Sampling Uncertainties of Long-Term Remote-Sensing Suspended Sediments Monitoring over China's Seas: Impacts of Cloud Coverage and Sediment Variations

doi:10.3390/rs12121945

CORC > 烟台海岸带研究所 > 中国科学院烟台海岸带研究所 > 中科院海岸带环境过程与生态修复重点实验室 > 海岸带信息集成与综合管理实验室

	Sampling Uncertainties of Long-Term Remote-Sensing Suspended Sediments Monitoring over China's Seas: Impacts of Cloud Coverage and Sediment Variations
	Tian, Liqiao 2; Sun, Xianghan 2; Li, Jian 3,4; Xing, Qianguo 1; Song, Qingjun 5; Tong, Ruqing 2
刊名	REMOTE SENSING
	2020-06-01
卷号	12 期号:12 页码:26
关键词	ocean color remote sensing sampling uncertainty GOCI Terra Aqua MODIS cloud coverage suspended sediment
DOI	10.3390/rs12121945
通讯作者	Li, Jian(lijian@nuist.edu.cn)
英文摘要	Satellite-based ocean color sensors have provided an unprecedentedly large amount of information on ocean, coastal and inland waters at varied spatial and temporal scales. However, observations are often adversely affected by cloud coverage and other poor weather conditions, like sun glint, and this influences the accuracy associated with long-term monitoring of water quality parameters. This study uses long-term (2013-2017) and high-frequency (eight observations per day) datasets from the Geostationary Ocean Color Imager (GOCI), the first geostationary ocean color satellite sensor, to quantify the cloud coverage over China's seas, the resultant interrupted observations in remote sensing, and their impacts on the retrieval of total suspended sediments (TSS). The monthly mean cloud coverage for the East China Sea (ECS), Bohai Sea (BS) and Yellow Sea (YS) were 62.6%, 67.3% and 69.9%, respectively. Uncertainties regarding the long-term retrieved TSS were affected by a combination of the effects of cloud coverage and TSS variations. The effects of the cloud coverage dominated at the monthly scale, with the mean normalized bias (P-bias) at 14.1% (+/- 2.6%), 7.6% (+/- 2.3%) and 12.2% (+/- 4.3%) for TSS of the ECS, BS and YS, respectively. Cloud coverage-interfering observations with the Terra/Aqua MODIS systems were also estimated, with monthly P(bias)ranging from 6.5% (+/- 7.4%) to 20% (+/- 13.1%) for TSS products, and resulted in a smaller data range and lower maximum to minimum ratio compared to the eight GOCI observations. Furthermore, with approximately 16.7% monthly variations being missed during the periods, significant "missing trends" effects were revealed in monthly TSS variations from Terra/Aqua MODIS. For the entire region and the Bohai Sea, the most appropriate timeframe for sampling ranges from 12:30 to 15:30, while this timeframe was narrowed to from 13:30 to 15:30 for observations in the East China Sea and the Yellow Sea. This research project evaluated the effects of cloud coverage and times for sampling on the remote sensing monitoring of ocean color constituents, which would suggest the most appropriate timeframe for ocean color sensor scans, as well as in situ data collection, and can provide design specification guidance for future satellite sensor systems.
资助项目	National KeyR&DProgram of China[2018YFB0504900] ; National KeyR&DProgram of China[2018YFB0504904] ; National KeyR&DProgram of China[2016YFC0200900] ; National Natural Science Foundation of China[41571344] ; National Natural Science Foundation of China[41701379] ; National Natural Science Foundation of China[41331174] ; National Natural Science Foundation of China[41406205] ; Startup Foundation for Introducing Talent of NUISTChina Institute ofWater Resources and Hydropower Research[IWHR-SKL-KF201809] ; Wuhan University Luojia Talented Young Scholar project[32442] ; LIESMARS Special Research Funding ; 985 Project of Wuhan University ; Special funds of State Key Laboratory for equipment ; Open Research Fund of State Key Laboratory of Simulation and Regulation ofWater Cycle in River Basin
WOS关键词	OCEAN COLOR DATA ; PARTICULATE MATTER ; CHLOROPHYLL-A ; BOHAI SEA ; MODIS OBSERVATIONS ; YELLOW SEA ; COASTAL ; GOCI ; PRODUCTS ; ALGORITHM
WOS研究方向	Remote Sensing
语种	英语
WOS记录号	WOS:000552459500001
资助机构	National KeyR&DProgram of China ; National Natural Science Foundation of China ; Startup Foundation for Introducing Talent of NUISTChina Institute ofWater Resources and Hydropower Research ; Wuhan University Luojia Talented Young Scholar project ; LIESMARS Special Research Funding ; 985 Project of Wuhan University ; Special funds of State Key Laboratory for equipment ; Open Research Fund of State Key Laboratory of Simulation and Regulation ofWater Cycle in River Basin
内容类型	期刊论文
源URL	[http://ir.yic.ac.cn/handle/133337/28586]
专题	烟台海岸带研究所_海岸带信息集成与综合管理实验室烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室
通讯作者	Li, Jian
作者单位	1.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China 2.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China 3.Nanjing Univ Informat Sci & Technol NUIST, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China 4.China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, Beijing 100038, Peoples R China 5.State Ocean Adm, Key Lab Space Ocean Remote Sensing & Applicat, Beijing 10089, Peoples R China
推荐引用方式 GB/T 7714	Tian, Liqiao,Sun, Xianghan,Li, Jian,et al. Sampling Uncertainties of Long-Term Remote-Sensing Suspended Sediments Monitoring over China's Seas: Impacts of Cloud Coverage and Sediment Variations[J]. REMOTE SENSING,2020,12(12):26.
APA	Tian, Liqiao,Sun, Xianghan,Li, Jian,Xing, Qianguo,Song, Qingjun,&Tong, Ruqing.(2020).Sampling Uncertainties of Long-Term Remote-Sensing Suspended Sediments Monitoring over China's Seas: Impacts of Cloud Coverage and Sediment Variations.REMOTE SENSING,12(12),26.
MLA	Tian, Liqiao,et al."Sampling Uncertainties of Long-Term Remote-Sensing Suspended Sediments Monitoring over China's Seas: Impacts of Cloud Coverage and Sediment Variations".REMOTE SENSING 12.12(2020):26.