Plot-Scale Irrigation Dates and Amount Detection Using Surface Soil Moisture Derived from Sentinel-1 SAR Data in the Optirrig Crop Model
This study aimed to develop an approach using Sentinel-1 synthetic aperture radar (SAR) data and the Optirrig crop growth and irrigation model to detect irrigation dates and amounts for maize crops in the Occitanie region, Southern France. The surface soil moisture (SSM) derived from SAR data was an...
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MDPI AG
2023-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/16/4081 |
| _version_ | 1797583314114052096 |
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| author | Mohamad Hamze Bruno Cheviron Nicolas Baghdadi Dominique Courault Mehrez Zribi |
| author_facet | Mohamad Hamze Bruno Cheviron Nicolas Baghdadi Dominique Courault Mehrez Zribi |
| author_sort | Mohamad Hamze |
| collection | DOAJ |
| description | This study aimed to develop an approach using Sentinel-1 synthetic aperture radar (SAR) data and the Optirrig crop growth and irrigation model to detect irrigation dates and amounts for maize crops in the Occitanie region, Southern France. The surface soil moisture (SSM) derived from SAR data was analyzed for changes indicating irrigation events at the plot scale in four reference plots located in Montpellier (P1) and Tarbes (P2, P3, and P4). As rain most likely covers several square kilometers, while irrigation is decided at the plot scale, a difference between SSM signals at the grid scale (10 km × 10 km) and plot scale is a clear indication of a recent irrigation event. Its date and amount are then sought by forcing irrigation dates and amounts in Optirrig, selecting the most relevant (date, amount) combination from an appropriate criterion. As the observed SSM values hold for a depth of a few centimeters, while the modeled SSM values hold for exactly 10 cm, the best irrigation combination is the one that gives similar relative changes in SSM values rather than similar SSM values. The irrigation dates were detected with an overall accuracy (recall) of 86.2% and a precision of 85.7%, and thus, with relatively low numbers of missed or false irrigation detections, respectively. The performance of the method in detecting seasonal irrigation amounts varied with climatic conditions. For the P1 plot in the semi-arid climate of Montpellier, the mean absolute error percentage (MAE%) was 16.4%, showing a higher efficiency when compared with the humid climate of Tarbes (P2, P3, and P4 plots), where a higher MAE% of 50% was recorded, indicating a larger discrepancy between the detected and actual irrigation amounts. The limitations of the proposed method can be attributed to the characteristics of the Sentinel-1 constellation, including its 6-day revisit time and signal penetration challenges in dense maize cover, as well as the mismatch between the parameterization of Optirrig for SSM simulations and the actual irrigation practices followed by farmers. Despite these weaknesses, the results demonstrated the relevance of combining Optirrig and S1 SAR-derived SSM data for field-scale detection of irrigation dates and, potentially, irrigation amounts. |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T23:37:07Z |
| publishDate | 2023-08-01 |
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| spelling | doaj.art-f252c3c7772143a1ba66e375d7e127522023-11-19T02:54:17ZengMDPI AGRemote Sensing2072-42922023-08-011516408110.3390/rs15164081Plot-Scale Irrigation Dates and Amount Detection Using Surface Soil Moisture Derived from Sentinel-1 SAR Data in the Optirrig Crop ModelMohamad Hamze0Bruno Cheviron1Nicolas Baghdadi2Dominique Courault3Mehrez Zribi4National Research Institute for Agriculture, Food and Environment (INRAE), UMR TETIS, University of Montpellier, 500 rue François Breton, 34093 Montpellier CEDEX 5, FranceUMR G-EAU, National Research Institute for Agriculture, Food and Environment (INRAE), 34090 Montpellier, FranceNational Research Institute for Agriculture, Food and Environment (INRAE), UMR TETIS, University of Montpellier, 500 rue François Breton, 34093 Montpellier CEDEX 5, FranceUMR 1114 EMMAH, National Research Institute for Agriculture, Food and Environment (INRAE)-Avignon Université, Domaine StPaul, Agroparc, 84914 Avignon, FranceCESBIO (Centre d’Etudes Spatiales de la Biosphère, CNRS/UPS/IRD/CNES/INRAE), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, FranceThis study aimed to develop an approach using Sentinel-1 synthetic aperture radar (SAR) data and the Optirrig crop growth and irrigation model to detect irrigation dates and amounts for maize crops in the Occitanie region, Southern France. The surface soil moisture (SSM) derived from SAR data was analyzed for changes indicating irrigation events at the plot scale in four reference plots located in Montpellier (P1) and Tarbes (P2, P3, and P4). As rain most likely covers several square kilometers, while irrigation is decided at the plot scale, a difference between SSM signals at the grid scale (10 km × 10 km) and plot scale is a clear indication of a recent irrigation event. Its date and amount are then sought by forcing irrigation dates and amounts in Optirrig, selecting the most relevant (date, amount) combination from an appropriate criterion. As the observed SSM values hold for a depth of a few centimeters, while the modeled SSM values hold for exactly 10 cm, the best irrigation combination is the one that gives similar relative changes in SSM values rather than similar SSM values. The irrigation dates were detected with an overall accuracy (recall) of 86.2% and a precision of 85.7%, and thus, with relatively low numbers of missed or false irrigation detections, respectively. The performance of the method in detecting seasonal irrigation amounts varied with climatic conditions. For the P1 plot in the semi-arid climate of Montpellier, the mean absolute error percentage (MAE%) was 16.4%, showing a higher efficiency when compared with the humid climate of Tarbes (P2, P3, and P4 plots), where a higher MAE% of 50% was recorded, indicating a larger discrepancy between the detected and actual irrigation amounts. The limitations of the proposed method can be attributed to the characteristics of the Sentinel-1 constellation, including its 6-day revisit time and signal penetration challenges in dense maize cover, as well as the mismatch between the parameterization of Optirrig for SSM simulations and the actual irrigation practices followed by farmers. Despite these weaknesses, the results demonstrated the relevance of combining Optirrig and S1 SAR-derived SSM data for field-scale detection of irrigation dates and, potentially, irrigation amounts.https://www.mdpi.com/2072-4292/15/16/4081Sentinel-1surface soil moistureOptirrigcrop growth modelirrigationmaize |
| spellingShingle | Mohamad Hamze Bruno Cheviron Nicolas Baghdadi Dominique Courault Mehrez Zribi Plot-Scale Irrigation Dates and Amount Detection Using Surface Soil Moisture Derived from Sentinel-1 SAR Data in the Optirrig Crop Model Remote Sensing Sentinel-1 surface soil moisture Optirrig crop growth model irrigation maize |
| title | Plot-Scale Irrigation Dates and Amount Detection Using Surface Soil Moisture Derived from Sentinel-1 SAR Data in the Optirrig Crop Model |
| title_full | Plot-Scale Irrigation Dates and Amount Detection Using Surface Soil Moisture Derived from Sentinel-1 SAR Data in the Optirrig Crop Model |
| title_fullStr | Plot-Scale Irrigation Dates and Amount Detection Using Surface Soil Moisture Derived from Sentinel-1 SAR Data in the Optirrig Crop Model |
| title_full_unstemmed | Plot-Scale Irrigation Dates and Amount Detection Using Surface Soil Moisture Derived from Sentinel-1 SAR Data in the Optirrig Crop Model |
| title_short | Plot-Scale Irrigation Dates and Amount Detection Using Surface Soil Moisture Derived from Sentinel-1 SAR Data in the Optirrig Crop Model |
| title_sort | plot scale irrigation dates and amount detection using surface soil moisture derived from sentinel 1 sar data in the optirrig crop model |
| topic | Sentinel-1 surface soil moisture Optirrig crop growth model irrigation maize |
| url | https://www.mdpi.com/2072-4292/15/16/4081 |
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