Irrigation Timing Retrieval at the Plot Scale Using Surface Soil Moisture Derived from Sentinel Time Series in Europe

Abstract

The difficulty of calculating the daily water budget of irrigated fields is often due to the uncertainty surrounding irrigation amounts and timing. The automated detection of irrigation events has the potential to greatly simplify this process, and the combination of high-resolution SAR (Sentinel-1) and optical satellite observations (Sentinel-2) makes the detection of irrigation events feasible through the use of a surface soil moisture (SSM) product. The motivation behind this study is to utilize a large irrigation dataset (collected during the ESA Irrigation + project over five sites in three countries over three years) to analyze the performance of an established algorithm and to test potential improvements. The study’s main findings are (1) the scores decrease with SSM observation frequency; (2) scores decrease as irrigation frequency increases, which was supported by better scores in France (more sprinkler irrigation) than in Germany (more localized irrigation); (3) replacing the original SSM model with the force-restore model resulted in an improvement of about 6% in the F-score and narrowed the error on cumulative seasonal irrigation; (4) the Sentinel-1 configuration (incidence angle, trajectory) did not show a significant impact on the retrieval of irrigation, which supposes that the SSM is not affected by these changes. Other aspects did not allow a definitive conclusion on the irrigation retrieval algorithm: (1) the lower scores obtained with small NDVI compared to large NDVI were counter-intuitive but may have been due to the larger number of irrigation events during high vegetation periods; (2) merging different runs and interpolating all SSM data for one run produced comparable F-scores, but the estimated cumulative sum of irrigation was around −20% lower compared to the reference dataset in the best cases.

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., 2020, 2023; Zappa et al., 2022, to cite a few). The values of R between spatial patterns of irrigation and ΔE are lower than those referring to ΔSM, circumstance that could be explained by the fulfillment of potential conditions, determining a situation in which ΔE is less responsive to water applied than in a water‐limited regime.…”

Is It Possible to Quantify Irrigation Water‐Use by Assimilating a High‐Resolution Satellite Soil Moisture Product?

“…One approach to identify this is to compare the soil moisture/radar backscatter time series at the plot scale to the regional scale, as irrigation changes soil moisture at a plot scale while atmospheric forcing drives changes at a larger scale (Bazzi et al., 2022; Zappa et al., 2022). Alternatively, comparing SM simulation from a model without an irrigation module to a high‐resolution soil moisture product that contains the irrigation signal can help detecting an irrigation episode (Le Page et al., 2023). However, infrequent satellite observations, dense vegetation, and low irrigation rate (e.g., drip irrigation) are among the factors that might limit the applicability of these methods.…”

Plot-Scale Irrigation Dates and Amount Detection Using Surface Soil Moisture Derived from Sentinel-1 SAR Data in the Optirrig Crop Model

“…In addition, several studies showed the potential of SSM products derived from S1 SAR observations for monitoring and detecting irrigation events over agricultural areas [13,[42][43][44] since the SAR signal is sensitive to SSM through the dielectric properties of the soil [45,46]. Le Page et al [43] examined the potential of the S 2 MP (Sentinel-1/Sentinel-2-derived soil moisture product [47]) in detecting irrigation events at the individual plot level over irrigated maize fields of southwest France.…”

“…Conversely, recent studies assessed the potential of incorporating SSM derived from high-spatial-and -temporal-resolution Sentinel-1 data into simple land surface and water budget models for irrigation events detection [42,44]. For instance, Ouaadi et al [44] conducted a study to introduce and assess a novel method for predicting irrigation timing and amounts at the field scale.…”