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Investigation of coupling DSSAT with SCOPE-RTMo via sensitivity analysis and use of this coupled crop-radiative transfer model for sensitivity-based data assimilation
European Journal of Agronomy ( IF 4.5 ) Pub Date : 2024-11-15 , DOI: 10.1016/j.eja.2024.127431
Amit Weinman, Raphael Linker, Offer Rozenstein

The increasing availability of remote sensing (RS) data and the advancement of computation abilities, combined with the demands for enhancing crop production, encourages the creation of a framework in which crop growth simulation can be updated sequentially to serve as a yield predictor and be part of a decision support system. However, crop model outputs and RS data must be linked via a radiative transfer model (RTM), which simulates the interaction between the crop and the intercepted radiation. In this study, a comprehensive coupling scheme between a crop model (DSSAT-CROPGRO-tomato) and an RTM (SCOPE-RTMo) was formulated and investigated through global sensitivity analysis (SA) and by testing the coupled model in a synthetic data assimilation (DA) experiment. The DA experiment utilized a sensitivity-based particle filter (PF) in which the SA results were used to enhance the PF convergence rate and accuracy. The SA results provide the sensitivity of simulated reflectance at different wavelengths to DSSAT-CROPGRO parameters throughout the season. This information can help guide future data assimilation experiments by choosing imaging instruments with appropriate spectral bands and timing the measurements to enhance model calibration. The results of the synthetic DA experiment showed a good convergence of the particle filter towards the ground truth. The results also demonstrated the strong relation between LAI and reflectance, as several model runs with different initial values of DSSAT-CROPGRO parameters all converged and predicted the synthetic LAI observations very well. The convergence of DSSAT-CROPGRO parameters to their ground truth values was only partial, and phenology-related parameters tended to converge better than growth-related parameters.
更新日期:2024-11-15
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