Combination of remote sensing with crop modeling using Bayesian inferences to predict irrigated cotton yield - Farzam Moghbel; Forough Fazel; Jonathan Aguilar; Nathan Howell; Juan Enciso (Agricultural Water Management, 2025)
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 <RMSE<4.2). Furthermore, the MCARI index was found unsuitable for cotton monitoring under water stress conditions. Afterward, the Bayesian theorem-based Generalized Likelihood Uncertainty Estimation (GLUE) algorithm was linked to AquaCrop in the R environment to calibrate the model based on the remotely sensed CCs by seeking posterior distributions of the parameters through the Monte Carlo approach. Then, the model was validated for its key outputs, including cotton biomass, yield, and soil water content. The simulated CC results showed the model's automatic calibration success. The best performances of the model were found for simulating cotton biomass under 70 % and 80 % deficit irrigation conditions Pe = 0.88 % and −0.38 %) in 2022 and full irrigation conditions in 2021 Pe = 3.19 %); however, the biomass simulations were satisfactorily under all irrigation conditions. The outstanding performance of the AquaCrop was confirmed for reproducing cotton yield values regardless of irrigation conditions. The accurate retrieval of soil water dynamics by the model can introduce the framework created in this study as a robust tool to derive soil water content at cotton rootzone by having access to aerial RGB images. Overall, the findings of this study revealed that by supplying the introduced framework with one seasonal remote sensing data, the AquaCrop could successfully be turned into a decision support system for exploring irrigation scheduling strategies for producers of cotton in Kansas.