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Assessment of Grassland Biomass Prediction Using AquaCrop Model: Integrating Sentinel-2 Data and Ground Measurements in Wielkopolska and Podlasie Regions, Poland

Author

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  • Ewa Panek-Chwastyk

    (Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland)

  • Ceren Nisanur Ozbilge

    (Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland)

  • Katarzyna Dąbrowska-Zielińska

    (Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland)

  • Konrad Wróblewski

    (Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland)

Abstract

This study aimed to compare remotely sensed data with in situ data using the AquaCrop simulation model for accurately monitoring growth conditions and predict grassland biomass in the north-eastern and central-western regions of Poland from 2020 to 2022. The model was calibrated using input data, including daily climate parameters from the ERA5-Land Daily Aggregated dataset, crop characteristics (initial canopy cover, maximum canopy cover, and harvest index), and soil characteristics. Additionally, parameters such as the leaf area index (LAI), soil texture classes, and plant growth stages were obtained through field campaigns. The grassland’s biomass simulation results indicate that the root mean square error (RMSE) values for the north-eastern region ranged from 0.12 to 0.35 t·ha −1 , while for the central-western region, they ranged from 0.07 to 0.12 t·ha −1 . Overall, the outcomes obtained from Sentinel-2 data perform comparably to the in situ measurements, and in some instances, even yield superior results. This study contributes valuable insights into grass production management on farms, providing essential information and tools for managers to better understand grass growth and development.

Suggested Citation

  • Ewa Panek-Chwastyk & Ceren Nisanur Ozbilge & Katarzyna Dąbrowska-Zielińska & Konrad Wróblewski, 2024. "Assessment of Grassland Biomass Prediction Using AquaCrop Model: Integrating Sentinel-2 Data and Ground Measurements in Wielkopolska and Podlasie Regions, Poland," Agriculture, MDPI, vol. 14(6), pages 1-16, May.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:6:p:837-:d:1402788
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    References listed on IDEAS

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    1. Raes, Dirk & Fereres, Elias & García Vila, Margarita & Curnel, Yannick & Knoden, David & Çelik, Sema Kale & Ucar, Yusuf & Türk, Mevlüt & Wellens, Joost, 2023. "Simulation of alfalfa yield with AquaCrop," Agricultural Water Management, Elsevier, vol. 284(C).
    2. Umesh, Barikara & Reddy, K.S. & Polisgowdar, B.S. & Maruthi, V. & Satishkumar, U. & Ayyanagoudar, M.S. & Rao, Sathyanarayan & Veeresh, H., 2022. "Assessment of climate change impact on maize (Zea mays L.) through aquacrop model in semi-arid alfisol of southern Telangana," Agricultural Water Management, Elsevier, vol. 274(C).
    3. Mohamed Sallah, Abdoul-Hamid & Tychon, Bernard & Piccard, Isabelle & Gobin, Anne & Van Hoolst, Roel & Djaby, Bakary & Wellens, Joost, 2019. "Batch-processing of AquaCrop plug-in for rainfed maize using satellite derived Fractional Vegetation Cover data," Agricultural Water Management, Elsevier, vol. 217(C), pages 346-355.
    4. Zhang, Ting & Zuo, Qiang & Ma, Ning & Shi, Jianchu & Fan, Yuchuan & Wu, Xun & Wang, Lichun & Xue, Xuzhang & Ben-Gal, Alon, 2023. "Optimizing relative root-zone water depletion thresholds to maximize yield and water productivity of winter wheat using AquaCrop," Agricultural Water Management, Elsevier, vol. 286(C).
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