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Integration of Sentinel-derived NDVI to reduce uncertainties in the operational field monitoring of maize

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  • Tsakmakis, I.D.
  • Gikas, G.D.
  • Sylaios, G.K.

Abstract

Canopy cover (CC) is a key parameter in calibration and validation of crop growth models, especially those used in operational field monitoring. However, CC direct measurements require intense field campaigns, increasing the cost in time-series data acquisition for large agricultural areas. Normalized Difference Vegetation Index (NDVI) is a commonly used remote-sensing vegetation index, expressing crop water-status, being indirectly related to CC. In this paper, we explore the relationship between on-site CC and the high-resolution NDVI data acquired via Sentinel 2 products. This relationship was utilized to produce CC time series over the cultivation period in four maize fields in northern and central Greece. Subsequently, the expression linking CC and NDVI was used to operationally validate CC change in a crop model capable to simulate the maize growth cycle (AquaCrop). The proposed method involves the dynamic in-season re-adjustment to a number of key model input parameters, based on the remotely acquired CC time series, namely maximum CC, canopy growth and decline coefficient, growing degree days needed to the beginning of senescence stage. These re-adjusted parameters were imported to model’s crop file to improve simulations in CC, soil water content, final biomass and yield. Results showed that the remotely acquired CC time series could be successfully used as an alternative mean to validate CC simulations. Moreover, the ingestion of re-estimated parameters to crop file, improved model’s capability to simulate CC (R2 >0.98; RMSE<5.12%), biomass (Pe<12%) and yield (Pe<12%). No significant differences were observed in model’s performance regarding soil water content simulation.

Suggested Citation

  • Tsakmakis, I.D. & Gikas, G.D. & Sylaios, G.K., 2021. "Integration of Sentinel-derived NDVI to reduce uncertainties in the operational field monitoring of maize," Agricultural Water Management, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:agiwat:v:255:y:2021:i:c:s0378377421002638
    DOI: 10.1016/j.agwat.2021.106998
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    1. Tenreiro, Tomás R. & García-Vila, Margarita & Gómez, José A. & Jimenez-Berni, José A. & Fereres, Elías, 2020. "Water modelling approaches and opportunities to simulate spatial water variations at crop field level," Agricultural Water Management, Elsevier, vol. 240(C).
    2. Battude, Marjorie & Al Bitar, Ahmad & Brut, Aurore & Tallec, Tiphaine & Huc, Mireille & Cros, Jérôme & Weber, Jean-Jacques & Lhuissier, Ludovic & Simonneaux, Vincent & Demarez, Valérie, 2017. "Modeling water needs and total irrigation depths of maize crop in the south west of France using high spatial and temporal resolution satellite imagery," Agricultural Water Management, Elsevier, vol. 189(C), pages 123-136.
    3. Wang, Xingpeng & Wang, Hongbo & Si, Zhuanyun & Gao, Yang & Duan, Aiwang, 2020. "Modelling responses of cotton growth and yield to pre-planting soil moisture with the CROPGRO-Cotton model for a mulched drip irrigation system in the Tarim Basin," Agricultural Water Management, Elsevier, vol. 241(C).
    4. Tsakmakis, I.D. & Kokkos, N.P. & Gikas, G.D. & Pisinaras, V. & Hatzigiannakis, E. & Arampatzis, G. & Sylaios, G.K., 2019. "Evaluation of AquaCrop model simulations of cotton growth under deficit irrigation with an emphasis on root growth and water extraction patterns," Agricultural Water Management, Elsevier, vol. 213(C), pages 419-432.
    5. Sandhu, Rupinder & Irmak, Suat, 2019. "Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    6. Abedinpour, M. & Sarangi, A. & Rajput, T.B.S. & Singh, Man & Pathak, H. & Ahmad, T., 2012. "Performance evaluation of AquaCrop model for maize crop in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 110(C), pages 55-66.
    7. Hassanli, Mohammad & Ebrahimian, Hamed & Mohammadi, Ehsan & Rahimi, Amirreza & Shokouhi, Amirhossein, 2016. "Simulating maize yields when irrigating with saline water, using the AquaCrop, SALTMED, and SWAP models," Agricultural Water Management, Elsevier, vol. 176(C), pages 91-99.
    8. 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.
    9. Katerji, Nader & Campi, Pasquale & Mastrorilli, Marcello, 2013. "Productivity, evapotranspiration, and water use efficiency of corn and tomato crops simulated by AquaCrop under contrasting water stress conditions in the Mediterranean region," Agricultural Water Management, Elsevier, vol. 130(C), pages 14-26.
    10. Conrad, Yvonne & Fohrer, Nicola, 2016. "Simulating impacts of silage maize (Zea mays) in monoculture and undersown with annual grass (Lolium perenne L.) on the soil water balance in a sandy-humic soil in Northwest Germany," Agricultural Water Management, Elsevier, vol. 178(C), pages 52-65.
    11. Malik, Wafa & Isla, Ramon & Dechmi, Farida, 2019. "DSSAT-CERES-maize modelling to improve irrigation and nitrogen management practices under Mediterranean conditions," Agricultural Water Management, Elsevier, vol. 213(C), pages 298-308.
    12. Seyed Ahmadi & Elnaz Mosallaeepour & Ali Kamgar-Haghighi & Ali Sepaskhah, 2015. "Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2837-2853, June.
    13. I. Tsakmakis & N. Kokkos & V. Pisinaras & V. Papaevangelou & E. Hatzigiannakis & G. Arampatzis & G.D. Gikas & R. Linker & S. Zoras & V. Evagelopoulos & V.A. Tsihrintzis & A. Battilani & G. Sylaios, 2017. "Operational Precise Irrigation for Cotton Cultivation through the Coupling of Meteorological and Crop Growth Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 563-580, January.
    14. Zou, Yufeng & Saddique, Qaisar & Ali, Ajaz & Xu, Jiatun & Khan, Muhammad Imran & Qing, Mu & Azmat, Muhammad & Cai, Huanjie & Siddique, Kadambot H.M., 2021. "Deficit irrigation improves maize yield and water use efficiency in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 243(C).
    15. Zhou, Zhenjiang & Plauborg, Finn & Parsons, David & Andersen, Mathias Neumann, 2018. "Potato canopy growth, yield and soil water dynamics under different irrigation systems," Agricultural Water Management, Elsevier, vol. 202(C), pages 9-18.
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