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

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...
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Autor*in:	Gaoxiang Zhou [verfasserIn] Xiangnan Liu [verfasserIn] Ming Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	data assimilation WOFOST model remote sensing penology rice growth simulation

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 11(2019), 3, p 268
Übergeordnetes Werk:	volume:11 ; year:2019 ; number:3, p 268

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs11030268

Katalog-ID:	DOAJ069274460

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source	In Remote Sensing 11(2019), 3, p 268 volume:11 year:2019 number:3, p 268
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author	Gaoxiang Zhou
spellingShingle	Gaoxiang Zhou misc data assimilation misc WOFOST model misc remote sensing penology misc rice growth simulation misc Science misc Q Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation
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topic_title	Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation data assimilation WOFOST model remote sensing penology rice growth simulation
topic	misc data assimilation misc WOFOST model misc remote sensing penology misc rice growth simulation misc Science misc Q
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title	Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation
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title_sort	assimilating remote sensing phenological information into the wofost model for rice growth simulation
title_auth	Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation
abstract	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 LAI<sub<mean</sub< 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.
abstractGer	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 LAI<sub<mean</sub< 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.
abstract_unstemmed	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 LAI<sub<mean</sub< 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.
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title_short	Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation
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