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多光谱遥感在监测约旦内陆水体叶绿素-a 水平中的应用。

Multispectral Remote Sensing Utilization for Monitoring Chlorophyll-a Levels in Inland Water Bodies in Jordan.

机构信息

Department of Civil Engineering, University of Petra, Amman, Jordan.

School of Natural Resources Engineering and Management, German Jordanian University, Amman, Jordan.

出版信息

ScientificWorldJournal. 2020 Aug 7;2020:5060969. doi: 10.1155/2020/5060969. eCollection 2020.

DOI:10.1155/2020/5060969
PMID:32831806
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7428877/
Abstract

This study focuses on the utilization of multispectral satellite images for remote water-quality evaluation of inland water body in Jordan. The geophysical parameters based on water's optical properties, due to the presence of optically active constituents, are used to determine contaminant level in water. It has a great potential to be employed for continuous and cost-effective water-quality monitoring and leads to a reliable regularly updated tool for better water sector management. Three sets of water samples were collected from three different dams in Jordan. Chl-a concentration of the water samples was measured and used with corresponding Sentinel 2 surface reflectance (SR) data to develop a predictive model. Chl-a concentrations and corresponding SR data were used to calibrate and validate different models. The predictive capability of each of the investigated models was determined in terms of determination coefficient ( ) and lowest root mean square error (RMSE) values. For the investigated sites, the B3/B2 (green/blue bands) model and the Ln (B3/B2) model showed the best overall predictive capability of all models with the highest and the lowest RMSE values of (0.859, 0.824) and (30.756 mg/m, 29.787 mg/m), respectively. The outcome of this study on selected sites can be expanded for future work to cover more sites in the future and ultimately cover all sites in Jordan.

摘要

本研究专注于利用多光谱卫星图像对约旦内陆水体进行远程水质评估。基于水体光学特性的地球物理参数，由于存在光活性成分，可用于确定水中污染物的水平。它具有用于连续和具有成本效益的水质监测的巨大潜力，并为更好的水部门管理提供了可靠的定期更新工具。从约旦的三个不同大坝采集了三组水样。测量了水样中的 Chl-a 浓度，并将其与相应的 Sentinel 2 表面反射率 (SR) 数据一起用于开发预测模型。使用 Chl-a 浓度和相应的 SR 数据来校准和验证不同的模型。根据决定系数（ ）和最低均方根误差 (RMSE) 值来确定每个研究模型的预测能力。对于研究的地点，B3/B2（绿色/蓝色波段）模型和 Ln（B3/B2）模型显示出所有模型中最佳的整体预测能力，具有最高的 和最低的 RMSE 值（0.859、0.824）和（30.756 mg/m、29.787 mg/m）。在选定地点进行的这项研究的结果可以扩展到未来的更多地点，最终覆盖约旦的所有地点。

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A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques.利用遥感技术估算水质参数的综合综述
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