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

Zhi Huang,Ling Wu,Jiale Jiang,Chao Ding,Ming Jin,Xiangnan Liu

doi:10.3390/s16030340

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.

Highlights

Nowadays, soil heavy metal pollution has become of great concern to the public given increased release of metals from industrial processes and consequential food security contamination incidents; heavy metals are highly persistent, non-biodegradable, and toxic, and they can be harmful to human health when they bio-accumulate in plants and animals or bio-magnify in the food chain [1,2,3,4,5]
The weight of roots (WRT) was consider as the representative characteristic, and this parameter has been shown to be a good indicator in previous studies [21]
The assessment performance of WRT was favorably proven in terms of the sensitivity analysis that compared the results for WRT data to those for chlorophyll and leaf area index (LAI) data

Summary

Introduction

Soil heavy metal pollution has become of great concern to the public given increased release of metals from industrial processes and consequential food security contamination incidents; heavy metals are highly persistent, non-biodegradable, and toxic, and they can be harmful to human health when they bio-accumulate in plants and animals or bio-magnify in the food chain [1,2,3,4,5]. This underlines the need for the development of accurate and effective methods to monitor heavy metal stress in agricultural crops grown over large areas.

Methods

Results

Conclusion