高分一号卫星浑浊水体水质参数软分类反演  

Estimation of water quality parameters of GF-1 WFV in turbid water based on soft classification

作　　者：张方方 李俊生 王超[4] 王胜蕾 ZHANG Fangfang;LI Junsheng;WANG Chao;WANG Shenglei(International Research Center of Big Data for Sustainable Development Goals,Beijing 100094,China;Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;University of Chinese Academy of Sciences,Beijing 100049,China;Institute of Geographical Sciences,Henan Academy of Sciences,Zhengzhou 450052,China)

机构地区：[1]可持续发展大数据国际研究中心,北京100094 [2]中国科学院空天信息创新研究院,北京100094 [3]中国科学院大学,北京100049 [4]河南省科学院地理研究所,郑州450052

出　　处：《遥感学报》2023年第3期769-779,共11页NATIONAL REMOTE SENSING BULLETIN

基　　金：中国科学院空天信息创新研究院重点部署项目(编号:E0Z202010F);可持续发展大数据国际研究中心主任青年基金(编号:CBAS2022DF005);河南省科学院重大科研聚焦项目(编号:210101007);高分辨率对地观测系统重大选项(编号:21-Y20B02-9003-19/22,21-Y201301-9001-19122)。

摘　　要：高分一号卫星宽幅盖相机(GF-1 WFV)拥有高空间和高时间分辨率,在水环境遥感应用方面有较大潜力,现有研究以特定区域算法为主,缺少可用于大范围的水质参数反演算法。基于以上问题,本研究在全国开展了28次共计68个航次的水面测量与采样实验,获取了具有较好典型性和代表性的647个采样点数据用于水质参数反演建模和验证。为了满足光学特性复杂的浑浊水体大范围水质参数反演的需求,发展了基于软分类的GF-1 WFV水质参数反演算法。算法首先将水体分为4类,并计算各类型水体的质心光谱;然后为各类型水体优选和优化适用的叶绿素a浓度、总悬浮物浓度和透明度反演模型,并用距离权重进行加权融合获取最终的水质参数反演结果。经星地同步实验数据验证,水体叶绿素a浓度、总悬浮物浓度和透明度反演的相对误差分别为33.1%、28.6%和17.6%,且类别边界过渡平滑,避免了不同模型导致的数值跳变问题。结果表明,本算法具有大范围水质参数反演产品生产的能力。The GF-1 wide-field-of-view cameras(GF-1 WFV)has high spatial and temporal resolution,and has great potential in the application of water environment remote sensing.Existing studies mainly focus on region-specific algorithms,and lack of water-quality parameter estimation algorithms that can be used in a large range.Based on the above problems,this study carried out 28 water surface measurement and sampling experiments with 68 voyages in China,and obtained 647 typical and representative sampling point data for water quality parameter estimation modeling and validation.The study area included Taihu Lake,Chaohu Lake,Dianchi Lake,Three Gorges Reservoir,Guanting Reservoir,Yuqiao Reservoir,Shandong Pingyin Small Water Body,Shaanxi Yulin Small Water Body,Ningxia Ningdong Base Small Water Body.The GF-1 WFV images were used the relative atmospheric correction algorithm based on Sentinel2-MSI data of uniform invariant ground object spectral database to obtain the water remote sensing reflectance data.In order to meet the needs of large-scale estimation of water quality parameters in turbid water with complex optical characteristics,a GF-1 WFV water quality parameter estimation algorithm based on soft classification was developed.Firstly,the algorithm divided the water into four types(OWTs)by a stepwise iterative K-mean clustering method and calculated the centroid spectra of each type of water by the average of all spectra of this category,among them,OWT1 was jointly dominated by phytoplankton and non-algae particles,OWT2 was dominated by non-algae particles,OWT3 was dominated by phytoplankton,OWT4 was bloom(no water quality inversion in this water type);Then,the Spectral Angular distance(SAD)was used to calculate the distance from each pixel to each type of centroid spectra and the SAD was converted into distance weight,and the suitable estimation models of chlorophyll a concentration,total suspended solids concentration and transparency were selected and optimized for each type of water body,and the final estimation resu

关 键 词：GF-1 WFV 水体类型 叶绿素A 总悬浮物 透明度 

分 类 号：P2[天文地球—测绘科学与技术]