{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T15:41:17Z","timestamp":1776958877521,"version":"3.51.4"},"reference-count":27,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"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"}]},{"DOI":"10.13039\/501100000844","name":"European Space Agency","doi-asserted-by":"publisher","award":["2b0324"],"award-info":[{"award-number":["2b0324"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]},{"name":"EO Network of Resources","award":["4000123852\/18\/NL\/CBi"],"award-info":[{"award-number":["4000123852\/18\/NL\/CBi"]}]},{"name":"EO Network of Resources","award":["2b0324"],"award-info":[{"award-number":["2b0324"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The study explores the feasibility of adapting the EOStat crop monitoring system, originally designed for monitoring crop growth conditions in Poland, to fulfill the requirements of a similar system in Ukraine. The system utilizes satellite data and agrometeorological information provided by the Copernicus program, which offers these resources free of charge. To predict crop yields, the system uses several factors, such as vegetation condition indices obtained from Sentinel-3 Ocean and Land Color Instrument (OLCI) optical and Sea and Land Surface Temperature Radiometer (SLSTR). It also incorporates climate information, including air temperature, total precipitation, surface radiation, and soil moisture. To identify the best predictors for each administrative unit, the study utilizes a recursive feature elimination method and employs the Extreme Gradient Boosting regressor, a machine learning algorithm, to forecast crop yields. The analysis indicates a noticeable decrease in crop losses in 2022 in certain regions of Ukraine, compared to the previous year (2021) and the 5-year average (2017\u20132021), specifically for winter crops and maize. Considering the reduction in yield, it is estimated that the decline in production of winter crops in 2022 was up to 20%, while for maize, it was up to 50% compared to the decline in production.<\/jats:p>","DOI":"10.3390\/s24072257","type":"journal-article","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T01:26:10Z","timestamp":1712021170000},"page":"2257","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Estimates of Crop Yield Anomalies for 2022 in Ukraine Based on Copernicus Sentinel-1, Sentinel-3 Satellite Data, and ERA-5 Agrometeorological Indicators"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5722-3887","authenticated-orcid":false,"given":"Ewa","family":"Panek-Chwastyk","sequence":"first","affiliation":[{"name":"Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland"}]},{"given":"Katarzyna","family":"D\u0105browska-Zieli\u0144ska","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2133-0984","authenticated-orcid":false,"given":"Marcin","family":"Kluczek","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8446-6171","authenticated-orcid":false,"given":"Anna","family":"Markowska","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland"}]},{"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1058-0304","authenticated-orcid":false,"given":"Maciej","family":"Bartold","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8944-6159","authenticated-orcid":false,"given":"Marek","family":"Ruci\u0144ski","sequence":"additional","affiliation":[{"name":"Space Research Centre, Polish Academy of Sciences, 00-716 Warsaw, Poland"}]},{"given":"Cezary","family":"Wojtkowski","sequence":"additional","affiliation":[{"name":"Space Research Centre, Polish Academy of Sciences, 00-716 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8634-436X","authenticated-orcid":false,"given":"Sebastian","family":"Aleksandrowicz","sequence":"additional","affiliation":[{"name":"Space Research Centre, Polish Academy of Sciences, 00-716 Warsaw, Poland"}]},{"given":"Ewa","family":"Gromny","sequence":"additional","affiliation":[{"name":"Space Research Centre, Polish Academy of Sciences, 00-716 Warsaw, Poland"}]},{"given":"Stanis\u0142aw","family":"Lewi\u0144ski","sequence":"additional","affiliation":[{"name":"Space Research Centre, Polish Academy of Sciences, 00-716 Warsaw, Poland"}]},{"given":"Artur","family":"\u0141\u0105czy\u0144ski","sequence":"additional","affiliation":[{"name":"Statistics Poland, 00-925 Warsaw, Poland"}]},{"given":"Svitlana","family":"Masiuk","sequence":"additional","affiliation":[{"name":"State Statistics Service of Ukraine, 01601 Kyiv, Ukraine"}]},{"given":"Olha","family":"Zhurbenko","sequence":"additional","affiliation":[{"name":"State Statistics Service of Ukraine, 01601 Kyiv, Ukraine"}]},{"given":"Tetiana","family":"Trofimchuk","sequence":"additional","affiliation":[{"name":"State Statistics Service of Ukraine, 01601 Kyiv, Ukraine"}]},{"given":"Anna","family":"Burzykowska","sequence":"additional","affiliation":[{"name":"European Space Agency, 00044 Frascati, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bojanowski, J.S., Sikora, S., Musia\u0142, J.P., Wo\u017aniak, E., D\u0105browska-Zieli\u0144ska, K., Slesi\u0144ski, P., Milewski, T., and \u0141\u0105czy\u0144ski, A. (2022). Integration of Sentinel-3 and MODIS Vegetation Indices with ERA-5 Agro-Meteorological Indicators for Operational Crop Yield Forecasting. Remote Sens., 14.","DOI":"10.3390\/rs14051238"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","unstructured":"Panek, E., Gozdowski, D., St\u0119pie\u0144, M., Samborski, S., Ruci\u0144ski, D., and Buszke, B. (2020). Within-Field Relationships between Satellite-Derived Vegetation Indices, Grain Yield and Spike Number of Winter Wheat and Triticale. Agronomy, 10.","DOI":"10.3390\/agronomy10111842"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Panek, E., and Gozdowski, D. (2021). Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries. Agronomy, 11.","DOI":"10.3390\/agronomy11020340"},{"key":"ref_5","first-page":"100286","article-title":"Analysis of Relationship between Cereal Yield and NDVI for Selected Regions of Central Europe Based on MODIS Satellite Data","volume":"17","author":"Panek","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s41324-020-00339-5","article-title":"Evaluation of the Saturation Property of Vegetation Indices Derived from Sentinel-2 in Mixed Crop-Forest Ecosystem","volume":"29","year":"2021","journal-title":"Spat. Inf. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dabrowska-Zielinska, K., Malinska, A., Bochenek, Z., Bartold, M., Gurdak, R., Paradowski, K., and Lagiewska, M. (2020). Drought Model DISS Based on the Fusion of Satellite and Meteorological Data under Variable Climatic Conditions. Remote Sens., 12.","DOI":"10.3390\/rs12182944"},{"key":"ref_8","first-page":"87","article-title":"Monitoring of Agricultural Drought in Poland Using Data Derived from Environmental Satellite Images","volume":"3","author":"Bartold","year":"2011","journal-title":"Geoinf. Issues"},{"key":"ref_9","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_10","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_11","unstructured":"(2023, June 15). Ukraine Production, Available online: https:\/\/ipad.fas.usda.gov\/countrysummary\/Default.aspx?id=UP."},{"key":"ref_12","unstructured":"(2023, June 15). WASDE Report, Available online: https:\/\/www.usda.gov\/oce\/commodity\/wasde."},{"key":"ref_13","unstructured":"Rozwadowski, R., O\u2019Connell, J., Toirov, F., and Voitovska, Y. (2018). The Agriculture Sector in Eastern Ukraine: Analysis and Recommendations. Food Agric. Organ. U. N., Available online: http:\/\/www.fao.org\/3\/i8862en\/I8862EN.pdf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/36.551935","article-title":"An Entropy Based Classification Scheme for Land Applications of Polarimetric SAR","volume":"35","author":"Cloude","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","first-page":"2","article-title":"Cloude Shane The Dual Polarization Entropy\/Alpha Decomposition: A PALSAR Case Study","volume":"644","author":"Cloude","year":"2007","journal-title":"Sci. Appl. SAR Polarim. Polarim. Interferom."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8182","DOI":"10.1080\/01431161.2018.1483084","article-title":"Multi-Temporal Polarimetry in Land-Cover Classification","volume":"39","author":"Kofman","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","first-page":"15","article-title":"Validation of the LAI Biophysical Product Derived from Sentinel-2 and Proba-V Images for Winter Wheat in Western Poland","volume":"9","author":"Bochenek","year":"2017","journal-title":"Geoinf. Issues"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107346","DOI":"10.1016\/j.compag.2022.107346","article-title":"Extreme Gradient Boosting for Yield Estimation Compared with Deep Learning Approaches","volume":"202","author":"Huber","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102418","DOI":"10.1016\/j.foodpol.2023.102418","article-title":"Quantifying War-Induced Crop Losses in Ukraine in near Real Time to Strengthen Local and Global Food Security","volume":"115","author":"Deininger","year":"2022","journal-title":"Food Policy"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"39","DOI":"10.5194\/isprsarchives-XL-7-W3-39-2015","article-title":"Comparison of Biophysical and Satellite Predictors for Wheat Yield Forecasting in Ukraine","volume":"XL-7\/W3","author":"Kolotii","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_22","unstructured":"Ben Aoun, W., Lemoine, G., Cerrani, I., Claverie, M., Nisini Scacchiafichi, L., Panarello, L., and Sedano, S.F. (2023). JRC MARS Bulletin\u2014Global Outlook\u2014Crop Monitoring European Neighbourhood\u2014Ukraine. Publ. Off. Eur. Union."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"108530","DOI":"10.1016\/j.agrformet.2021.108530","article-title":"Optimal county-level crop yield prediction using MODIS-based variables and weather data: A comparative study on machine learning models","volume":"307","author":"Ju","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_25","first-page":"102668","article-title":"Crop yield prediction using MODIS LAI, TIGGE weather forecasts and WOFOST model: A case study for winter wheat in Hebei, China during 2009\u20132013","volume":"106","author":"Zhuo","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1111\/1746-692X.12389","article-title":"The War in Ukraine, Food Security and the Role for Europe","volume":"22","author":"Chepeliev","year":"2023","journal-title":"Eurochoices"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100662","DOI":"10.1016\/j.gfs.2022.100662","article-title":"Needed global wheat stock and crop management in response to the war in Ukraine","volume":"35","author":"Ewert","year":"2022","journal-title":"Glob. Food Secur."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2257\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:22:18Z","timestamp":1760106138000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2257"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,1]]},"references-count":27,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["s24072257"],"URL":"https:\/\/doi.org\/10.3390\/s24072257","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,1]]}}}