, a methodology to map the irrigated areas through the K-means clustering algorithm is proposed. The remotely sensed soil moisture products used in this study are: SMOS (Soil Moisture and Ocean Salinity) at 1 km, SMAP (Soil Moisture Active Passive) at 1 km and 9 km, Sentinel-1 at 1 km and ASCAT (Advanced SCATterometer) at 12.5 km. The 1 km versions of SMOS and SMAP are DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled versions of the corresponding coarser resolution products. An additional data set of soil moisture simulated by the SURFEX-ISBA (<em>Surface Externalisée - Interaction Sol Biosphère Atmosphère</em>) land surface model is used as a support for the performed analyses.</p><p>The capability of soil moisture products to detect irrigation has been investigated by exploiting indices representing the spatial and temporal dynamics of soil moisture. The L-band passive microwave downscaled products, especially SMAP at 1 km, result the best performing ones in detecting the irrigation signal over the pilot area; on the basis of these data sets, the K-means algorithm has been employed to classify three kinds of surfaces within the study area: the dryland, the forest or natural areas, and the actually irrigated areas. The resulting maps have been validated by exploiting maps of crops in Catalonia as ground truth data set. The percentage of irrigated areas well classified by the proposed method reaches the value of 78%; this result is obtained for the period May - September 2017. In addition, the method performs well in distinguishing the irrigated areas from rainfed agricultural areas, which are dry during summer, thus representing a useful tool to obtain explicit spatial information about where irrigation practices actually occur over agricultural areas equipped for this purpose.</p>