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Int. J. Environ. Res. Public Health 2018, 15(3), 461; https://doi.org/10.3390/ijerph15030461

Comparative Analysis of GF-1 and HJ-1 Data to Derive the Optimal Scale for Monitoring Heavy Metal Stress in Rice

School of Information Engineering, China University of Geosciences, Beijing 100083, China
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Received: 6 February 2018 / Revised: 1 March 2018 / Accepted: 4 March 2018 / Published: 6 March 2018
(This article belongs to the Section Environmental Science and Engineering)
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Abstract

Remote sensing can actively monitor heavy metal contamination in crops, but with the increase of satellite sensors, the optimal scale for monitoring heavy metal stress in rice is still unknown. This study focused on identifying the optimal scale by comparing the ability to detect heavy metal stress in rice at various spatial scales. The 2 m, 8 m, and 16 m resolution GF-1 (China) data and the 30 m resolution HJ-1 (China) data were used to invert leaf area index (LAI). The LAI was the input parameter of the World Food Studies (WOFOST) model, and we obtained the dry weight of storage organs (WSO) and dry weight of roots (WRT) through the assimilation method; then, the mass ratio of rice storage organs and roots (SORMR) was calculated. Through the comparative analysis of SORMR at each spatial scale of data, we determined the optimal scale to monitor heavy metal stress in rice. The following conclusions were drawn: (1) SORMR could accurately and effectively monitor heavy metal stress; (2) the 8 m and 16 m images from GF-1 were suitable for monitoring heavy metal stress in rice; (3) 16 m was considered the optimal scale to assess heavy metal stress in rice. View Full-Text
Keywords: remote sensing; optimal scale; heavy metal stress; WOFOST model remote sensing; optimal scale; heavy metal stress; WOFOST model
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Wang, D.; Liu, X. Comparative Analysis of GF-1 and HJ-1 Data to Derive the Optimal Scale for Monitoring Heavy Metal Stress in Rice. Int. J. Environ. Res. Public Health 2018, 15, 461.

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