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Download fileState and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter
journal contribution
posted on 11.01.2021, 10:20 authored by Tianxiang Zhang, Jinya Su, Cunjia LiuCunjia Liu, Wen-Hua ChenWen-Hua ChenCrop models play a paramount role in providing quantitative information on crop growth and field management. However, its prediction performance degrades significantly in the presence of unknown, uncertain parameters and noisy measurements. Consequently, simultaneous state and parameter estimation (SSPE) for crop model is required to maximize its potentials. This work aims to develop an integrated dynamic SSPE framework for the AquaCrop model by leveraging constrained particle filter, crop sensitivity analysis and UAV remote sensing. Both Monte Carlo simulation and one winter wheat experimental case study are performed to validate the proposed framework. It is shown that: (i) the proposed framework with state/parameter bound and parameter sensitivity information outperforms conventional particle filter and constrained particle filter in both state and parameter estimation in Monte Carlo simulations; (ii) in real-world experiment, the proposed approach achieves the smallest root mean squared error for canopy cover estimation among the three algorithms by using day forward-chaining validation method.
Funding
Enabling wide area persistent remote sensing for agriculture applications by developing and coordinating multiple heterogeneous platforms
Science and Technology Facilities Council
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School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Aeronautical and Automotive Engineering