Although irrigation practices affect food production and water resource management, with ever more impacting effects under climate change and population increasing scenarios, detailed knowledge of irrigation is still lacking. In fact, explicit information on the spatial occurrence of irrigation and on the amounts of water used for this purpose is often not available, thus making irrigation the missing variable to comprehensively understand the hydrological cycle dynamics over agricultural areas. Nevertheless, remote sensing techniques can be used to delimit the irrigation extent. In this study, the capability of five remotely sensed soil moisture products to detect the irrigation signal over an area intensely equipped for irrigation in the North East of Spain is investigated; moreover, a method to map the actually irrigated areas based on the K-means clustering algorithm is proposed. The remote sensing soil moisture data sets 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 resolution versions of SMOS and SMAP are obtained by downscaling coarser SMOS and SMAP data through the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) algorithm. The analyses are supported by an additional data set of soil moisture at 1 km resolution simulated by the SURFEX-ISBA (SURFace EXternalisée – Interaction Sol Biosphère Atmosphère) land surface model. Among all the considered data sets, the L-band passive microwave downscaled products show the best performances in detecting the irrigation signal over the pilot area, especially SMAP at 1 km. The proposed maps of irrigated areas derived by exploiting soil moisture from SMAP at 1 km data set agree well (up to 78%) with the ground truth derived irrigated areas. Furthermore, the method is able to well distinguish the actually irrigated areas from rainfed agricultural areas, thus representing a useful tool to obtain reliable spatial information about the areas where irrigation actually occurs.

Detecting and mapping irrigated areas in a Mediterranean environment by using remote sensing soil moisture and a land surface model

Dari J.
;
Morbidelli R.
2021

Abstract

Although irrigation practices affect food production and water resource management, with ever more impacting effects under climate change and population increasing scenarios, detailed knowledge of irrigation is still lacking. In fact, explicit information on the spatial occurrence of irrigation and on the amounts of water used for this purpose is often not available, thus making irrigation the missing variable to comprehensively understand the hydrological cycle dynamics over agricultural areas. Nevertheless, remote sensing techniques can be used to delimit the irrigation extent. In this study, the capability of five remotely sensed soil moisture products to detect the irrigation signal over an area intensely equipped for irrigation in the North East of Spain is investigated; moreover, a method to map the actually irrigated areas based on the K-means clustering algorithm is proposed. The remote sensing soil moisture data sets 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 resolution versions of SMOS and SMAP are obtained by downscaling coarser SMOS and SMAP data through the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) algorithm. The analyses are supported by an additional data set of soil moisture at 1 km resolution simulated by the SURFEX-ISBA (SURFace EXternalisée – Interaction Sol Biosphère Atmosphère) land surface model. Among all the considered data sets, the L-band passive microwave downscaled products show the best performances in detecting the irrigation signal over the pilot area, especially SMAP at 1 km. The proposed maps of irrigated areas derived by exploiting soil moisture from SMAP at 1 km data set agree well (up to 78%) with the ground truth derived irrigated areas. Furthermore, the method is able to well distinguish the actually irrigated areas from rainfed agricultural areas, thus representing a useful tool to obtain reliable spatial information about the areas where irrigation actually occurs.
2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1492061
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