Progress in Remote Sensing of Heavy Metals in Water

This review article details the advancements in detecting heavy metals in aquatic environments using remote sensing methodologies. Heavy metals are significant pollutants in aquatic environment, and their detection and monitoring are crucial for predicting water quality. Traditional in situ water sampling methods are time-consuming and costly, highlighting the advantages of remote sensing techniques. Analysis of the reflectance and absorption characteristics of heavy metals has identified the red and near-infrared bands as the sensitive wavelengths for heavy metal detection in aquatic environments. Several studies have demonstrated a correlation between total suspended matter and heavy metals, which forms the basis for retrieving heavy metal content from TSM data. Recent developments in hyperspectral remote sensing and machine (deep) learning technologies may pave the way for developing more effective heavy metal detection algorithms.

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Publication Details

Journal
Remote Sensing
Published
2024-10-19
DOI
https://doi.org/10.3390/rs16203888
Citations
10
Primary Topic
Remote-Sensing Image Classification
Type
article
Field-Weighted Citation Impact
3.20

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article

Progress in Remote Sensing of Heavy Metals in Water

Jiayi Pan, Hui Lin, Hua Zhang, Xiaoling Xu
10 citations
Remote Sensing
Remote-Sensing Image Classification
3.20
article

Progress in Remote Sensing of Heavy Metals in Water

Jiayi Pan, Hui Lin, Hua Zhang, Xiaoling Xu
article en
10 citations

Abstract

This review article details the advancements in detecting heavy metals in aquatic environments using remote sensing methodologies. Heavy metals are significant pollutants in aquatic environment, and their detection and monitoring are crucial for predicting water quality. Traditional in situ water sampling methods are time-consuming and costly, highlighting the advantages of remote sensing techniques. Analysis of the reflectance and absorption characteristics of heavy metals has identified the red and near-infrared bands as the sensitive wavelengths for heavy metal detection in aquatic environments. Several studies have demonstrated a correlation between total suspended matter and heavy metals, which forms the basis for retrieving heavy metal content from TSM data. Recent developments in hyperspectral remote sensing and machine (deep) learning technologies may pave the way for developing more effective heavy metal detection algorithms.

Remote SensingVol. 16(20)
Chinese University of Hong Kong (HK), Jiangxi Normal University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 7%
Remote-Sensing Image Classification
3.20
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Progress in Remote Sensing of Heavy Metals in Water — Jiayi Pan, Hui Lin, et al. · Remote Sensing (2024) | TGRS Research Map | TGRS