LIU_1-s2.0-S0168169920331148-main.pdf (5.7 MB)
State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter
journal contribution
posted on 2021-01-11, 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
Department for Business, Energy and Industrial Strategy
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School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Aeronautical and Automotive Engineering
Published in
Computers and Electronics in AgricultureVolume
180Publisher
ElsevierVersion
- 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
2020-11-20Publication date
2020-12-23Copyright date
2021ISSN
0168-1699Publisher version
Language
- en
Depositor
Tianxiang Zhang. Deposit date: 8 December 2020Article number
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