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Finding the Key Periods for Assimilating HJ-1A/B CCD Data and the WOFOST Model to Evaluate Heavy Metal Stress in Rice

Accurately monitoring heavy metal stress in crops is vital for food security and agricultural production. The assimilation of remote sensing images into the World Food Studies (WOFOST) model provides an efficient way to solve this problem. In this study, we aimed at investigating the key periods of...

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Autores principales: Zhao, Shuang, Qian, Xu, Liu, Xiangnan, Xu, Zhao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948729/
https://www.ncbi.nlm.nih.gov/pubmed/29673178
http://dx.doi.org/10.3390/s18041230
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author Zhao, Shuang
Qian, Xu
Liu, Xiangnan
Xu, Zhao
author_facet Zhao, Shuang
Qian, Xu
Liu, Xiangnan
Xu, Zhao
author_sort Zhao, Shuang
collection PubMed
description Accurately monitoring heavy metal stress in crops is vital for food security and agricultural production. The assimilation of remote sensing images into the World Food Studies (WOFOST) model provides an efficient way to solve this problem. In this study, we aimed at investigating the key periods of the assimilation framework for continuous monitoring of heavy metal stress in rice. The Harris algorithm was used for the leaf area index (LAI) curves to select the key period for an optimized assimilation. To obtain accurate LAI values, the measured dry weight of rice roots (WRT), which have been proven to be the most stress-sensitive indicator of heavy metal stress, were incorporated into the improved WOFOST model. Finally, the key periods, which contain four dominant time points, were used to select remote sensing images for the RS-WOFOST model for continuous monitoring of heavy metal stress. Compared with the key period which contains all the available remote sensing images, the results showed that the optimal key period can significantly improve the time efficiency of the assimilation framework by shortening the model operation time by more than 50%, while maintaining its accuracy. This result is highly significant when monitoring heavy metals in rice on a large-scale. Furthermore, it can also offer a reference for the timing of field measurements in monitoring heavy metal stress in rice.
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spelling pubmed-59487292018-05-17 Finding the Key Periods for Assimilating HJ-1A/B CCD Data and the WOFOST Model to Evaluate Heavy Metal Stress in Rice Zhao, Shuang Qian, Xu Liu, Xiangnan Xu, Zhao Sensors (Basel) Article Accurately monitoring heavy metal stress in crops is vital for food security and agricultural production. The assimilation of remote sensing images into the World Food Studies (WOFOST) model provides an efficient way to solve this problem. In this study, we aimed at investigating the key periods of the assimilation framework for continuous monitoring of heavy metal stress in rice. The Harris algorithm was used for the leaf area index (LAI) curves to select the key period for an optimized assimilation. To obtain accurate LAI values, the measured dry weight of rice roots (WRT), which have been proven to be the most stress-sensitive indicator of heavy metal stress, were incorporated into the improved WOFOST model. Finally, the key periods, which contain four dominant time points, were used to select remote sensing images for the RS-WOFOST model for continuous monitoring of heavy metal stress. Compared with the key period which contains all the available remote sensing images, the results showed that the optimal key period can significantly improve the time efficiency of the assimilation framework by shortening the model operation time by more than 50%, while maintaining its accuracy. This result is highly significant when monitoring heavy metals in rice on a large-scale. Furthermore, it can also offer a reference for the timing of field measurements in monitoring heavy metal stress in rice. MDPI 2018-04-17 /pmc/articles/PMC5948729/ /pubmed/29673178 http://dx.doi.org/10.3390/s18041230 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhao, Shuang
Qian, Xu
Liu, Xiangnan
Xu, Zhao
Finding the Key Periods for Assimilating HJ-1A/B CCD Data and the WOFOST Model to Evaluate Heavy Metal Stress in Rice
title Finding the Key Periods for Assimilating HJ-1A/B CCD Data and the WOFOST Model to Evaluate Heavy Metal Stress in Rice
title_full Finding the Key Periods for Assimilating HJ-1A/B CCD Data and the WOFOST Model to Evaluate Heavy Metal Stress in Rice
title_fullStr Finding the Key Periods for Assimilating HJ-1A/B CCD Data and the WOFOST Model to Evaluate Heavy Metal Stress in Rice
title_full_unstemmed Finding the Key Periods for Assimilating HJ-1A/B CCD Data and the WOFOST Model to Evaluate Heavy Metal Stress in Rice
title_short Finding the Key Periods for Assimilating HJ-1A/B CCD Data and the WOFOST Model to Evaluate Heavy Metal Stress in Rice
title_sort finding the key periods for assimilating hj-1a/b ccd data and the wofost model to evaluate heavy metal stress in rice
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948729/
https://www.ncbi.nlm.nih.gov/pubmed/29673178
http://dx.doi.org/10.3390/s18041230
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