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Article

Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation

1
School of Information Engineering, China University of Geosciences, Beijing 100083, China
2
Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada
*
Author to whom correspondence should be addressed.
Received: 3 January 2019 / Revised: 24 January 2019 / Accepted: 28 January 2019 / Published: 30 January 2019
Precise simulation of crop growth is crucial to yield estimation, agricultural field management, and climate change. Although assimilation of crop model and remote sensing data has been applied in crop growth simulation, few studies have considered optimizing the crop model with respect to phenology. In this study, we assimilated phenological information obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) time series data into the World Food Study (WOFOST) model to improve the accuracy of rice growth simulation at the regional scale. The particle swarm optimization (PSO) algorithm was implemented to optimize the initial phenology development stage (IDVS) and transplanting date (TD) in the WOFOST model by minimizing the difference between simulated and observed phenology, including heading and maturity date. Assimilating phenology improved the accuracy of the rice growth simulation, with correlation coefficients (R) equal to 0.793, 0822, and 0.813 at three fieldwork dates. The performance of the proposed strategy is comparable with that of the enhanced vegetation index (EVI) time series assimilation strategy, with less computation time. Additionally, the result confirms that the proposed strategy could be applied with different spatial resolution images and the difference of simulated LAImean is less than 0.35 in three experimental areas. This study offers a novel assimilation strategy with regard to the phenology development process, which is efficient and scalable for crop growth simulation. View Full-Text
Keywords: data assimilation; WOFOST model; remote sensing penology; rice growth simulation data assimilation; WOFOST model; remote sensing penology; rice growth simulation
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MDPI and ACS Style

Zhou, G.; Liu, X.; Liu, M. Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation. Remote Sens. 2019, 11, 268. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11030268

AMA Style

Zhou G, Liu X, Liu M. Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation. Remote Sensing. 2019; 11(3):268. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11030268

Chicago/Turabian Style

Zhou, Gaoxiang, Xiangnan Liu, and Ming Liu. 2019. "Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation" Remote Sensing 11, no. 3: 268. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11030268

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