Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
In the context of monitoring and assessment of water consumption in the agricultural sector, the objective of this study is to build an operational approach capable of detecting irrigation events at plot scale in a near real-time scenario using Sentinel-1 (S1) data. The proposed approach is a decisi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/9/1456 |
| _version_ | 1827717456484892672 |
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| author | Hassan Bazzi Nicolas Baghdadi Ibrahim Fayad Mehrez Zribi Hatem Belhouchette Valérie Demarez |
| author_facet | Hassan Bazzi Nicolas Baghdadi Ibrahim Fayad Mehrez Zribi Hatem Belhouchette Valérie Demarez |
| author_sort | Hassan Bazzi |
| collection | DOAJ |
| description | In the context of monitoring and assessment of water consumption in the agricultural sector, the objective of this study is to build an operational approach capable of detecting irrigation events at plot scale in a near real-time scenario using Sentinel-1 (S1) data. The proposed approach is a decision tree-based method relying on the change detection in the S1 backscattering coefficients at plot scale. First, the behavior of the S1 backscattering coefficients following irrigation events has been analyzed at plot scale over three study sites located in Montpellier (southeast France), Tarbes (southwest France), and Catalonia (northeast Spain). To eliminate the uncertainty between rainfall and irrigation, the S1 synthetic aperture radar (SAR) signal and the soil moisture estimations at grid scale (10 km × 10 km) have been used. Then, a tree-like approach has been constructed to detect irrigation events at each S1 date considering additional filters to reduce ambiguities due to vegetation development linked to the growth cycle of different crops types as well as the soil surface roughness. To enhance the detection of irrigation events, a filter using the normalized differential vegetation index (NDVI) obtained from Sentinel-2 optical images has been proposed. Over the three study sites, the proposed method was applied on all possible S1 acquisitions in ascending and descending modes. The results show that 84.8% of the irrigation events occurring over agricultural plots in Montpellier have been correctly detected using the proposed method. Over the Catalonian site, the use of the ascending and descending SAR acquisition modes shows that 90.2% of the non-irrigated plots encountered no detected irrigation events whereas 72.4% of the irrigated plots had one and more detected irrigation events. Results over Catalonia also show that the proposed method allows the discrimination between irrigated and non-irrigated plots with an overall accuracy of 85.9%. In Tarbes, the analysis shows that irrigation events could still be detected even in the presence of abundant rainfall events during the summer season where two and more irrigation events have been detected for 90% of the irrigated plots. The novelty of the proposed method resides in building an effective unsupervised tool for near real-time detection of irrigation events at plot scale independent of the studied geographical context. |
| first_indexed | 2024-03-10T20:02:40Z |
| format | Article |
| id | doaj.art-b5eca65326ef4951984feba808c6bacb |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:02:40Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b5eca65326ef4951984feba808c6bacb2023-11-19T23:28:14ZengMDPI AGRemote Sensing2072-42922020-05-01129145610.3390/rs12091456Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 DataHassan Bazzi0Nicolas Baghdadi1Ibrahim Fayad2Mehrez Zribi3Hatem Belhouchette4Valérie Demarez5INRAE, UMR TETIS, University of Montpellier, 500 rue François Breton, CEDEX 5 34093 Montpellier, FranceINRAE, UMR TETIS, University of Montpellier, 500 rue François Breton, CEDEX 5 34093 Montpellier, FranceINRAE, UMR TETIS, University of Montpellier, 500 rue François Breton, CEDEX 5 34093 Montpellier, FranceCESBIO (CNRS/UPS/IRD/CNES/INRAE), 18 av. Edouard Belin, bpi 2801, CEDEX 9 31401 Toulouse, FranceCIHEAM-IAMM, UMR-System, 34090 Montpellier, FranceCESBIO (CNRS/UPS/IRD/CNES/INRAE), 18 av. Edouard Belin, bpi 2801, CEDEX 9 31401 Toulouse, FranceIn the context of monitoring and assessment of water consumption in the agricultural sector, the objective of this study is to build an operational approach capable of detecting irrigation events at plot scale in a near real-time scenario using Sentinel-1 (S1) data. The proposed approach is a decision tree-based method relying on the change detection in the S1 backscattering coefficients at plot scale. First, the behavior of the S1 backscattering coefficients following irrigation events has been analyzed at plot scale over three study sites located in Montpellier (southeast France), Tarbes (southwest France), and Catalonia (northeast Spain). To eliminate the uncertainty between rainfall and irrigation, the S1 synthetic aperture radar (SAR) signal and the soil moisture estimations at grid scale (10 km × 10 km) have been used. Then, a tree-like approach has been constructed to detect irrigation events at each S1 date considering additional filters to reduce ambiguities due to vegetation development linked to the growth cycle of different crops types as well as the soil surface roughness. To enhance the detection of irrigation events, a filter using the normalized differential vegetation index (NDVI) obtained from Sentinel-2 optical images has been proposed. Over the three study sites, the proposed method was applied on all possible S1 acquisitions in ascending and descending modes. The results show that 84.8% of the irrigation events occurring over agricultural plots in Montpellier have been correctly detected using the proposed method. Over the Catalonian site, the use of the ascending and descending SAR acquisition modes shows that 90.2% of the non-irrigated plots encountered no detected irrigation events whereas 72.4% of the irrigated plots had one and more detected irrigation events. Results over Catalonia also show that the proposed method allows the discrimination between irrigated and non-irrigated plots with an overall accuracy of 85.9%. In Tarbes, the analysis shows that irrigation events could still be detected even in the presence of abundant rainfall events during the summer season where two and more irrigation events have been detected for 90% of the irrigated plots. The novelty of the proposed method resides in building an effective unsupervised tool for near real-time detection of irrigation events at plot scale independent of the studied geographical context.https://www.mdpi.com/2072-4292/12/9/1456irrigationplot scalenear real-timeSentinel-1 |
| spellingShingle | Hassan Bazzi Nicolas Baghdadi Ibrahim Fayad Mehrez Zribi Hatem Belhouchette Valérie Demarez Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data Remote Sensing irrigation plot scale near real-time Sentinel-1 |
| title | Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data |
| title_full | Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data |
| title_fullStr | Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data |
| title_full_unstemmed | Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data |
| title_short | Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data |
| title_sort | near real time irrigation detection at plot scale using sentinel 1 data |
| topic | irrigation plot scale near real-time Sentinel-1 |
| url | https://www.mdpi.com/2072-4292/12/9/1456 |
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