{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T21:35:06Z","timestamp":1773869706688,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T00:00:00Z","timestamp":1634083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rice is considered one of the most important crops in the world. According to the Food and Agriculture Organization of the United Nations (FAO), rice production has increased significantly (156%) during the last 50 years, with a limited increase in cultivated area (24%). With the recent advances in remote sensing technologies, it is now possible to monitor rice crop production for a better understanding of its management at field scale to ultimately improve rice yields. In this work, we monitor within-field rice production of the two main rice varieties grown in Valencia (Spain) JSendra and Bomba during the 2020 season. The sowing date of both varieties was May 22\u201325, while the harvesting date was September 15\u201317 for Bomba and October 5\u20138 for JSendra. Rice yield data was collected over 66.03 ha (52 fields) by harvesting machines equipped with onboard sensors that determine the dry grain yield within irregular polygons of 3\u20137 m width. This dataset was split in two, selecting 70% of fields for training and 30% for validation purposes. Sentinel-2 surface reflectance spectral data acquired from May until September 2020 was considered over the test area at the two different spatial resolutions of 10 and 20 m. These two datasets were combined assessing the best combination of spectral reflectance bands (SR) or vegetation indices (VIs) as well as the best timing to infer final within-field yields. The results show that SR improves the performance of models with VIs. Furthermore, the correlation of each spectral band and VIs with the final yield changes with the dates and varieties. Considering the training data, the best correlation with the yields is obtained on July 4, with R2 for JSendra of 0.72 at 10 m and 0.76 at 20 m resolution, while the R2 for Bomba is 0.87 at 10 m and 0.92 at 20 m resolution. Based on the validation dataset, the proposed models provide within-field yield modelling Mean Absolute Error (MAE) of 0.254 t\u00d7ha\u22121 (Mean Absolute Percentage Error, MAPE, of 3.73%) for JSendra at 10 m (0.240 t\u00d7ha\u22121; 3.48% at 20 m) and 0.218 t\u00d7ha\u22121 (MAPE 5.82%) for Bomba (0.223 t\u00d7ha\u22121; 5.78% at 20 m) on July 4, that is three months before harvest. At parcel level the model\u2019s MAE is 0.176 t\u00d7ha\u22121 (MAPE 2.61%) for JSendra and 0.142 t\u00d7ha\u22121 (MAPE 4.51%) for Bomba. These results confirm the close correlation between the rice yield and the spectral information from satellite imagery. Additionally, these models provide a timeliness overview of underperforming areas within the field three months before the harvest where farmers can improve their management practices. Furthermore, it highlights the importance of optimum agronomic management of rice plants during the first weeks of rice cultivation (40\u201350 days after sowing) to achieve high yields.<\/jats:p>","DOI":"10.3390\/rs13204095","type":"journal-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T21:48:39Z","timestamp":1634161719000},"page":"4095","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Within-Field Rice Yield Estimation Based on Sentinel-2 Satellite Data"],"prefix":"10.3390","volume":"13","author":[{"given":"Belen","family":"Franch","sequence":"first","affiliation":[{"name":"Global Change Unit, Image Processing Laboratory, Universitat de Val\u00e8ncia, Paterna, 46980 Valencia, Spain"},{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}]},{"given":"Alberto San","family":"Bautista","sequence":"additional","affiliation":[{"name":"Departamento de Producci\u00f3n Vegetal, Universitat Polit\u00e9cnica de Val\u00e8ncia, 46022 Valencia, Spain"}]},{"given":"David","family":"Fita","sequence":"additional","affiliation":[{"name":"Departamento de Producci\u00f3n Vegetal, Universitat Polit\u00e9cnica de Val\u00e8ncia, 46022 Valencia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4395-7473","authenticated-orcid":false,"given":"Constanza","family":"Rubio","sequence":"additional","affiliation":[{"name":"Centro de Tecnolog\u00edas F\u00edsicas, Universitat Polit\u00e9cnica de Val\u00e8ncia, 46022 Valencia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9986-0884","authenticated-orcid":false,"given":"Daniel","family":"Tarraz\u00f3-Serrano","sequence":"additional","affiliation":[{"name":"Centro de Tecnolog\u00edas F\u00edsicas, Universitat Polit\u00e9cnica de Val\u00e8ncia, 46022 Valencia, Spain"}]},{"given":"Antonio","family":"S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9039-0174","authenticated-orcid":false,"given":"Sergii","family":"Skakun","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4883-2765","authenticated-orcid":false,"given":"Eric","family":"Vermote","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"},{"name":"NASA Goddard Space Flight Center Code 619, 8800 Greenbelt Road, Greenbelt, MD 20771, USA"}]},{"given":"Inbal","family":"Becker-Reshef","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}]},{"given":"Antonio","family":"Uris","sequence":"additional","affiliation":[{"name":"Centro de Tecnolog\u00edas F\u00edsicas, Universitat Polit\u00e9cnica de Val\u00e8ncia, 46022 Valencia, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,13]]},"reference":[{"key":"ref_1","unstructured":"FAOSTAT (2021, June 15). 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