title: Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST Model.
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.
authors.affiliation: School of Information Engineering, China University of Geosciences, 100083 Beijing, China. zhihuang@cugb.edu.cn.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Huang
authors.affiliation: School of Information Engineering, China University of Geosciences, 100083 Beijing, China. liuxn@cugb.edu.cn.
authors.forename: Xiangnan
authors.initials: X
authors.lastname: Liu
authors.affiliation: School of Information Engineering, China University of Geosciences, 100083 Beijing, China. jinming@cugb.edu.cn.
authors.forename: Ming
authors.initials: M
authors.lastname: Jin
authors.affiliation: School of Information Engineering, China University of Geosciences, 100083 Beijing, China. dc@cugb.edu.cn.
authors.forename: Chao
authors.initials: C
authors.lastname: Ding
authors.affiliation: School of Information Engineering, China University of Geosciences, 100083 Beijing, China. jjl90@163.com.
authors.forename: Jiale
authors.initials: J
authors.lastname: Jiang
authors.affiliation: Institute of Remote Sensing and GIS, Peking University, 5 Yiheyuan Road, 100871 Beijing, China. wl_19830807@sohu.com.
authors.forename: Ling
authors.initials: L
authors.lastname: Wu
mesh_terms: D018556:Crops, Agricultural / Q000187:drug effects*; D004784:Environmental Monitoring; D019216:Metals, Heavy / Q000633:toxicity*; D012275:Oryza / Q000187:drug effects*; D018515:Plant Leaves / Q000187:drug effects; D018517:Plant Roots / Q000187:drug effects; D058998:Remote Sensing Technology; D012989:Soil Pollutants / Q000737:chemistry / Q000302:isolation & purification
chemical_list: D019216:Metals, Heavy; D012989:Soil Pollutants
keywords: GF-1; characteristic scale; data assimilation; heavy metal stress; rice