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Estimates of Crop Yield Anomalies for 2022 in Ukraine Based on Copernicus Sentinel-1, Sentinel-3 Satellite Data, and ERA-5 Agrometeorological Indicators.
Panek-Chwastyk, Ewa; Dabrowska-Zielinska, Katarzyna; Kluczek, Marcin; Markowska, Anna; Wozniak, Edyta; Bartold, Maciej; Rucinski, Marek; Wojtkowski, Cezary; Aleksandrowicz, Sebastian; Gromny, Ewa; Lewinski, Stanislaw; Laczynski, Artur; Masiuk, Svitlana; Zhurbenko, Olha; Trofimchuk, Tetiana; Burzykowska, Anna.
Afiliação
  • Panek-Chwastyk E; Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland.
  • Dabrowska-Zielinska K; Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland.
  • Kluczek M; Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland.
  • Markowska A; Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland.
  • Wozniak E; Space Research Centre, Polish Academy of Sciences, 00-716 Warsaw, Poland.
  • Bartold M; Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland.
  • Rucinski M; Space Research Centre, Polish Academy of Sciences, 00-716 Warsaw, Poland.
  • Wojtkowski C; Space Research Centre, Polish Academy of Sciences, 00-716 Warsaw, Poland.
  • Aleksandrowicz S; Space Research Centre, Polish Academy of Sciences, 00-716 Warsaw, Poland.
  • Gromny E; Space Research Centre, Polish Academy of Sciences, 00-716 Warsaw, Poland.
  • Lewinski S; Space Research Centre, Polish Academy of Sciences, 00-716 Warsaw, Poland.
  • Laczynski A; Statistics Poland, 00-925 Warsaw, Poland.
  • Masiuk S; State Statistics Service of Ukraine, 01601 Kyiv, Ukraine.
  • Zhurbenko O; State Statistics Service of Ukraine, 01601 Kyiv, Ukraine.
  • Trofimchuk T; State Statistics Service of Ukraine, 01601 Kyiv, Ukraine.
  • Burzykowska A; European Space Agency, 00044 Frascati, Italy.
Sensors (Basel) ; 24(7)2024 Apr 01.
Article em En | MEDLINE | ID: mdl-38610468
ABSTRACT
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 (2017-2021), 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.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article