Irrigation Timing Retrieval at the Plot Scale Using Surface Soil Moisture Derived from Sentinel Time Series in Europe
Le Page, Michel ; Nguyen, Thang ; Zribi, Mehrez ; Boone, Aaron ; Dari, Jacopo ; Modanesi, Sara ; Zappa, Luca ; Ouaadi, Nadia ; Jarlan, Lionel
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.</p>
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