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

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

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...

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Main Authors: Zhi Huang, Xiangnan Liu, Ming Jin, Chao Ding, Jiale Jiang, Ling Wu
Format: Article
Language:English
Published: MDPI AG 2016-03-01
Series:Sensors
Subjects:
characteristic scale
heavy metal stress
GF-1
rice
data assimilation
Online Access:http://www.mdpi.com/1424-8220/16/3/340
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spelling doaj-e89e4a13f6f54e51a0e6b583b99305a52020-11-24T22:12:51ZengMDPI AGSensors1424-82202016-03-0116334010.3390/s16030340s16030340Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST ModelZhi Huang0Xiangnan Liu1Ming Jin2Chao Ding3Jiale Jiang4Ling Wu5School of Information Engineering, China University of Geosciences, 100083 Beijing, ChinaSchool of Information Engineering, China University of Geosciences, 100083 Beijing, ChinaSchool of Information Engineering, China University of Geosciences, 100083 Beijing, ChinaSchool of Information Engineering, China University of Geosciences, 100083 Beijing, ChinaSchool of Information Engineering, China University of Geosciences, 100083 Beijing, ChinaInstitute of Remote Sensing and GIS, Peking University, 5 Yiheyuan Road, 100871 Beijing, ChinaAccurate 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.http://www.mdpi.com/1424-8220/16/3/340characteristic scaleheavy metal stressGF-1ricedata assimilation
collection DOAJ
language English
format Article
sources DOAJ
author Zhi Huang
Xiangnan Liu
Ming Jin
Chao Ding
Jiale Jiang
Ling Wu
spellingShingle Zhi Huang
Xiangnan Liu
Ming Jin
Chao Ding
Jiale Jiang
Ling Wu
Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST Model
Sensors
characteristic scale
heavy metal stress
GF-1
rice
data assimilation
author_facet Zhi Huang
Xiangnan Liu
Ming Jin
Chao Ding
Jiale Jiang
Ling Wu
author_sort Zhi Huang
title Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST Model
title_short Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST Model
title_full Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST Model
title_fullStr Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST Model
title_full_unstemmed Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST Model
title_sort deriving the characteristic scale for effectively monitoring heavy metal stress in rice by assimilation of gf-1 data with the wofost model
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2016-03-01
description 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.
topic characteristic scale
heavy metal stress
GF-1
rice
data assimilation
url http://www.mdpi.com/1424-8220/16/3/340
work_keys_str_mv AT zhihuang derivingthecharacteristicscaleforeffectivelymonitoringheavymetalstressinricebyassimilationofgf1datawiththewofostmodel
AT xiangnanliu derivingthecharacteristicscaleforeffectivelymonitoringheavymetalstressinricebyassimilationofgf1datawiththewofostmodel
AT mingjin derivingthecharacteristicscaleforeffectivelymonitoringheavymetalstressinricebyassimilationofgf1datawiththewofostmodel
AT chaoding derivingthecharacteristicscaleforeffectivelymonitoringheavymetalstressinricebyassimilationofgf1datawiththewofostmodel
AT jialejiang derivingthecharacteristicscaleforeffectivelymonitoringheavymetalstressinricebyassimilationofgf1datawiththewofostmodel
AT lingwu derivingthecharacteristicscaleforeffectivelymonitoringheavymetalstressinricebyassimilationofgf1datawiththewofostmodel
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