Use of Copernicus data for developing a geoportal to support agricultural management monitoring in Poland

• Autorzy: Markowska Anna , Paradowski Karol

Identyfikatory

DOI

10.2478/pcr-2025-0002

• Języki publikacji: EN

Abstrakty

EN

This article focuses on mapping agricultural productivity, which can be assessed using vegetation and environmental satellite indices (e.g., NDVI, NDWI). It highlights the main agricultural risks in Poland, namely spring frosts and agricultural droughts. The significance of employing remote sensing indicators to create maps supporting agricultural management at national and voivodeship levels is emphasised. The article demonstrates how such maps can be developed using selected satellite-derived indices for agricultural monitoring across different administrative levels. Furthermore, it discusses access to satellite data from the European Copernicus Programme, including land cover, vegetation condition, and weather data (e.g., ERA5). The paper presents the results of a project undertaken by the Institute of Geodesy and Cartography in Poland (IGiK), which developed a geoportal designed for institutions involved in supporting agricultural development in Poland – KOWR (National Support Centre for Agriculture; in Polish: Krajowy Ośrodek Wsparcia Rolnictwa) and ARMA (Agency for Restructuring and Modernisation of Agriculture; in Polish: Agencja Restrukturyzacji i Modernizacji Rolnictwa).

Słowa kluczowe

EN

geoportal; agricultural map; agriculture; Poland; Copernicus Programme; remote sensing data

• Wydawca: Polskie Towarzystwo Geograficzne. Oddział Kartograficzny
• Czasopismo: Polish Cartographical Review
• Rocznik: 2025
• Tom: Vol. 57, No. 1
• Strony: 21--39
• Opis fizyczny: Bibliogr. 41 poz., mapy., tys., tab., wykr.

Twórcy

autor

Markowska Anna

• Institute of Geodesy and Cartography Warsaw, Poland

• https://orcid.org/0000-0001-8446-6171

autor

Paradowski Karol

• Institute of Geodesy and Cartography Warsaw, Poland

• https://orcid.org/0000-0002-2141-7281

Bibliografia

• Amazon Web Services. (n.d.). https://aws.amazon.com/earth/
• ARMA, Agencja Restrukturyzacji i Modernizacji Rolnictwa. (2023). Pomoc finansowa dla producenta rolnego w związku ze szkodami w uprawach owocujących drzew owocowych, owocujących krzewów owocowych lub truskawek, spowodowanymi wystąpieniem w 2023 r. przymrozków wiosennych, gradu lub huraganu, które objęły co najmniej 30% i mniej niż 70% danej uprawy. https://www.gov.pl/web/arimr/pomoc-finansowa-dla-producentarolnego-w-zwiazku-ze-szkodami-w-uprawachowocujacych-drzew-owocowych-owocujacychkrzewow-owocowych-lub-truskawek-spowodowanymiwystapieniem-w-2023-r-przymrozkow-wiosennychgradu-lub-huraganu-ktore-objely-co-najmniej-30-i-mniej-niz-70-danej-uprawy
• Campbell, J. B., & Wynne, R. H. (2011). Introduction to remote sensing (5th ed.). Guilford Press.
• Chen, P., Liu, H., Wang, Z., Mao, D., Liang, C., Wen, L., Li, Z., Zhang, J., Liu, D., Zhuo, Y., & Wang, L. (2021). Vegetation dynamic assessment by NDVI and field observations for sustainability of China’s Wulagai River Basin. International Journal of Environmental Research and Public Health, 18(5), 2528. https://doi.org/10.3390/ijerph18052528
• Climate Changes. (n.d.). https://climate.copernicus.eu/
• Common System and Platform, Based on Copernicus Data and Services, for Agricultural Agencies in Poland. (n.d.). https://fpcup.pl/agri/
• Copernicus Atmosphere Monitoring Service. (n.d.). https://atmosphere.copernicus.eu/
• Copernicus Contributing Missions. (n.d.). https://dataspace.copernicus.eu/
• Copernicus Emergency Management Service. (n.d.). https://emergency.copernicus.eu/
• Copernicus Land Monitoring Service. (n.d.). https://land.copernicus.eu/en
• Copernicus Marine Service. (n.d.). https://marine.copernicus.eu/
• Copernicus Service on Support to EU External and Security Actions. (n.d.). https://sesa.security.copernicus.eu/
• Copernicus. (n.d.). https://www.copernicus.eu/en
• Dabrowska-Zielinska, K., Malinska, A., Bochenek, Z., Bartold, M., Gurdak, R., Paradowski, K., & Lagiewska, M. (2020). Drought model DISS based on the fusion of satellite and meteorological data under variable climatic conditions. Remote Sensing, 12(18), 2944. https://doi.org/10.3390/rs12182944
• Data and Information Access Services. (n.d.). https://www.copernicus.eu/en/access-data/dias)
• De Sherbinin, A., Reuben, A., Levy, M. A., & Johnson, L. (2013). Indicators in practice: How environmental indicators are being used in policy and management contexts. Center for International Earth Science Information Network; Yale Centre for Environmental Law & Policy.
• Drought Information Satellite System. (n.d.). https://copernicusdroughtservice.fpcup.pl/
• Drygas, M., Nurzyńska, I., Dudek, M., & Wieliczko, B. (2024). Wspólna polityka rolna w Polsce – bilans 20-lecia członkostwa w UE i wyzwania na kolejne lata. Podsumowanie. EFRWP, IRWiR PAN. https://doi.org/10.53098/978-83-967511-4-0
• European Association of Remote Sensing Companies. (2024). European Earth Observation Industry Survey. https://earsc.org/2024/11/18/earsc-industry-survey-2024/
• Framework Partnership Agreement on Copernicus User Uptake. (n.d.). https://www.copernicus-user-uptake.eu/
• Gao, B.-C. (1996). NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
• Geostatistics Portal. (n.d.). https://portal.geo.stat.gov.pl/en/home/
• Google Earth Engine. (n.d.). https://earthengine.google.com/
• Hardisky, M. A., Klemas, V., & Smart, R. M. (1983). The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogrammetric Engineering and Remote Sensing, 48(1), 77–84.
• Jin, H., & Eklundh, L. (2014). A physically based vegetation index for improved monitoring of plant phenology. Remote Sensing of Environment, 152, 512–525. https://doi.org/10.1016/j.rse.2014.07.010
• Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15(11), 91–100. https://doi.org/10.1016/0273-1177(95)00079-T
• Kosztra, B., Büttner, G., Hazeu, G., & Arnold, S. (2017). Updated CLC illustrated nomenclature guidelines. European Environment Agency.
• Local Data Bank. (n.d.). (https://bdl.stat.gov.pl),
• Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2015). Geographic information science and systems (4th ed.). Wiley.
• MacEachren, A. M. (1995). How maps work: Representation, visualization, and design. Guilford Press.
• McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714
• McMaster, G. S., & Wilhelm, W. W. (1997). Growing degree-days: One equation, two interpretations. Agricultural and Forest Meteorology, 87(4), 291–300. https://doi.org/10.1016/S0168-1923(97)00027-0
• Móricz, Á. M., Szeremeta, D., Knaś, M., Długosz, E., Ott, P. G., Kowalska, T., & Sajewicz, M. (2018). Antibacterial potential of the Cistus incanus L. phenolics as studied with use of thin-layer chromatography combined with direct bioautography and in situ hydrolysis. Journal of Chromatography A, 1534, 170178. https://doi.org/10.1016/j.chroma.2017.12.056
• Morse, S. (2011). Harnessing the power of the press with three indices of sustainable development. Ecological Indicators, 11 (6), 1681-1688. https://doi.org/10.1016/j.ecolind.2011.04.016
• Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Souhail Boussetta, Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, J. N., Zsoter, E., Buontempo, C., & Thépaut, J.-N. (2021). ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data, 13(9), 4349-4383. https://doi.org/10.5194/essd-13-4349-2021
• National Drought Mitigation Centre. (n.d). https://www.drought.gov/topics/agriculture
• Ochtyra, A., Marcinkowska-Ochtyra, A., & Raczko, E. (2020). Threshold- and trend-based vegetation change monitoring algorithm based on the inter-annual multi-temporal normalized difference moisture index series: A case study of the Tatra Mountains. Remote Sensing of Environment, 249, Article 112026. https://doi.org/10.1016/j.rse.2020.112026
• Open Data Framework for the Baltic Sea Drainage Basin. (n.d.). https://fpcup.pl/fpcup-baltyk/
• SentinelHub. (n.d.). https://www.sentinel-hub.com/
• Yan, G., Hu, R., Luo, J., Weiss, M., Jiang, H., Mu, X., Xie, D., & Zhang, W. (2019). Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Agricultural and Forest Meteorology, 265, 390-411. https://doi.org/10.1016/j.agrformet.2018.11.033
• Żyszkowska, W., Spallek, W., & Borowicz, D. (2012). Kartografia tematyczna. PWN.