Version 1
: Received: 6 December 2021 / Approved: 8 December 2021 / Online: 8 December 2021 (15:42:11 CET)
How to cite:
Bojanowski, J.S.; Sikora, S.; Musiał, J.P.; Woźniak, E.; Dąbrowska-Zielińska, K.; Slesiński, P.; Milewski, T.; Łączyński, A. Integration of Sentinel-3 and MODIS Vegetation Indices with ERA-5 Agro-meteorological Indicators for Operational Crop Yield Forecasting. Preprints2021, 2021120143. https://doi.org/10.20944/preprints202112.0143.v1
Bojanowski, J.S.; Sikora, S.; Musiał, J.P.; Woźniak, E.; Dąbrowska-Zielińska, K.; Slesiński, P.; Milewski, T.; Łączyński, A. Integration of Sentinel-3 and MODIS Vegetation Indices with ERA-5 Agro-meteorological Indicators for Operational Crop Yield Forecasting. Preprints 2021, 2021120143. https://doi.org/10.20944/preprints202112.0143.v1
Bojanowski, J.S.; Sikora, S.; Musiał, J.P.; Woźniak, E.; Dąbrowska-Zielińska, K.; Slesiński, P.; Milewski, T.; Łączyński, A. Integration of Sentinel-3 and MODIS Vegetation Indices with ERA-5 Agro-meteorological Indicators for Operational Crop Yield Forecasting. Preprints2021, 2021120143. https://doi.org/10.20944/preprints202112.0143.v1
APA Style
Bojanowski, J.S., Sikora, S., Musiał, J.P., Woźniak, E., Dąbrowska-Zielińska, K., Slesiński, P., Milewski, T., & Łączyński, A. (2021). Integration of Sentinel-3 and MODIS Vegetation Indices with ERA-5 Agro-meteorological Indicators for Operational Crop Yield Forecasting. Preprints. https://doi.org/10.20944/preprints202112.0143.v1
Chicago/Turabian Style
Bojanowski, J.S., Tomasz Milewski and Artur Łączyński. 2021 "Integration of Sentinel-3 and MODIS Vegetation Indices with ERA-5 Agro-meteorological Indicators for Operational Crop Yield Forecasting" Preprints. https://doi.org/10.20944/preprints202112.0143.v1
Abstract
Timely crop yield forecasts at 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, (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 2000–2019 period, the relative RMSE for NUTS-2 units are: 8% for winter wheat, and 13% for winter rapeseed and maize. Respectively, for the LAU units 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 DIAS or Amazon AWS), where data sets from the Copernicus programme are directly accessible.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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