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基于高光谱观测的雄安新区冬季水质模拟

Winter Water Quality Modeling in Xiong'an New Area Supported by Hyperspectral Observation.

机构信息

National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology, Beijing Research Institute of Uranium Geology, Beijing 100029, China.

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.

出版信息

Sensors (Basel). 2023 Apr 18;23(8):4089. doi: 10.3390/s23084089.

DOI:10.3390/s23084089
PMID:37112430
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10144822/
Abstract

Xiong'an New Area is defined as the future city of China, and the regulation of water resources is an important part of the scientific development of the city. Baiyang Lake, the main supplying water for the city, is selected as the study area, and the water quality extraction of four typical river sections is taken as the research objective. The GaiaSky-mini2-VN hyperspectral imaging system was executed on the UAV to obtain the river hyperspectral data for four winter periods. Synchronously, water samples of COD, PI, AN, TP, and TN were collected on the ground, and the in situ data under the same coordinate were obtained. A total of 2 algorithms of band difference and band ratio are established, and the relatively optimal model is obtained based on 18 spectral transformations. The conclusion of the strength of water quality parameters' content along the four regions is obtained. This study revealed four types of river self-purification, namely, uniform type, enhanced type, jitter type, and weakened type, which provided the scientific basis for water source traceability evaluation, water pollution source area analysis, and water environment comprehensive treatment.

摘要

雄安新区被定义为中国的未来之城,水资源的规划是城市科学发展的重要组成部分。白洋淀作为城市的主要供水水源地被选作研究区域,以四条典型河段的水质提取作为研究对象。使用 GaiaSky-mini2-VN 高光谱成像系统在无人机上获取了四个冬季的河流水质高光谱数据。同时,在地面上采集了 COD、PI、AN、TP 和 TN 的水样,并获得了相同坐标下的原位数据。总共建立了 2 种波段差和波段比算法,并基于 18 种光谱变换得到了较优模型。得到了四个区域沿程水质参数含量的强弱结论。本研究揭示了四种河流自净类型,即均匀型、增强型、波动型和减弱型,为水源溯源评价、水污染溯源分析和水环境综合治理提供了科学依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa32/10144822/8f2e11ad7e0d/sensors-23-04089-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa32/10144822/90580b6453b6/sensors-23-04089-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa32/10144822/a5c860ad4f3c/sensors-23-04089-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa32/10144822/b1716d5a00d1/sensors-23-04089-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa32/10144822/f242ae3e984a/sensors-23-04089-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa32/10144822/8f2e11ad7e0d/sensors-23-04089-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa32/10144822/90580b6453b6/sensors-23-04089-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa32/10144822/a5c860ad4f3c/sensors-23-04089-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa32/10144822/b1716d5a00d1/sensors-23-04089-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa32/10144822/f242ae3e984a/sensors-23-04089-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa32/10144822/8f2e11ad7e0d/sensors-23-04089-g004a.jpg

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