天空地一体立体网络的水质遥感监测及其溯源初探——以淀山湖为例

徐晓军; 魏小岛; 程佩瑄; 魏本胜; 卫炎豪; 孟陈

引用本文:	XU Xiaojun,WEI Xiaodao,CHENG Peixuan,WEI Bensheng,WEI Yanhao,MENG Chen.Stereoscopic Network Monitoring for Water Quality Integration of Satellite，UAV，and Ship Observations： A Case Study of Dianshan Lake[J].Environmental Monitoring and Forewarning,2024,16(6):8~14

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天空地一体立体网络的水质遥感监测及其溯源初探——以淀山湖为例
徐晓军¹，魏小岛^2,3，程佩瑄¹，魏本胜²，卫炎豪⁴，孟陈^5*
1. 上海市环境监测中心，上海 200050；2. 上海勘测设计研究院有限公司，上海 200050；3. 中国长江三峡集团有限公司长江生态环境工程研究中心（上海），上海 200050；4. 景遥（上海）信息技术有限公司，上海 201109；5. 华东师范大学地理科学学院，上海 201109

摘要:

利用卫星遥感、无人机和走航船等手段组成的天空地一体化立体水质监测网络对淀山湖水质进行监测，并对水质参数的空间分布进行溯源分析。结果表明：（1）构建的水质参数反演模型实现了对淀山湖区域高精度的水质反演。其中总磷质量浓度反演结果的均方根误差（RMSE）为0.019 1 mg/L，决定系数（R²）=0.64（P<0.001）；氨氮质量浓度反演结果的RMSE为0.036 0 mg/L，R²=0.83（P<0.001）；（2）水质空间分布特征为淀山湖总磷和氨氮浓度均表现为西北高，中部和东西低；叶绿素a浓度呈现出北高南低、东高西低及岸边高、中心低的空间分布特征；（3）通过广义线性混合模型对淀山湖水质空间分布进行溯源分析发现，入湖支流、动力船只、农田和水生植物对淀山湖总磷、氨氮和叶绿素a浓度的分布格局均有显著影响，其中入湖支流的影响最大。该研究结果可为上海市水环境立体监测提供重要参考和科学依据。

关键词: 天空地一体立体监测网络淀山湖水质监测溯源分析

DOI：DOI：10.3969/j.issn.1674-6732.2024.06.002

分类号:X823

基金项目:中国长江三峡集团有限公司科研项目（202103552）

Stereoscopic Network Monitoring for Water Quality Integration of Satellite，UAV，and Ship Observations： A Case Study of Dianshan Lake

XU Xiaojun¹， WEI Xiaodao^2,3， CHENG Peixuan¹， WEI Bensheng²， WEI Yanhao⁴， MENG Chen^5*

1.Shanghai Environmental Monitoring Center， Shanghai 200050， China； 2.Shanghai Investigation， Design & Research Institute Co.， Ltd.， Shanghai 200050， China； 3. Yangtze River Ecological and Environmental Engineering Research Center（Shanghai） of China Three Gorges Corporation， Shanghai 200050，China； 4. Jingyao（Shanghai） Information Technology Co.， Ltd.， Shanghai 201109， China； 5. School of Geographical Sciences， East China Normal University， Shanghai 201109， China

Abstract:

This study developed a stereoscopic network monitoring system that integrates satellite-based remote sensing， unmanned aerial vehicle（UAV） observations， and ship-based surveys to monitor the water quality of Dianshan Lake. The spatial distribution of water quality parameters was monitored and analyzed. The results demonstrated the followings：（1） The constructed water quality inversion model achieved high precision， with root mean square errors（RMSE） of 0.019 1 mg/L（R²=0.64，P<0.001） for total phosphorus and 0.036 0 mg/L（R²=0.83，P<0.001） for total nitrogen concentrations.（2） Characteristics of spatial distribution of water quality： The total phosphorus and ammonia nitrogen in Dianshan Lake are higher in the northwest and lower in the central， eastern， and western regions; the Chlorophyll-a shows a spatial distribution characterized by higher levels in the north and east， lower levels in the south and west， and higher concentrations along the shores and lower in the center.（3） Trace analysis using a generalized linear mixing model revealed significant impacts from inflowing tributaries， motorized boats， farmland， and aquatic plants on the distribution patterns of total phosphorus， ammonia-nitrogen concentrations， and chlorophyll-a concentration， with inflowing tributaries having the most substantial influence. These research findings provide essential references and scientific foundations for stereoscopic network monitoring of the water environment in Shanghai.

Key words: Sky Ground Integrated Stereoscopic network monitoring Dianshan Lake Water quality monitoring Tracing analysis