Irrigation Timing Retrieval at the Plot Scale Using Surface Soil Moisture Derived from Sentinel Time Series in Europe Article Swipe
Michel Le Page
,
Thang Nguyen
,
Mehrez Zribi
,
Aaron Boone
,
Jacopo Dari
,
Sara Modanesi
,
Luca Zappa
,
Nadia Ouaadi
,
Lionel Jarlan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/rs15051449
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/rs15051449
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs15051449
- https://www.mdpi.com/2072-4292/15/5/1449/pdf?version=1679034306
- OA Status
- gold
- Cited By
- 10
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4323318665
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4323318665Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs15051449Digital Object Identifier
- Title
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Irrigation Timing Retrieval at the Plot Scale Using Surface Soil Moisture Derived from Sentinel Time Series in EuropeWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-03-04Full publication date if available
- Authors
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Michel Le Page, Thang Nguyen, Mehrez Zribi, Aaron Boone, Jacopo Dari, Sara Modanesi, Luca Zappa, Nadia Ouaadi, Lionel JarlanList of authors in order
- Landing page
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https://doi.org/10.3390/rs15051449Publisher landing page
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https://www.mdpi.com/2072-4292/15/5/1449/pdf?version=1679034306Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2072-4292/15/5/1449/pdf?version=1679034306Direct OA link when available
- Concepts
-
Irrigation, Environmental science, Remote sensing, Normalized Difference Vegetation Index, Water content, Irrigation scheduling, Satellite, Soil science, Soil water, Climate change, Geology, Agronomy, Oceanography, Biology, Geotechnical engineering, Aerospace engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 4, 2023: 4Per-year citation counts (last 5 years)
- References (count)
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35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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