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

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journal contribution
posted on 11.01.2021, 10:20 by Tianxiang Zhang, Jinya Su, Cunjia Liu, Wen-Hua Chen
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.

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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History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Computers and Electronics in Agriculture

Volume

180

Publisher

Elsevier

Version

VoR (Version of Record)

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

20/11/2020

Publication date

2020-12-23

Copyright date

2021

ISSN

0168-1699

Language

en

Depositor

Mr Tianxiang Zhang. Deposit date: 8 December 2020

Article number

105909