{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T01:27:18Z","timestamp":1780363638043,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T00:00:00Z","timestamp":1661472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Support to Development of a Rural Business Information System (RBIS)","award":["ENI\/2019\/412-386"],"award-info":[{"award-number":["ENI\/2019\/412-386"]}]},{"name":"European Union funded Integrated Regional Development of Azerbaijan (IRDA)","award":["ENI\/2019\/412-386"],"award-info":[{"award-number":["ENI\/2019\/412-386"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper presents the development and update of a multi-scale yield prediction model for processing tomatoes. The study was carried out under the EU-funded programme \u201cSupport to Development of a Rural Business Information System (RBIS)\u201d, and the performance of the updated crop-specific yield prediction models and their generated predictions at regional and national levels are presented. The model was built using Sentinel-2 satellite imagery to obtain cumulative values of six (6) selected vegetation indices (VIs). The data were collected on five (5) different dates for processing tomato fields in the Khachmaz region of Azerbaijan during summer 2021 (June to August) at 10- to 13-day intervals. In addition, a targeted field sampling campaign was conducted on selected Khachmaz pilot fields towards the end of the growing season to assess the potential of Sentinel-2 data to determine yield variability in tomato fields. Finally, actual recorded yields were collected at the field level to build the yield prediction regression model and evaluate its performance at different spatial scales, ranging from single field to national level, as well as under different data availability scenarios (number of consecutive Sentinel-2 images used). The results showed a high degree of correlation between all implemented VIs and processing tomato yield, with a coefficient of determination of up to 0.89 for the NDVI, providing valuable information for future estimates of tomato production across multiple spatial scales. The developed prediction model could also be used in the agri-food sector for national yield estimates to support policy and regulatory decisions at the national level.<\/jats:p>","DOI":"10.3390\/rs14174202","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T01:37:55Z","timestamp":1661823475000},"page":"4202","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Development of a Multi-Scale Tomato Yield Prediction Model in Azerbaijan Using Spectral Indices from Sentinel-2 Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2046-7674","authenticated-orcid":false,"given":"Vasilis","family":"Psiroukis","sequence":"first","affiliation":[{"name":"Laboratory of Agricultural Engineering, Department of Natural Resources Management & Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4130-7303","authenticated-orcid":false,"given":"Nicoleta","family":"Darra","sequence":"additional","affiliation":[{"name":"Laboratory of Agricultural Engineering, Department of Natural Resources Management & Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7620-3782","authenticated-orcid":false,"given":"Aikaterini","family":"Kasimati","sequence":"additional","affiliation":[{"name":"Laboratory of Agricultural Engineering, Department of Natural Resources Management & Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pavel","family":"Trojacek","sequence":"additional","affiliation":[{"name":"International Development Ireland, The Courtyard Building, Carmanhall Road, Sandyford, D18 HP90 Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gunay","family":"Hasanli","sequence":"additional","affiliation":[{"name":"Agricultural Economics Research Center, Nizami Street 92, AZ1016 Baku, Azerbaijan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Spyros","family":"Fountas","sequence":"additional","affiliation":[{"name":"Laboratory of Agricultural Engineering, Department of Natural Resources Management & Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,26]]},"reference":[{"key":"ref_1","unstructured":"Kerimov, N. 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