Exploiting High-Resolution Remote Sensing Soil Moisture to Estimate Irrigation Water Amounts over a Mediterranean Region

Abstract

Despite irrigation being one of the main sources of anthropogenic water consumption, detailed information about water amounts destined for this purpose are often lacking worldwide. In this study, a methodology which can be used to estimate irrigation amounts over a pilot area in Spain by exploiting remotely sensed soil moisture is proposed. Two high-resolution disaggregation based on physical and theoretical scale change (DISPATCH) downscaled soil moisture products have been used: Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) at 1 km. The irrigation estimates have been obtained through the SM2RAIN algorithm, in which the evapotranspiration term has been improved to adequately reproduce the crop evapotranspiration over irrigated areas according to the Food and Agriculture Organization (FAO) model. The experiment exploiting the SMAP data at 1 km represents the main work analyzed in this study and covered the period from January 2016 to September 2017. The experiment with the SMOS data at 1 km, for which a longer time series is available, allowed the irrigation estimates to be extended back to 2011. For both of the experiments carried out, the proposed method performed well in reproducing the magnitudes of the irrigation amounts that actually occurred in four of the five pilot irrigation districts. The SMAP experiment, for which a more detailed analysis was performed, also provided satisfactory results in representing the spatial distribution and the timing of the irrigation events. In addition, the investigation into which term of the SM2RAIN algorithm plays the leading role in determining the amount of water entering into the soil highlights the importance of correct representation of the evapotranspiration process.

A Review of Irrigation Information Retrievals from Space and Their Utility for Users

“…However, due to the lack of reliable ground reference information about irrigation, it currently remains difficult to provide exact accuracy estimates. Nevertheless, in a recent study, Dari et al [102] reported irrigation retrieval accuracies that span from 10% to 40%, which is not far from the defined targets.…”

“…Notwithstanding the potential of currently available microwave-based soil moisture products, their biggest limitation to the retrieval of accurate irrigation water amounts is the coarse spatial resolution of (most) sensors, i.e., pixel sizes in the order of tens of km. To address this issue, Dari et al [102] recently exploited 1 km DISPATCH downscaled versions of SMOS and SMAP soil moisture to estimate almost 7 years of irrigation water amounts over an intensely irrigated area in the North East of Spain. Irrigation was retrieved through an improved version of the SM2RAIN algorithm, in which the crop evapotranspiration was estimated according to the FAO model.…”

Estimating water balance components in irrigated agriculture using a combined approach of soil moisture and energy balance monitoring, and numerical modelling

“…Soil moisture changes can be derived from in situ observations (Brocca et al, 2015) or from satellite‐based radar data (Brocca et al, 2014). Later, this approach was also used to estimate irrigation volumes (Brocca et al, 2018; Dari et al, 2020; Filippucci et al, 2020).…”

High resolution (1 km) satellite rainfall estimation from SM2RAIN applied to Sentinel-1: Po River Basin as case study

“…Many different approaches, from geostatistical analysis to data fusion, have been developed in the last years in order to obtain sub-pixel information from coarse resolution products (Peng et al, 2017) to be used in different applications (e.g. Dari et al, 2020). However, their results were often unsatisfactory, because of the limitations of the auxiliary data (e.g., cloud cover for optical data and model errors when using model data) and the uncertainties of downscaling algorithms (Peng et al, 2017).…”