投稿中心

审稿中心

编辑中心

期刊出版

网站地图

友情链接

引用本文:胡耀躲,张运林,杨波,张毅博.基于高频次GOCI数据的太湖悬浮物浓度短期动态和驱动力分析.湖泊科学,2018,30(4):992-1003. DOI:10.18307/2018.0412
HU Yaoduo,ZHANG Yunlin,YANG Bo,ZHANG Yibo.Short-term dynamics and driving factors of total suspended matter concentration in Lake Taihu using high frequent geostationary ocean color imager data. J. Lake Sci.2018,30(4):992-1003. DOI:10.18307/2018.0412
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 6864次   下载 3160 本文二维码信息
码上扫一扫!
分享到: 微信 更多
基于高频次GOCI数据的太湖悬浮物浓度短期动态和驱动力分析
胡耀躲1,2, 张运林1, 杨波2, 张毅博1
1.中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008;2.湖南师范大学资源与环境科学学院GIS研究中心, 长沙 410081
摘要:
总悬浮物是水体中重要的光学敏感物质之一,很大程度上决定了水柱中光的吸收、散射和衰减,同时吸附营养盐、重金属和有毒有害物,对水体物质生物地球化学过程、沉积物埋藏动力和湖泊环境演化具有重要的意义.基于星地同步实验和静止水色成像仪GOCI(Geostationary Ocean Color Imager)构建了太湖悬浮物浓度估算模型,并分析了典型风浪过程中太湖悬浮物浓度短期动态变化过程.研究表明:对太湖水体悬浮物浓度较为敏感的波段为GOCI的第7波段(745 nm)和第8波段(865 nm),悬浮物浓度与对应波段遥感反射率线性相关决定系数分别为0.72和0.55;基于GOCI第7波段的悬浮物浓度单波段遥感估算模型能较为准确地估算太湖的悬浮物浓度,模型相对均方根误差和平均绝对百分误差分别为28.3%和24.4%.通过研究典型风浪过程前后太湖悬浮物浓度变化发现其短期动态变化显著,风速、风向是悬浮物浓度短期动态变化的重要驱动因素,悬浮物浓度与风速呈正比,并随着风向扩散;高频连续GOCI影像结果显示悬浮物浓度短期动态变化对风浪扰动的响应有一定的滞后性,滞后时间为数小时到1天,悬浮物沉降与沉积物再悬浮的临界风速约为3.4 m/s.
关键词:  GOCI  太湖  总悬浮物  遥感估算  风浪
DOI:10.18307/2018.0412
分类号:
基金项目:国家自然科学基金项目(41621002,41661134036)和中国科学院前沿科学重点研究项目(QYZDB-SSW-DQC016)联合资助.
Short-term dynamics and driving factors of total suspended matter concentration in Lake Taihu using high frequent geostationary ocean color imager data
HU Yaoduo1,2, ZHANG Yunlin1, YANG Bo2, ZHANG Yibo1
1.State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, P. R. China;2.College of Resources & Environmental Science, GIS Research Center, Hunan Normal University, Changsha 410081, P. R. China
Abstract:
Total suspended matter (TSM) is one of the important optical active substances in water body, which largely determines the absorption, scattering and attenuation of light in water column and adsorbs nutrients, heavy metals and toxic and harmful substances. Therefore, TSM is of great important to biogeochemical processes of water substance, lake sediment burial dynamics andlake environment evolution. This study developed TSM estimation model in Lake Taihu based on satellite-ground synchronous experiment and Geostationary Ocean Color Imager (GOCI) image, and further analyzed TSM short-term dynamics in Lake Taihu during its typical wind-waves process. The results showed that the band 7 (745 nm) and band 8 (865 nm) of GOCI data were the sensitive bands of TSM estimation in Lake Taihu with the linear correlation determination coefficients of 0.72 and 0.55, respectively. A single band empirical model based on GOCI band 7 was used to estimate the TSM concentration of Lake Taihu and its relative root mean square error and mean absolute percentage error were 28.3% and 24.4%, respectively. Significant short-term TSM dynamic change was observed in Lake Taihu, and wind direction and wind speed were the important factors determining TSM concentration. TSM concentration increased with the increase of wind speed and diffused along with wind direction. High-frequency continuous GOCI images showed that the short-term dynamics of TSM concentration had a certain lag in response to wind-wave disturbance. The lag time ranged from several hours to one day, and the critical wind speed of TSM settling and sediment resuspension was about 3.4 m/s.
Key words:  GOCI  Lake Taihu  total suspended matter  remote sensing estimation  wind-waves
分享按钮