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

Xiangnan Liu,Dongmin Wang

doi:10.3390/ijerph15030461

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.

Highlights

Heavy metals in soils have become major environmental pollutants that can affect crops throughout the world [1,2]
This study mainly focuses on spatial scale, and the optimal scale in this study refers to the optimal spatial resolution
Through the improved World Food Studies (WOFOST) model, we simulated the weight of the rice roots in study areas A and B at the four spatial resolutions

Summary

Introduction

Heavy metals in soils have become major environmental pollutants that can affect crops throughout the world [1,2]. Rapid and effective monitoring of heavy metal pollution in rice has important and practical significance. Traditional methods for monitoring heavy metal stress involve collecting rice samples and conducting laboratory analyses [3,4]. Remote sensing can remotely detect target objects and dynamically monitor a large area in real-time, which provides a new method for rapid and large-scale monitoring of environmental pollution [5,6]. The characteristics of remote sensing information under rice pollution stress have been revealed, and studies indicate that the use of remote sensing technology to monitor heavy metal pollution in rice is principally and technologically feasible [11,12].

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Methods

Results

Discussion

Conclusion