{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:31:51Z","timestamp":1753893111428,"version":"3.41.2"},"reference-count":54,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Remote Sens."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>The Common Agricultural Policy (CAP) is a vital policy framework implemented by the European Union to regulate and support agricultural production within member states. The Land Parcel Identification System (LPIS) is a key component that provides reliable land identification for administrative control procedures. On-the-spot checks (OTSC) are carried out to verify compliance with CAP requirements, typically relying on visual interpretation or field visits. However, the CAP is embracing advanced technologies to enhance its efficiency.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study focuses on using Sentinel-2 time series data and a two-level approach involving recurrent neural networks (RNN) and convolutional neural networks (CNN) to accurately identify permanent pastures.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the first step, using RNN, the model achieved an accuracy of 68%, a precision of 36%, a recall of 97% and a F1-score of 52%, which indicates the model\u2019s ability to identify all the true positive parcels (correctly identified permanent pasture parcels) and minimize the false negative parcels (non-identified permanent pasture parcels). This occurs due to the difficulty in distinguishing between permanent pastures and other similar land covers (such as temporary pastures and shrublands). In the second step, it was possible to distinguish the permanent pasture parcels from the others. The obtained results improved significantly from the first to the second step. Using CNN, an accuracy of 93%, a precision of 89%, and a recall of 98% were achieved for the \u201cPermanent pasture\u201d class. The F1-score was 94%, indicating a balanced measure of the model\u2019s performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>The integration of advanced technologies in the CAP\u2019s control mechanisms, as demonstrated, has the potential to automate the verification of farmers\u2019 declarations and subsequent subsidy payments.<\/jats:p><\/jats:sec>","DOI":"10.3389\/frsen.2024.1459000","type":"journal-article","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T05:10:33Z","timestamp":1729573833000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Permanent pastures identification in Portugal using remote sensing and multi-level machine learning"],"prefix":"10.3389","volume":"5","author":[{"given":"Tiago G.","family":"Morais","sequence":"first","affiliation":[]},{"given":"Tiago","family":"Domingos","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o","family":"Falc\u00e3o","sequence":"additional","affiliation":[]},{"given":"Manuel","family":"Camacho","sequence":"additional","affiliation":[]},{"given":"Ana","family":"Marques","sequence":"additional","affiliation":[]},{"given":"In\u00eas","family":"Neves","sequence":"additional","affiliation":[]},{"given":"Hugo","family":"Lopes","sequence":"additional","affiliation":[]},{"given":"Ricardo F. 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