Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring
In this upcoming Common Agricultural Policy (CAP) reform, the use of satellite imagery is taking an increasing role for improving the Integrated Administration and Control System (IACS). Considering the operational aspect of the CAP monitoring process, the use of Sentinel-1 SAR (Synthetic Aperture R...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/14/2785 |
| _version_ | 1827686967308976128 |
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| author | Emilie Beriaux Alban Jago Cozmin Lucau-Danila Viviane Planchon Pierre Defourny |
| author_facet | Emilie Beriaux Alban Jago Cozmin Lucau-Danila Viviane Planchon Pierre Defourny |
| author_sort | Emilie Beriaux |
| collection | DOAJ |
| description | In this upcoming Common Agricultural Policy (CAP) reform, the use of satellite imagery is taking an increasing role for improving the Integrated Administration and Control System (IACS). Considering the operational aspect of the CAP monitoring process, the use of Sentinel-1 SAR (Synthetic Aperture Radar) images is highly relevant, especially in regions with a frequent cloud cover, such as Belgium. Indeed, SAR imagery does not depend on sunlight and is barely affected by the presence of clouds. Moreover, the SAR signal is particularly sensitive to the geometry and the water content of the target. Crop identification is often a pre-requisite to monitor agriculture at parcel level (ploughing, harvest, grassland mowing, intercropping, etc.) The main goal of this study is to assess the performances and constraints of a SAR-based crop classification in an operational large-scale application. The Random Forest object-oriented classification model is built on Sentinel-1 time series from January to August 2020 only. It can identify crops in the Walloon Region (south part of Belgium) with high performance: 93.4% of well-classified area, representing 88.4% of the parcels. Among the 48 crop groups, the six most represented ones get a F1-score higher or equal to 84%. Additionally, this research documents how the classification performance is affected by different parameters: the SAR orbit, the size of the training dataset, the use of different internal buffers on parcel polygons before signal extraction, the set of explanatory variables, and the period of the time series. In an operational context, this allows to choose the right balance between classification accuracy and model complexity. A key result is that using a training dataset containing only 3.2% of the total number of parcels allows to correctly classify 91.7% of the agricultural area. The impact of rain and snow is also discussed. Finally, this research analyses how the classification accuracy depends on some characteristics of the parcels like their shape or size. This allows to assess the relevance of the classification depending on those characteristics, as well as to identify a subset of parcels for which the global accuracy is higher. |
| first_indexed | 2024-03-10T09:25:08Z |
| format | Article |
| id | doaj.art-d8b75002d6fb4639a98a0f2d583c55be |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T09:25:08Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d8b75002d6fb4639a98a0f2d583c55be2023-11-22T04:52:25ZengMDPI AGRemote Sensing2072-42922021-07-011314278510.3390/rs13142785Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP MonitoringEmilie Beriaux0Alban Jago1Cozmin Lucau-Danila2Viviane Planchon3Pierre Defourny4Agriculture, Territory and Technologies Integration Unit, Production in Agriculture Department, Walloon Agricultural Research Centre, rue de Liroux 9, 5030 Gembloux, BelgiumAgriculture, Territory and Technologies Integration Unit, Production in Agriculture Department, Walloon Agricultural Research Centre, rue de Liroux 9, 5030 Gembloux, BelgiumAgriculture, Territory and Technologies Integration Unit, Production in Agriculture Department, Walloon Agricultural Research Centre, rue de Liroux 9, 5030 Gembloux, BelgiumAgriculture, Territory and Technologies Integration Unit, Production in Agriculture Department, Walloon Agricultural Research Centre, rue de Liroux 9, 5030 Gembloux, BelgiumEarth and Life Institute, Université Catholique de Louvain, Croix du Sud 2, 1348 Louvain-la-Neuve, BelgiumIn this upcoming Common Agricultural Policy (CAP) reform, the use of satellite imagery is taking an increasing role for improving the Integrated Administration and Control System (IACS). Considering the operational aspect of the CAP monitoring process, the use of Sentinel-1 SAR (Synthetic Aperture Radar) images is highly relevant, especially in regions with a frequent cloud cover, such as Belgium. Indeed, SAR imagery does not depend on sunlight and is barely affected by the presence of clouds. Moreover, the SAR signal is particularly sensitive to the geometry and the water content of the target. Crop identification is often a pre-requisite to monitor agriculture at parcel level (ploughing, harvest, grassland mowing, intercropping, etc.) The main goal of this study is to assess the performances and constraints of a SAR-based crop classification in an operational large-scale application. The Random Forest object-oriented classification model is built on Sentinel-1 time series from January to August 2020 only. It can identify crops in the Walloon Region (south part of Belgium) with high performance: 93.4% of well-classified area, representing 88.4% of the parcels. Among the 48 crop groups, the six most represented ones get a F1-score higher or equal to 84%. Additionally, this research documents how the classification performance is affected by different parameters: the SAR orbit, the size of the training dataset, the use of different internal buffers on parcel polygons before signal extraction, the set of explanatory variables, and the period of the time series. In an operational context, this allows to choose the right balance between classification accuracy and model complexity. A key result is that using a training dataset containing only 3.2% of the total number of parcels allows to correctly classify 91.7% of the agricultural area. The impact of rain and snow is also discussed. Finally, this research analyses how the classification accuracy depends on some characteristics of the parcels like their shape or size. This allows to assess the relevance of the classification depending on those characteristics, as well as to identify a subset of parcels for which the global accuracy is higher.https://www.mdpi.com/2072-4292/13/14/2785Sentinel-1SARmultitemporal analysiscrop identificationparcel-based classificationremote sensing |
| spellingShingle | Emilie Beriaux Alban Jago Cozmin Lucau-Danila Viviane Planchon Pierre Defourny Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring Remote Sensing Sentinel-1 SAR multitemporal analysis crop identification parcel-based classification remote sensing |
| title | Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring |
| title_full | Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring |
| title_fullStr | Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring |
| title_full_unstemmed | Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring |
| title_short | Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring |
| title_sort | sentinel 1 time series for crop identification in the framework of the future cap monitoring |
| topic | Sentinel-1 SAR multitemporal analysis crop identification parcel-based classification remote sensing |
| url | https://www.mdpi.com/2072-4292/13/14/2785 |
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