Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space

Abstract

Abstract. Irrigation water use represents the primary source of freshwater consumption by humans. The amount of water withdrawals for agricultural purposes is expected to further increase in the upcoming years to face the rising world population and higher living standards. Hence, effective plans for enacting a rational management of agricultural water use are urgent, but they are limited by knowledge gaps about irrigation. Detailed information on irrigation dynamics (i.e., extents, timing, and amounts) is generally lacking worldwide, but satellite observations can be used to fill this gap. This paper describes the first regional-scale and high-resolution (1 and 6 km) irrigation water data sets obtained from satellite observations. The products are developed over three major river basins characterized by varying irrigation extents and methodologies, as well as by different climatic conditions. The data sets are an outcome of the European Space Agency (ESA) Irrigation+ project. The irrigation amounts have been estimated through the SM-based (soil-moisture-based) inversion approach over the Ebro river basin (northeastern Spain), the Po valley (northern Italy), and the Murray–Darling basin (southeastern Australia). The satellite-derived irrigation products referring to the case studies in Europe have a spatial resolution of 1 km, and they are retrieved by exploiting Sentinel-1 soil moisture data obtained through the RT1 (first-order Radiative Transfer) model. A spatial sampling of 6 km is instead used for the Australian pilot area, since in this case the soil moisture information comes from CYGNSS (Cyclone Global Navigation Satellite System) observations. All the irrigation products are delivered with a weekly temporal aggregation. The 1 km data sets over the two European regions cover a period ranging from January 2016 to July 2020, while the irrigation estimates over the Murray–Darling basin are available for the time span April 2017–July 2020. The retrieved irrigation amounts have been compared with benchmark rates collected over selected agricultural districts. Results highlight satisfactory performances over the major part of the pilot sites falling within the two regions characterized by a semiarid climate, namely, the Ebro and the Murray–Darling basins, quantified by median values of RMSE, Pearson correlation r, and bias equal to 12.4 mm/14 d, 0.66, and −4.62 mm/14 d, respectively, for the Ebro basin and to 10.54 mm/month, 0.77, and −3.07 mm/month, respectively, for the Murray–Darling basin. The assessment of the performances over the Po valley is affected by the limited availability of in situ reference data for irrigation. The developed products are made available to the scientific community for use and further validation at https://doi.org/10.5281/zenodo.7341284 (Dari et al., 2022a).

Quantifying the Hydrological Impacts of Irrigation on a Mediterranean Agricultural Context Through Explicit Satellite‐Derived Irrigation Estimates

“…In terms of spatial patterns (see Table 1), ΔSM shows the highest correspondence with the irrigation amounts applied. Such a result corroborates the assumption of using soil moisture as a proxy of irrigation at the basis of several studies aimed at detecting, mapping, and quantifying irrigation (Dari et al, 2023;Dari, Brocca, et al, 2022;Elwan et al, 2022;Filippucci et al, 2020;Le Page et al, 2020Zappa et al, 2022, to cite a few).…”

“…Such irrigation schemes proved to be useful tools to investigate irrigation impacts on both water and energy balances at the regional scale (Modanesi et al, 2022). Recent exploitation of remotely sensed data to estimate irrigation amounts has led to the development of the first spatially distributed irrigation water data sets at different scales (Dari et al, 2020(Dari et al, , 2023Zhang et al, 2022). More in detail, Dari et al (2020) developed a data set of irrigation estimates at 1 km spatial resolution over a heavily irrigated area in Northeastern Spain through the SM-based (Soil Moisture-based) inversion approach, firstly proposed by Brocca et al (2018).…”

“…The data set, relying on DISPATCH (DISaggregation based on Physical And Theoretical scale CHange algorithm) (Merlin et al, 2013) SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) soil moisture, covers an almost-7-year time span. Later on, Dari et al (2023) implemented the methodology to develop high-resolution and regional-scale irrigation data sets for three major basins: the Ebro basin (in Spain), the Po river valley (in Italy), and the Murray-Darling basin (in Australia). In this case, the SM-based inversion approach was forced with Sentinel-1 soil moisture data obtained through the RT1 (firstorder Radiative Transfer) model (Quast et al, 2019(Quast et al, , 2023 for the European sites and with CYGNSS (Cyclone Global Navigation Satellite System) soil moisture (Freeman et al, 2020) for the Australian pilot area.…”

“…Such irrigation schemes proved to be useful tools to investigate irrigation impacts on both water and energy balances at the regional scale (Modanesi et al., 2022). Recent exploitation of remotely sensed data to estimate irrigation amounts has led to the development of the first spatially distributed irrigation water data sets at different scales (Dari et al., 2020, 2023; Zhang et al., 2022). More in detail, Dari et al.…”

Irrigation Quantification Through Backscatter Data Assimilation With a Buddy Check Approach

“…The method showed promising results in terms of detection and correlation, but systematic underestimations of the irrigation water amounts were found when the observation interval was longer than 1 day (in a follow-up synthetic experiment; Zappa et al, 2022). The first regional datasets of high resolution irrigation water use, based on S1 data, have been released by Dari et al (2023) using the soil moisture-based inversion approach. While the backscatter itself has already been used in irrigation mapping and timing studies at the local scale (Bazzi, Baghdadi, Fayad, Charron, et al, 2020;Bazzi, Baghdadi, Fayad, Zribi, et al, 2020), the direct use of S1 data to quantify irrigation is only in its infancy, as changes in backscatter are affected by the water in the topsoil, but also by the vegetation (water, volume, density, and geometry), and terrain roughness (McNairn & Shang, 2016).…”