{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:36:47Z","timestamp":1775666207233,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T00:00:00Z","timestamp":1588118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PRIN 2017","award":["Prot. 2017S559BB"],"award-info":[{"award-number":["Prot. 2017S559BB"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are grown by rows. Indeed, in this framework, the satellite\u2019s output could be biased by intra-row covering, giving inaccurate information about crop status. This paper presents a novel satellite imagery refinement framework, based on a deep learning technique which exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensors. To train the convolutional neural network, only a single UAV-driven dataset is required, making the proposed approach simple and cost-effective. A vineyard in Serralunga d\u2019Alba (Northern Italy) was chosen as a case study for validation purposes. Refined satellite-driven normalized difference vegetation index (NDVI) maps, acquired in four different periods during the vine growing season, were shown to better describe crop status with respect to raw datasets by correlation analysis and ANOVA. In addition, using a K-means based classifier, 3-class vineyard vigor maps were profitably derived from the NDVI maps, which are a valuable tool for growers.<\/jats:p>","DOI":"10.3390\/s20092530","type":"journal-article","created":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T13:23:45Z","timestamp":1588166625000},"page":"2530","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":133,"title":["UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7624-1850","authenticated-orcid":false,"given":"Vittorio","family":"Mazzia","sequence":"first","affiliation":[{"name":"Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy"},{"name":"PIC4SeR, Politecnico Interdepartmental Centre for Service Robotics, 10129 Turin, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5466-0918","authenticated-orcid":false,"given":"Lorenzo","family":"Comba","sequence":"additional","affiliation":[{"name":"Department of Agricultural, Forest and Food Sciences, Universit\u00e0 degli Studi di Torino, Largo Paolo Braccini 2, 10095 Grugliasco (TO), Italy"},{"name":"Institute of Electronics, Computer and Telecommunication Engineering of the National Research Council of Italy, c\/o Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9771-6595","authenticated-orcid":false,"given":"Aleem","family":"Khaliq","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy"},{"name":"PIC4SeR, Politecnico Interdepartmental Centre for Service Robotics, 10129 Turin, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1921-0126","authenticated-orcid":false,"given":"Marcello","family":"Chiaberge","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy"},{"name":"PIC4SeR, Politecnico Interdepartmental Centre for Service Robotics, 10129 Turin, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paolo","family":"Gay","sequence":"additional","affiliation":[{"name":"Department of Agricultural, Forest and Food Sciences, Universit\u00e0 degli Studi di Torino, Largo Paolo Braccini 2, 10095 Grugliasco (TO), Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0065-2113(08)60513-1","article-title":"Aspects of Precision Agriculture","volume":"Volume 67","author":"Pierce","year":"1999","journal-title":"Advances in Agronomy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1007\/s11119-019-09653-x","article-title":"A Systematic literature review of the factors affecting the precision agriculture adoption process","volume":"20","author":"Pathak","year":"2019","journal-title":"Precis. 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