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Combination of remote sensing with crop modeling using Bayesian inferences to predict irrigated cotton yield

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  • Moghbel, Farzam
  • Fazel, Forough
  • Aguilar, Jonathan
  • Howell, Nathan
  • Enciso, Juan

Abstract

The primary goal of this study was to create a Bayesian framework that would incorporate remote sensing data to automatically calibrate the AquaCrop model for simulating cotton responses to irrigation strategies in the northern border of the United States Cotton Belt, which faces a lack of observational data. Multiple regression models (linear and non-linear) were fitted to establish a correlation between cotton canopy cover (CC) values and aerial vegetation indices (EVI, EVI2, MACARI, NDRE, NDVI, NDSVI, OSAVI, and VARI) obtained from sUAS multispectral imagery for 2021 and 2022 growing seasons. The highest correlation was found between RGB-Based VARI index and cotton CC by fitting the linear model (R2 = 0.83 and RMSE = 0.12), which contradicted the results of other studies that emphasized the importance of using red-edge and near-infrared for monitoring crop canopy cover. A considerably less accurate correlation was detected for fitting the polynomial model (0.4

Suggested Citation

  • Moghbel, Farzam & Fazel, Forough & Aguilar, Jonathan & Howell, Nathan & Enciso, Juan, 2025. "Combination of remote sensing with crop modeling using Bayesian inferences to predict irrigated cotton yield," Agricultural Water Management, Elsevier, vol. 317(C).
    • Handle: RePEc:eee:agiwat:v:317:y:2025:i:c:s0378377425003890
    DOI: 10.1016/j.agwat.2025.109675
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