Abstract
This article discusses the topic of the use of application maps in precision agriculture (PA), particularly in the context of grassland management, which accounts for over 21% of utilised agricultural area (UAA) in Poland. New technological developments in the area of smart agriculture (Precision Agriculture, Agriculture 4.0), in terms of sensor technology and information processing, are creating a wide range of data acquisition opportunities to document biological production processes with both high temporal and spatial resolution. That information can be used to rationalise production processes and reduce trade-offs between different environmental services. The technologies that support this kind of research are analyses using satellite imagery, and map-based applications like the system developed in the GRASSAT project are discussed in detail in this article. The developed application provides farmers with information on events using free data from the Copernicus Programme (Sentinel-1, Sentinel-2, ERA5-Land reanalyses). Remote sensing indices, such as the Normalised Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and fresh biomass production volumes, are calculated to show the condition of the green vegetation in the grassland plots. Meteorological risks, such as field freezing, are also presented. The GRASSAT application is available in both desktop and mobile versions.
References
Bajocco S., Ginaldi F., Savian F., Morelli D., Scaglione M., Fanchini D., Raparelli E., Bregaglio S.U.M., 2022. On the use of NDVI to estimate LAI in field crops: Implementing a conversion equation library. Remote Sensing 14(15): 3554. DOI: https://doi.org/10.3390/rs14153554
Bański J., 2010. Atlas of Polish Agriculture. Institute of Geography and Spatial Organization Polish Academy of Science, Warsaw.
C3S [Copernicus Climate Change Service], 2024a. Copernicus climate data store, Online: https://cds.climate.copernicus.eu (accessed 16 April 2024).
C3S [Copernicus Climate Change Service], 2024b. ERA5-Land hourly data from 1950 to present. Online: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=form (accessed 16 April 2024).
Chehbouni J.Q.A., Huete A.R., Kerr Y.H., Sorooshian S., 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment 48(2): 119–126. DOI: https://doi.org/10.1016/0034-4257(94)90134-1
Dąbrowska-Zielińska K., Goliński P., Jørgensen M., Davids C., Persson T., 2021. Tools for information to farmers on grasslands yields under stressed conditions to support management practices – The GrasSAT project. Proceedings of the 21st Symposium of the European Grassland Federation Sensing – New Insights into Grassland Science and Practice, Grassland Science in Europe Vol. 26: 214–216.
Dąbrowska-Zielińska K., Goliński P., Jørgensen M., Mølmann J., Taff G., Tomaszewska M., Golińska B., Budzyńska M., Gatkowska M., 2015. New methodologies for grasslands monitoring. In: Vijay D., Srivastava M.K., Gupta C.K., Malaviya D.R., Roy M.M., Mahanta S.K., Singh J.B., Maity A., Ghosh P.K. (eds), Sustainable use of grassland resources for forage production, biodiversity and environmental protection. Range Management Society of India, Jhansi: 30–40.
Dąbrowska-Zielińska K., Musiał J., Malińska A., Budzyńska M., Gurdak R., Kiryla W., Bartold M., Grzybowski P., 2018. Soil moiamount of fresh (wet) biomass sture in the Biebrza Wetlands retrieved from Sentinel-1 imagery. Remote Sensing 10(12): 1979. DOI: https://doi.org/10.3390/rs10121979
Dąbrowska-Zielińska K., Wróblewski K., Goliński P., Malińska A., Bartold M., Łągiewska M., Kluczek M., Panek-Chwastyk E., Ziółkowski D., Golińska B., Markowska A., Paradowski K., 2024. Integrating copernicus LMS with ground measurements data for leaf area index and biomass assessment for grasslands in Poland and Norway. International Journal of Digital Earth 17(1): 2425165. DOI: https://doi.org/10.1080/17538947.2024.2425165
Dent B.D., Torguson J.S., Hodler T.W., 2009. Cartography: Thematic Map Design, 6th Edn. McGraw Hill, Madison, WI.
EEA [European Environment Agency], 2023. Precision agriculture. Online: https://climate-adapt.eea.europa.eu/en/metadata/adaptation-options/precision-agriculture (accessed 11 April 2024).
Ess D., Morgan M., 2003. The precision-farming guide for agriculturists. Deere & Company, Moline, IL, USA.
Evangelides Ch, Nobajas A., 2020. Red-edge normalised difference vegetation index (NDVI705) from Sentinel-2 imagery to assess post-fire regeneration. Remote Sensing Applications: Society and Environment 17: 100283. DOI: https://doi.org/10.1016/j.rsase.2019.100283
Goliński P., Czerwiński M., Jørgensen M., Mølmann J.A.B., Golińska B., Taff G., 2018. Relationship between climate trends and grassland yield across contrasting European locations. Open Life Sciences 13: 589–598. DOI: https://doi.org/10.1515/biol-2018-0070
Goliński P., Golińska B., 2019. Poland. In: van den Pol-van Dasselaar A., Bastiaansen-Aantjes L.M., Bogue F., O’Donovan M., Huyghe C. (eds), Grassland use in Europe. A syllabus for young farmers. Éditions Quae, Paris: 221231. DOI: https://doi.org/10.35690/978-2-7592-3146-1
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 49(1): 77–83.
Łączyński A. (red.), 2020. Użytkowanie gruntów i powierzchnia zasiewów w 2019 r. Główny Urząd Statystyczny, Warszawa.
Li F., Miao Y., Feng G., Yuan F., Yue S., Gao X., Liu Y., Liu B., Ustin S.L., Chen X., 2014. Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops Research 157: 111–123. DOI: https://doi.org/10.1016/j.fcr.2013.12.018
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. DOI: 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. DOI: https://doi.org/10.1016/S0168-1923(97)00027-0
Móricz ÁM., 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 1534: 170–178. DOI: https://doi.org/10.1016/j.chroma.2017.12.056
Ochtyra A., Marcinkowska-Ochtyra A., Raczko E., 2020. Thresholdand 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: 112026. DOI: https://doi.org/10.1016/j.rse.2020.112026
Pacheco-Labrador J., Perez-Priego O., El-Madany T.S., Julitta T., Rossini M., Guan J., Moreno G., Carvalhais N., Martín M.P., Gonzalez-Cascon R., Gonzalez-Cascon R., Kolle O., Reischtein M., van der Tol Ch, Carrara A., Martini D., Hammer T.W., Moossen H., Migliavacca M., 2019. Multiple-constraint inversion of SCOPE. Evaluating the potential of GPP and SIF for the retrieval of plant functional traits. Remote Sensing of Environment 234: 111362. DOI: https://doi.org/10.1016/j.rse.2019.111362
Pedersen S.M., Lind K.M., 2017. Precision Agriculture: Technology and Economic Perspectives. Springer International Publishing AG, part of Springer Nature, Cham, Switzerland . DOI: https://doi.org/10.1007/978-3-319-68715-5
Punalekar S.M., Verhoef A., Quaife T.L., Humphries D., Bermingham L., Reynolds C.K., 2018. Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model. Remote Sensing of Environment 218: 207–220. DOI: https://doi.org/10.1016/j.rse.2018.09.028
Rains C.R, Thomas D.L., 2009. Precision farming: An introduction. The University of Georgia. Bulletin 1186.
Reinermann S., Asam S., Kuenzer S., 2020. Remote sensing of grassland production and management – A review. Remote Sensing 12(12): 1949. DOI: https://doi.org/10.3390/rs12121949
Tucker C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8(2): 127–150. DOI: https://doi.org/10.1016/0034-4257(79)90013-0
Wang Z., Ma Y., Zhang Y., Shang J., 2022. Review of remote sensing applications in grassland monitoring. Remote Sensing 14: 2903. DOI: https://doi.org/10.3390/rs14122903
WEkEO, 2024. How to use the WEkEO harmonized data access REST API with cURL? Online: https://help.wekeo.eu/en/articles/6771578-how-to-use-the-wekeo-harmonized-data-access-api-rest-with-curl (accessed 16 April 2024).
Witek T., 1973. Mapy glebowo-rolnicze oraz kierunki ich wykorzystania. Instytut Uprawy, Nawożenia i Gleboznawstwa, Puławy.
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. DOI: https://doi.org/10.1016/j.agrformet.2018.11.033
Żyszkowska W., Spallek W., Borowicz D., 2012. Kartografia tematyczna. PWN, Warsaw.
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Copyright (c) 2025 Anna Markowska, Katarzyna Dąbrowska-Zielińska, Konrad Wróblewski, Michał Wyczałek-Jagiełło, Dariusz Ziółkowski, Piotr Goliński

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