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李渊,郭宇龙,程春梅,张毅博,胡耀躲,夏忠,毕顺.基于OLCI数据的杭州湾悬浮物浓度估算及其产品适用性分析[J].365体育开户_万博体育365_365棋牌体育,2019,41(9):156-169
基于OLCI数据的杭州湾悬浮物浓度估算及其产品适用性分析
Remote estimation of total suspended matter concentration in the Hangzhou Bay based on OLCI and its water color product applicability analysis
投稿时间:2018-08-15??修订日期:2018-11-01
DOI:10.3969/j.issn.0253-4193.2019.09.015
中文关键词:??杭州湾??OLCI数据??悬浮物浓度??适用性分析??大气校正
英文关键词:Hangzhou Bay??OLCI data??total suspended matter??applicability analysis??atmospheric correction
基金项目:国家自然科学基金项目(41501374,41701422);浙江省自然科学基金项目(LQ16D010001)。
作者单位E-mail
李渊?浙江工商大学 旅游与城乡规划学院, 浙江 杭州 310018
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, 江苏 南京 210008?
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郭宇龙?河南农业大学 资源与环境学院, 河南 郑州 450002?gyl.18@163.com?
程春梅?浙江水利水电学院 测绘与市政工程学院, 浙江 杭州 310018??
张毅博?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, 江苏 南京 210008??
胡耀躲?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, 江苏 南京 210008??
夏忠?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, 江苏 南京 210008??
毕顺?南京师范大学 地理科学学院, 江苏 南京 210023??
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中文摘要:
??????悬浮物含量及其时空分布是河口海岸环境中关心的热点问题。2016年2月16日,欧洲航天局发射了新一代海洋水色传感器(OLCI),该传感器具有良好的时空及光谱分辨率。本研究结合2017年7月杭州湾同步采样数据,对比了6种大气校正算法和8种悬浮物浓度(TSM)估算模型,遴选和分析了适宜于杭州湾和OLCI数据的大气校正方法和TSM估算模型,验证了OLCI数据二级产品精度和适用性。结果表明:(1)基于紫外光谱的大气校正算法(UVAC)精度最高,同步4个采样点的大气校正平均相对误差(MAPE)分别为34.21%、13.11%、5.92%和20.28%。在除Oa1以外的14个波段的MAPE均值为15.23%,Oa4至Oa10波段的MAPE低于8%;(2)基于Oa16/Oa5的波段比值模型,具有良好的建模(MAPE为16.49%,RMSE为50.92 mg/L)和验证(MAPE为19.08%,RMSE为19.29 mg/L)精度及模型稳健性;(3)基于C2RCC算法的固有光学量和TSM含量产品及OLCI二级TSM含量产品在杭州湾精度较差,不适用于杭州湾TSM和固有光学量遥感监测应用;(4)空间上,TSM在杭州湾中部区域含量较低,在杭州湾南岸和湾口区域含量较高。
英文摘要:
??????As a main carrier of nutrients and pollutants, total suspended matter (TSM) has a significant influence on water environment, especially on estuary water environment. The Ocean and Land Colour Instrument (OLCI) was onboard ESA Sentinel-3A satellite and launched in February 16, 2016, with fine spatial, temporal and spectral resolution. To find the best atmospheric correction method and TSM retrieval model for the application of OLCI in Hangzhou Bay (HZB), six atmospheric correction methods and eight TSM retrieval models were test based on in situ water color data collected from HZB on July 2017. In addition, the OLCI Level 2 product (e.g. TSM and inherent optical properties (IOP) data) was compared with in situ data to evaluate the accuracy and applicability of OLCI Level 2 product. The results show that the method of atmospheric correction based on ultraviolet wavelength (UVAC) and the TSM retrieval model based on band ratio have best performance. Specifically, the mean absolute percentage error (MAPE) of atmospheric correction in four match-up sites is 34.21%, 13.11%, 5.92% and 20.28%, respectively. In addition, the averaged MAPE of atmospheric correction in band Oa2 to Oa12 and Oa16 to Oa18 is 15.23%, and in band Oa4 to Oa10 is less than 8%. The band ratio (Oa16/Oa5) model has the best performance, with a MAPE of 16.49% and root mean square error (RMSE) of 50.92 mg/L in calibration stage, and a MAPE of 19.08% and RMSE of 19.29 mg/L in validation stage. However, the TSM and IOP product derived from C2RCC (case 2 regional coast colour) algorithm and the TSM product derived from OLCI Level 2 product has no linear relationship with in situ data. These results indicate that the above Level 2 product is unsuitable for HZB TSM and IOP remote estimation. Finally, the UVAC method and band ratio model are applied to OLCI imagery that is collected on July 23, 2017. Spatially, TSM shows a relative low value in the center of HZB and relative high value in the south and east part of HZB.
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