Sentinel-2 Enables Nationwide Monitoring of Single Area Payment Scheme and Greening Agricultural Subsidies in Hungary
The verification and monitoring of agricultural subsidy claims requires combined evaluation of several criteria at the scale of over a million cultivation units. Sentinel-2 satellite imagery is a promising data source and paying agencies are encouraged to test their pre-operational use. Here, we pre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/16/3917 |
| _version_ | 1797442300846014464 |
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| author | László Henits Ákos Szerletics Dávid Szokol Gergely Szlovák Emese Gojdár András Zlinszky |
| author_facet | László Henits Ákos Szerletics Dávid Szokol Gergely Szlovák Emese Gojdár András Zlinszky |
| author_sort | László Henits |
| collection | DOAJ |
| description | The verification and monitoring of agricultural subsidy claims requires combined evaluation of several criteria at the scale of over a million cultivation units. Sentinel-2 satellite imagery is a promising data source and paying agencies are encouraged to test their pre-operational use. Here, we present the outcome of the Hungarian agricultural subsidy monitoring pilot: our goal was to propose a solution based on open-source components and evaluate the main strengths and weaknesses for Sentinel-2 in the framework of a complex set of tasks. These include the checking of the basic cultivation of grasslands and arable land and compliance to the criteria of ecological focus areas. The processing of the satellite data was conducted based on random forest for crop classification and the detection of cultivation events was conducted based on NDVI (Normalized Differential Vegetation Index) time series analysis results. The outputs of these processes were combined in a decision tree ruleset to provide the final results. We found that crop classification provided good performance (overall accuracy 88%) for 22 vegetation classes and cultivation detection was also reliable when compared to on-screen visual interpretation. The main limitation was the size of fields, which were frequently small compared to the spatial resolution of the images: more than 4% of the parcels had to be excluded, although these represent less than 3% of the cultivated area of Hungary. Based on these results, we find that operational satellite-based monitoring is feasible for Hungary, and expect further improvements from integration with Sentinel-1 due to additional temporal resolution. |
| first_indexed | 2024-03-09T12:39:51Z |
| format | Article |
| id | doaj.art-671db51b8d56400db9181d8f0b18de31 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T12:39:51Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-671db51b8d56400db9181d8f0b18de312023-11-30T22:19:06ZengMDPI AGRemote Sensing2072-42922022-08-011416391710.3390/rs14163917Sentinel-2 Enables Nationwide Monitoring of Single Area Payment Scheme and Greening Agricultural Subsidies in HungaryLászló Henits0Ákos Szerletics1Dávid Szokol2Gergely Szlovák3Emese Gojdár4András Zlinszky5Ulyssys Software Development and Consulting Ltd., 1024 Budapest, HungaryDepartment for Direct Payments, Hungarian State Treasury, 1095 Budapest, HungaryUlyssys Software Development and Consulting Ltd., 1024 Budapest, HungaryUlyssys Software Development and Consulting Ltd., 1024 Budapest, HungaryUlyssys Software Development and Consulting Ltd., 1024 Budapest, HungaryUlyssys Software Development and Consulting Ltd., 1024 Budapest, HungaryThe verification and monitoring of agricultural subsidy claims requires combined evaluation of several criteria at the scale of over a million cultivation units. Sentinel-2 satellite imagery is a promising data source and paying agencies are encouraged to test their pre-operational use. Here, we present the outcome of the Hungarian agricultural subsidy monitoring pilot: our goal was to propose a solution based on open-source components and evaluate the main strengths and weaknesses for Sentinel-2 in the framework of a complex set of tasks. These include the checking of the basic cultivation of grasslands and arable land and compliance to the criteria of ecological focus areas. The processing of the satellite data was conducted based on random forest for crop classification and the detection of cultivation events was conducted based on NDVI (Normalized Differential Vegetation Index) time series analysis results. The outputs of these processes were combined in a decision tree ruleset to provide the final results. We found that crop classification provided good performance (overall accuracy 88%) for 22 vegetation classes and cultivation detection was also reliable when compared to on-screen visual interpretation. The main limitation was the size of fields, which were frequently small compared to the spatial resolution of the images: more than 4% of the parcels had to be excluded, although these represent less than 3% of the cultivated area of Hungary. Based on these results, we find that operational satellite-based monitoring is feasible for Hungary, and expect further improvements from integration with Sentinel-1 due to additional temporal resolution.https://www.mdpi.com/2072-4292/14/16/3917agriculture monitoringCommon Agricultural Policymachine learningrandom forestcrop classificationmowing detection |
| spellingShingle | László Henits Ákos Szerletics Dávid Szokol Gergely Szlovák Emese Gojdár András Zlinszky Sentinel-2 Enables Nationwide Monitoring of Single Area Payment Scheme and Greening Agricultural Subsidies in Hungary Remote Sensing agriculture monitoring Common Agricultural Policy machine learning random forest crop classification mowing detection |
| title | Sentinel-2 Enables Nationwide Monitoring of Single Area Payment Scheme and Greening Agricultural Subsidies in Hungary |
| title_full | Sentinel-2 Enables Nationwide Monitoring of Single Area Payment Scheme and Greening Agricultural Subsidies in Hungary |
| title_fullStr | Sentinel-2 Enables Nationwide Monitoring of Single Area Payment Scheme and Greening Agricultural Subsidies in Hungary |
| title_full_unstemmed | Sentinel-2 Enables Nationwide Monitoring of Single Area Payment Scheme and Greening Agricultural Subsidies in Hungary |
| title_short | Sentinel-2 Enables Nationwide Monitoring of Single Area Payment Scheme and Greening Agricultural Subsidies in Hungary |
| title_sort | sentinel 2 enables nationwide monitoring of single area payment scheme and greening agricultural subsidies in hungary |
| topic | agriculture monitoring Common Agricultural Policy machine learning random forest crop classification mowing detection |
| url | https://www.mdpi.com/2072-4292/14/16/3917 |
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