A Neural Network Model for K(lambda) Retrieval and Application to Global K-par Monitoring

doi:10.1371/journal.pone.0127514

CORC > 自然资源部第一海洋研究所 > 自然资源部第一海洋研究所 > 业务部门 > 海洋物理与遥感研究室

	A Neural Network Model for K(lambda) Retrieval and Application to Global K-par Monitoring
	Chen, Jun 1; Zhu, Yuanli 2,3; Wu, Yongsheng 4; Cui, Tingwei 5; Ishizaka, Joji 3; Ju, Yongtao 6
刊名	PLOS ONE
	2015-06-17
卷号	10 期号:6
ISSN号	1932-6203
DOI	10.1371/journal.pone.0127514
英文摘要	Accurate estimation of diffuse attenuation coefficients in the visible wavelengths K-d(lambda) from remotely sensed data is particularly challenging in global oceanic and coastal waters. The objectives of the present study are to evaluate the applicability of a semi-analytical K-d(lambda) retrieval model (SAKM) and Jamet's neural network model (JNNM), and then develop a new neural network K-d(lambda) retrieval model (NNKM). Based on the comparison of K-d(lambda) predicted by these models with in situ measurements taken from the global oceanic and coastal waters, all of the NNKM, SAKM, and JNNM models work well in K-d(lambda) retrievals, but the NNKM model works more stable and accurate than both SAKM and JNNM models. The near-infrared band-based and shortwave infrared band-based combined model is used to remove the atmospheric effects on MODIS data. The K-d(lambda) data was determined from the atmospheric corrected MODIS data using the NNKM, JNNM, and SAKM models. The results show that the NNKM model produces <30% uncertainty in deriving K-d(lambda) from global oceanic and coastal waters, which is 4.88-17.18% more accurate than SAKM and JNNM models. Furthermore, we employ an empirical approach to calculate K-par from the NNKM model-derived diffuse attenuation coefficient at visible bands (443, 488, 555, and 667 nm). The results show that our model presents a satisfactory performance in deriving K-par from the global oceanic and coastal waters with 20.2% uncertainty. The K-par are quantified from MODIS data atmospheric correction using our model. Comparing with field measurements, our model produces similar to 31.0% uncertainty in deriving K-par from Bohai Sea. Finally, the applicability of our model for general oceanographic studies is briefly illuminated by applying it to climatological monthly mean remote sensing reflectance for time ranging from July, 2002-July 2014 at the global scale. The results indicate that the high K-d(lambda) and K-par values are usually found around the coastal zones in the high latitude regions, while low K-d(lambda) and K-par values are usually found in the open oceans around the low-latitude regions. These results could improve our knowledge about the light field under waters at either the global or basin scales, and be potentially used into general circulation models to estimate the heat flux between atmosphere and ocean.
URL标识	查看原文
资助项目	China Geological Survey[GZH201400201] ; China Geological Survey[201300501]
WOS研究方向	Science & Technology - Other Topics
语种	英语
出版者	PUBLIC LIBRARY SCIENCE
WOS记录号	WOS:000356567400013
内容类型	期刊论文
源URL	[http://ir.fio.com.cn/handle/2SI8HI0U/3652]
专题	业务部门_海洋物理与遥感研究室
作者单位	1.Qingdao Inst Marine Geol, Key Lab Coastal Wetland Biogeosci, China Geol Survey, Qingdao 266071, Peoples R China; 2.Nagoya Univ, Hydrospher Atmospher Res Ctr, Nagoya, Aichi 4648601, Japan; 3.Nagoya Univ, Grad Sch Environm Studies, Nagoya, Aichi 4648601, Japan; 4.Fisheries & Oceans Canada, Bedford Inst Oceanog, Dartmouth, NS B2Y 4A2, Canada; 5.State Ocean Adm, Inst Oceanog 1, Qingdao 266071, Peoples R China; 6.Hebei United Univ, Coll Min Engn, Tangshan 063009, Peoples R China
推荐引用方式 GB/T 7714	Chen, Jun,Zhu, Yuanli,Wu, Yongsheng,et al. A Neural Network Model for K(lambda) Retrieval and Application to Global K-par Monitoring[J]. PLOS ONE,2015,10(6).
APA	Chen, Jun,Zhu, Yuanli,Wu, Yongsheng,Cui, Tingwei,Ishizaka, Joji,&Ju, Yongtao.(2015).A Neural Network Model for K(lambda) Retrieval and Application to Global K-par Monitoring.PLOS ONE,10(6).
MLA	Chen, Jun,et al."A Neural Network Model for K(lambda) Retrieval and Application to Global K-par Monitoring".PLOS ONE 10.6(2015).