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State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter

Zhang, Tianxiang and Su, Jinya and Liu, Cunjia and Chen, Wen-Hua (2021) 'State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter.' Computers and Electronics in Agriculture. ISSN 0168-1699

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Abstract

Crop 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.

Item Type: Article
Uncontrolled Keywords: particle filter, machine learning, multispectral image, UAV
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 06 Nov 2020 10:53
Last Modified: 20 Jan 2021 14:15
URI: http://repository.essex.ac.uk/id/eprint/29043

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