典型浅水草型湖泊水体悬浮物浓度遥感反演  

Remote sensing inversion of total suspended matter concentration in typical shallow macrophytic lake

作　　者：乔娜[1,2] 黄长平[1] 张立福[1] 赵红莉[3] 冶运涛[3] 岑奕[1] 李瑶[1,2] 

机构地区：[1]中国科学院遥感与数字地球研究所遥感科学国家重点实验室,北京100101 [2]中国科学院大学资源与环境学院,北京100049 [3]中国水利水电科学研究院水资源研究所水资源信息研究室,北京100038

出　　处：《湖北大学学报（自然科学版）》2016年第6期510-516,共7页Journal of Hubei University：Natural Science

基　　金：高分水利遥感应用示范系统(08-Y30B07-9001-13/15-01);高分辨率对地观测系统重大专项资助

摘　　要：针对典型浅水草型湖泊的特点,分析水生植物对悬浮物浓度及其遥感反演的影响,得到水生植物与其覆盖区水体总悬浮物浓度之间的定量关系,提出了基于水生植物分区反演水体悬浮物浓度的新思路:首先利用植被指数将全湖分为水生植物覆盖区和未覆盖区.在水生植物覆盖区,通过水生植物的盖度来指示水体水质,进而间接反演悬浮物浓度,解决了水生植物覆盖区难以直接反演悬浮物浓度的难题;在未覆盖区,利用地面实测数据与Landsat8卫星数据建立悬浮物浓度的近红外单波段反演模型,最终得到微山湖悬浮物浓度空间分布图.在晴朗、无风或轻风的天气条件下,水生植物与其覆盖区水体悬浮物浓度之间具有稳定的定量化特征,利用该特征指示水质对浅水草型湖泊水质参数的定量化遥感具有一定的参考意义.According to the characteristics of typical shallow macrophytic lakes,the quantitative relations are gotten between submerged aquatic vegetation（ SAV） and total suspended matter concentration（ TSM） based on analyzing the effects of SAV on remote sensing retrieval of TSM. And with the quantitative relations,the paper presents a new method for divisional inversion of TSM based on SAV： first,the whole lake was divided into different districts by NDVI. In the area of SAV,water quality could be indicated by SAV cover degree,and the method solved the problem of retrieval of TSM in aquatic plants water. In general water,the optimal inversion model of TSM was established with the in situ data and Landsat8 satellite data. Under sunny calm conditions,there exist the stable quantified characteristic between SAV and TSM. The paper has practical reference value for quantitative remote sensing of the water qualities of shallow macrophytic lakes.

关 键 词：遥感 悬浮物浓度 浅水草型湖泊 微山湖 水生植物 Landsat8 

分 类 号：P237[天文地球—摄影测量与遥感] P962[天文地球—测绘科学与技术]