{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T14:43:07Z","timestamp":1777041787149,"version":"3.51.4"},"reference-count":51,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T00:00:00Z","timestamp":1646265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005632","name":"National Centre for Research and Development","doi-asserted-by":"publisher","award":["GOSPOSTRATEG1\/381705\/13\/NCBR\/2018"],"award-info":[{"award-number":["GOSPOSTRATEG1\/381705\/13\/NCBR\/2018"]}],"id":[{"id":"10.13039\/501100005632","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000844","name":"European Space Agency","doi-asserted-by":"publisher","award":["4000123852\/18\/NL\/CBi"],"award-info":[{"award-number":["4000123852\/18\/NL\/CBi"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Timely crop yield forecasts at a national level are substantial to support food policies, to assess agricultural production, and to subsidize regions affected by food shortage. This study presents an operational crop yield forecasting system for Poland that employs freely available satellite and agro-meteorological products provided by the Copernicus programme. The crop yield predictors consist of: (1) Vegetation condition indicators provided daily by Sentinel-3 OLCI (optical) and SLSTR (thermal) imagery, (2) a backward extension of Sentinel-3 data (before 2018) derived from cross-calibrated MODIS data, and (3) air temperature, total precipitation, surface radiation, and soil moisture derived from ERA-5 climate reanalysis generated by the European Centre for Medium-Range Weather Forecasts. The crop yield forecasting algorithm is based on thermal time (growing degree days derived from ERA-5 data) to better follow the crop development stage. The recursive feature elimination is used to derive an optimal set of predictors for each administrative unit, which are ultimately employed by the Extreme Gradient Boosting regressor to forecast yields using official yield statistics as a reference. According to intensive leave-one-year-out cross validation for the 2000\u20132019 period, the relative RMSE for voivodships (NUTS-2) are: 8% for winter wheat, and 13% for winter rapeseed and maize. Respectively, for municipalities (LAU) it equals 14% for winter wheat, 19% for winter rapeseed, and 27% for maize. The system is designed to be easily applicable in other regions and to be easily adaptable to cloud computing environments such as Data and Information Access Services (DIAS) or Amazon AWS, where data sets from the Copernicus programme are directly accessible.<\/jats:p>","DOI":"10.3390\/rs14051238","type":"journal-article","created":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T20:36:30Z","timestamp":1646339790000},"page":"1238","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Integration of Sentinel-3 and MODIS Vegetation Indices with ERA-5 Agro-Meteorological Indicators for Operational Crop Yield Forecasting"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8460-4183","authenticated-orcid":false,"given":"J\u0119drzej S.","family":"Bojanowski","sequence":"first","affiliation":[{"name":"Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sylwia","family":"Sikora","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0034-7827","authenticated-orcid":false,"given":"Jan P.","family":"Musia\u0142","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7717-4848","authenticated-orcid":false,"given":"Edyta","family":"Wo\u017aniak","sequence":"additional","affiliation":[{"name":"Space Research Centre, Polish Academy of Sciences, 00-716 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8928-1942","authenticated-orcid":false,"given":"Katarzyna","family":"D\u0105browska-Zieli\u0144ska","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5438-1281","authenticated-orcid":false,"given":"Przemys\u0142aw","family":"Slesi\u0144ski","sequence":"additional","affiliation":[{"name":"Statistics Poland, 00-925 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomasz","family":"Milewski","sequence":"additional","affiliation":[{"name":"Statistics Poland, 00-925 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Artur","family":"\u0141\u0105czy\u0144ski","sequence":"additional","affiliation":[{"name":"Statistics Poland, 00-925 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111553","DOI":"10.1016\/j.rse.2019.111553","article-title":"Strengthening agricultural decisions in countries at risk of food insecurity: The GEOGLAM Crop Monitor for Early Warning","volume":"237","author":"Justice","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1146\/annurev.environ.041008.093740","article-title":"Crop yield gaps: Their importance, magnitudes, and causes","volume":"34","author":"Lobell","year":"2009","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.fcr.2012.08.008","article-title":"The use of satellite data for crop yield gap analysis","volume":"143","author":"Lobell","year":"2013","journal-title":"Field Crop. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.agee.2017.04.008","article-title":"What crop type for atmospheric carbon sequestration: Results from a global data analysis","volume":"243","author":"Mathew","year":"2017","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9828","DOI":"10.1002\/2015WR017612","article-title":"Impact of switching crop type on water and solute fluxes in deep vadose zone","volume":"51","author":"Turkeltaub","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_6","first-page":"10","article-title":"One-third of our greenhouse gas emissions come from agriculture","volume":"31","author":"Gilbert","year":"2012","journal-title":"Nature"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5989","DOI":"10.1038\/ncomms6989","article-title":"Climate variation explains a third of global crop yield variability","volume":"6","author":"Ray","year":"2015","journal-title":"Nat. Commun."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1038\/s41597-020-0433-7","article-title":"The global dataset of historical yields for major crops 1981\u20132016","volume":"7","author":"Iizumi","year":"2020","journal-title":"Sci. Data"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.rse.2010.01.010","article-title":"A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data","volume":"114","author":"Vermote","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_10","first-page":"600","article-title":"Relationship of spectral data to grain yield variation","volume":"45","author":"Tucker","year":"1980","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1080\/01431160110070744","article-title":"Modelling of crop growth conditions and crop yield in Poland using AVHRR-based indices","volume":"23","author":"Kogan","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/S0167-8809(02)00034-8","article-title":"A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan","volume":"94","author":"Bastiaanssen","year":"2003","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"97","DOI":"10.4141\/P03-070","article-title":"Optimal time for remote sensing to relate to crop grain yield on the Canadian prairies","volume":"84","author":"Basnyat","year":"2004","journal-title":"Can. J. Plant Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2325","DOI":"10.1080\/01431160500034235","article-title":"Modelling corn production in China using AVHRR-based vegetation health indices","volume":"26","author":"Kogan","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","first-page":"26","article-title":"Crop yield estimation model for Iowa using remote sensing and surface parameters","volume":"8","author":"Prasad","year":"2006","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.eja.2006.10.007","article-title":"A simple model of regional wheat yield based on NDVI data","volume":"26","author":"Moriondo","year":"2007","journal-title":"Eur. J. Agron."},{"key":"ref_17","first-page":"228","article-title":"Yield estimation using SPOT-VEGETATION products: A case study of wheat in European countries","volume":"32","author":"Kowalik","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.rse.2013.10.027","article-title":"An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States","volume":"141","author":"Johnson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_19","first-page":"78","article-title":"An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data","volume":"38","author":"Shao","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote sensing for agricultural applications: A meta-review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1080\/22797254.2018.1454265","article-title":"Crop inventory at regional scale in Ukraine: Developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery","volume":"51","author":"Kussul","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rao, P., Zhou, W., Bhattarai, N., Srivastava, A.K., Singh, B., Poonia, S., Lobell, D.B., and Jain, M. (2021). Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms. Remote Sens., 13.","DOI":"10.3390\/rs13101870"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tricht, K.V., Gobin, A., Gilliams, S., and Piccard, I. (2018). Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium. Remote Sens., 10.","DOI":"10.3390\/rs10101642"},{"key":"ref_24","first-page":"112","article-title":"Remote sensing based yield monitoring: Application to winter wheat in United States and Ukraine","volume":"76","author":"Franch","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/S0167-8809(02)00021-X","article-title":"Remote sensing of regional crop production in the Yaqui Valley, Mexico: Estimates and uncertainties","volume":"94","author":"Lobell","year":"2003","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1080\/01431161.2020.1809736","article-title":"Estimating soybean yield using time series of anomalies in vegetation indices from MODIS","volume":"42","author":"Nolasco","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.rse.2004.05.017","article-title":"Crop condition and yield simulations using Landsat and MODIS","volume":"92","author":"Doraiswamy","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_28","unstructured":"Vermote, E. (2020, November 20). MODIS\/Terra Surface Reflectance 8-Day L3 Global 250m SIN Grid V061, Available online: https:\/\/lpdaac.usgs.gov\/products\/mod09q1v061\/."},{"key":"ref_29","unstructured":"Schulzweida, U. (2020, November 20). CDO User Guide. Available online: https:\/\/code.mpimet.mpg.de\/projects\/cdo\/wiki\/Cite."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Theil, H. (1992). A Rank-Invariant Method of Linear and Polynomial Regression Analysis. Advanced Studies in Theoretical and Applied Econometrics, Springer.","DOI":"10.1007\/978-94-011-2546-8_20"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1051\/agro:2001111","article-title":"A methodology for a combined use of normalised difference vegetation index and CORINE land cover data for crop yield monitoring and forecasting. A case study on Spain","volume":"21","author":"Genovese","year":"2001","journal-title":"Agronomie"},{"key":"ref_32","unstructured":"R Core Team (2021). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S1161-0301(00)00058-7","article-title":"Bases and limits to using \u2018degree.day\u2019 units","volume":"13","author":"Bonhomme","year":"2000","journal-title":"Eur. J. Agron."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.1109\/TGRS.2012.2226731","article-title":"Using Thermal Time and Pixel Purity for Enhancing Biophysical Variable Time Series: An Interproduct Comparison","volume":"51","author":"Duveiller","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1744-7348.2005.04088.x","article-title":"Thermal time\u2014Concepts and utility","volume":"146","author":"Trudgill","year":"2005","journal-title":"Ann. Appl. Biol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1175\/1520-0477(1997)078<0621:GDWFS>2.0.CO;2","article-title":"Global Drought Watch from Space","volume":"78","author":"Kogan","year":"1997","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_39","unstructured":"Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., and Zhou, T. (Extreme Gradient Boosting, 2021). Extreme Gradient Boosting, R Package Version 1.5.0.2."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene Selection for Cancer Classification using Support Vector Machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ku\u015bmierek-Tomaszewska, R., and \u017barski, J. (2021). Assessment of Meteorological and Agricultural Drought Occurrence in Central Poland in 1961\u20132020 as an Element of the Climatic Risk to Crop Production. Agriculture, 11.","DOI":"10.3390\/agriculture11090855"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.rse.2015.02.014","article-title":"Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information","volume":"161","author":"Franch","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"108377","DOI":"10.1016\/j.fcr.2021.108377","article-title":"Machine learning for regional crop yield forecasting in Europe","volume":"276","author":"Paudel","year":"2022","journal-title":"Field Crop. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2017.07.015","article-title":"Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications","volume":"199","author":"Veloso","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Skakun, S., Vermote, E., Franch, B., Roger, J.C., Kussul, N., Ju, J., and Masek, J. (2019). Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models. Remote Sens., 11.","DOI":"10.3390\/rs11151768"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Franch, B., Bautista, A.S., Fita, D., Rubio, C., Tarraz\u00f3-Serrano, D., S\u00e1nchez, A., Skakun, S., Vermote, E., Becker-Reshef, I., and Uris, A. (2021). Within-Field Rice Yield Estimation Based on Sentinel-2 Satellite Data. Remote Sens., 13.","DOI":"10.3390\/rs13204095"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"112456","DOI":"10.1016\/j.rse.2021.112456","article-title":"Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe","volume":"260","author":"Tian","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"28","DOI":"10.2307\/1942049","article-title":"Relationships Between NDVI, Canopy Structure, and Photosynthesis in Three Californian Vegetation Types","volume":"5","author":"Gamon","year":"1995","journal-title":"Ecol. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"112708","DOI":"10.1016\/j.rse.2021.112708","article-title":"From parcel to continental scale\u2014A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations","volume":"266","author":"Verhegghen","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_50","first-page":"102683","article-title":"Multi-temporal phenological indices derived from time series Sentinel-1 images to country-wide crop classification","volume":"107","author":"Rybicki","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4023","DOI":"10.1111\/gcb.14302","article-title":"Satellite sun-induced chlorophyll fluorescence detects early response of winter wheat to heat stress in the Indian Indo-Gangetic Plains","volume":"24","author":"Song","year":"2018","journal-title":"Glob. Chang. Biol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1238\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:31:11Z","timestamp":1760135471000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1238"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,3]]},"references-count":51,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14051238"],"URL":"https:\/\/doi.org\/10.3390\/rs14051238","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,3]]}}}