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Sensors 2016, 16(3), 340; doi:10.3390/s16030340

Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST Model

1
School of Information Engineering, China University of Geosciences, 100083 Beijing, China
2
Institute of Remote Sensing and GIS, Peking University, 5 Yiheyuan Road, 100871 Beijing, China
*
Author to whom correspondence should be addressed.
Academic Editor: Simon X. Yang
Received: 27 January 2016 / Revised: 25 February 2016 / Accepted: 29 February 2016 / Published: 7 March 2016
(This article belongs to the Special Issue Sensors for Agriculture)
View Full-Text   |   Download PDF [3371 KB, uploaded 7 March 2016]   |  

Abstract

Accurate monitoring of heavy metal stress in crops is of great importance to assure agricultural productivity and food security, and remote sensing is an effective tool to address this problem. However, given that Earth observation instruments provide data at multiple scales, the choice of scale for use in such monitoring is challenging. This study focused on identifying the characteristic scale for effectively monitoring heavy metal stress in rice using the dry weight of roots (WRT) as the representative characteristic, which was obtained by assimilation of GF-1 data with the World Food Studies (WOFOST) model. We explored and quantified the effect of the important state variable LAI (leaf area index) at various spatial scales on the simulated rice WRT to find the critical scale for heavy metal stress monitoring using the statistical characteristics. Furthermore, a ratio analysis based on the varied heavy metal stress levels was conducted to identify the characteristic scale. Results indicated that the critical threshold for investigating the rice WRT in monitoring studies of heavy metal stress was larger than 64 m but smaller than 256 m. This finding represents a useful guideline for choosing the most appropriate imagery. View Full-Text
Keywords: characteristic scale; heavy metal stress; GF-1; rice; data assimilation characteristic scale; heavy metal stress; GF-1; rice; data assimilation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Huang, Z.; Liu, X.; Jin, M.; Ding, C.; Jiang, J.; Wu, L. Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST Model. Sensors 2016, 16, 340.

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