Towards a semi-empirical model of the sea ice thickness based on hyperspectral remote sensing in the Bohai Sea

YUAN Shuai; GU Wei; LIU Chengyu; XIE Feng

doi:10.1007/s13131-017-0996-0

基于高光谱遥感的渤海海冰厚度半经验模型

doi: 10.1007/s13131-017-0996-0

袁帅¹,
顾卫^2,,
刘成玉³,
谢锋³

1.
国家海洋环境监测中心, 大连 116023
2.
北京师范大学地表过程与资源生态国家重点实验室, 北京 610500
3.
中国科学院空间主动光电技术重点实验室, 上海 200083

基金项目: The National Natural Science Fundation of China under contract No. 41306091; the Public Science and Technology Research Funds Projects of Ocean under contract Nos 201105016 and 201505019.

计量
- 文章访问数: 1217
- HTML全文浏览量: 64
- PDF下载量: 591
- 被引次数: 0
出版历程
- 收稿日期: 2016-01-05
- 修回日期: 2016-07-08

Towards a semi-empirical model of the sea ice thickness based on hyperspectral remote sensing in the Bohai Sea

1.
National Marine Environmental Monitoring Center, State Oceanic Administration, Dalian 116023, China
2.
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
3.
Key Laboratory of Spatial Active Opto-electronic Techniques, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China

摘要

摘要: 无论是海冰灾害防灾减灾，还是冰情预报，海冰厚度都是最重要的输入参数之一。海冰厚度估算也是目前遥感监测海冰研究领域中最重要的问题。本文以渤海为研究区域，依据海冰光学辐射传输过程，在已有水深估算模型基础上，充分利用海冰在不同波段的吸收和散射特性（光谱维信息），提出了一个以高光谱遥感数据为基础的适用于一年生海冰厚度半经验模型（Semi-Empirical Model of Sea Ice Thickness，SEMSIT）。该模型用1088 nm处的反射峰高度估算像元级别的综合消光系数，用1550-1750 nm波段的反射率估算像元级别的海冰表面反射率。将该模型应用于Hyperion高光谱图像估算渤海海冰厚度。初步的验证结果表明，所提出的模型及其参数化方案可以有效克服海冰消光系数和表面反射率在时空上的异质性所带来的海冰厚度估算误差。为今后高光谱数据在海冰厚度估算方面的应用提供了实用化的半经验模型和可行的参数化方案。
- 渤海 /
- 海冰厚度 /
- 高光谱遥感 /
- 半经验模型
Abstract: Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semi-empirical model of the sea ice thickness (SEMSIT) that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands (spectral dimension information) are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550-1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided.
- Bohai Sea /
- sea ice thickness /
- hyperspectral remote sensing /
- semi-empirical model

HTML全文

参考文献(38)

Anderson G P, Felde G W, Hoke M L, et al. 2002. MODTRAN4-based atmospheric correction algorithm:FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes). In:Shen S S, Lewis P E, eds. Proceedings of Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII. Bellingham:International Society for Optics and Photonics, 65-71

Austin R W, Halikas G. 1976. The Index of Refraction of Seawater. San Diego:University of California

Beck R. 2003. EO-1 User Guide v. 2.3. Ohio:University of Cincinnati

Feng Shizuo, Li Fengqi, Li Shaojing. 1999. An Introduction to Marine Science (in Chinese). Beijing:China Higher Education Press, 463

Grenfell T C. 1983. A theoretical model of the optical properties of sea ice in the visible and near infrared. Journal of Geophysical Re-search, 88(C14):9723-9735

Gu Wei, Lin Yebin, Xu Yingjun, et al. 2012. Sea ice desalination under the force of gravity in low temperature environments. Desalination, 295:11-15

Gu Wei, Liu Chengyu, Yuan Shuai, et al. 2013. Spatial distribution characteristics of seaice-hazard risk in Bohai, China. Annals of Glaciology, 54(62):73-79

Guo Qiaozhen, Gu Wei, Li Jing, et al. 2008. Research on the seaice disaster risk in Bohai Sea based on the remote sensing. Journal of Catastrophology (in Chinese), 23(2):10-14, 18

Ji Shunying, Yue Qianjin. 2011. Engineering Sea Ice-The Numerical Model and Its Application (in Chinese). Beijing:Science Press

Kauufman L, Rousseeuw P J. 2009. Finding Groups in Data:An Intro-duction to Cluster analysis. New York:John Wiley & Sons

Lee Z, Carder K L, Mobley C D, et al. 1999. Hyperspectral remote sensing for shallow waters:2. Deriving bottom depths and wa-ter properties by optimization. Applied Optics, 38(18):3831-3843

Li Zhijun, Kong Xiangpeng, Zhang Yong, et al. 2009. Field investigations of piled ice forming in coastal area. Journal of Dalian Maritime University (in Chinese), 35(3):9-12

Li Zhijun, Lu Peng, Sodhi D S. 2004. Ice engineering sub-areas in Bo-hai from ice physical and mechanical parameters. Advances in Water Science (in Chinese), 15(5):598-602

Light B, Maykut G A, Grenfell T C. 2003. A two-dimensional Monte Carlo model of radiative transfer in sea ice. Journal of Geophysical Research, 108(C7):3219

Liu Chengyu, Chao Jinlong, Gu Wei, et al. 2014a. On the surface roughness characteristics of the land fast seaice in the Bohai Sea. Acta Oceanologica Sinica, 33(7):97-106

Liu Chengyu, Chao Jinlong, Gu Wei, et al. 2015a. Estimation of sea ice thickness in the Bohai Sea using a combination of VIS/NIR and SAR images. GIScience & Remote Sensing, 52(2):115-130

Liu Meijie, Dai Yongshou, Zhang Jie, et al. 2015b. PCA-based seaice image fusion of optical data by HIS transform and SAR data by wavelet transform. Acta Oceanologica Sinica, 34(3):59-67

Liu Chengyu, Gu Wei, Li Lantao, et al. 2013. Sea ice monitoring for the Bohai Sea based on the Hyperion image. Marine Science Bulletin (in Chinese), 32(2):200-207

Liu Chengyu, Shao Honglan, Xie Feng, et al. 2014b. Sea ice density es-timation in the Bohai Sea using the hyperspectral remote sens-ing technology. In:Larar A M, Suzuki M, Wang Jianyu, eds. Pro-ceedings of SPIE 9263, Multispectral, Hyperspectral, and Ul-traspectral Remote Sensing Technology, Techniques and Ap-plications V. Bellingham:International Society for Optics and Photonics, 92632T-1-8

Luo Yawei, Wu Huiding, Zhang Yunfei, et al. 2004. Application of the HY-1 satellite to sea ice monitoring and forecasting. Acta Oceanologica Sinica, 23(3):251-266

Mobley C D. 1995. Hydrolight 3.0 User's Guide. Menlo Park:SRI In-ternational

Ning Li, Xie Feng, Gu, Wei, et al. 2009. Using remote sensing to estim-ate sea ice thickness in the Bohai Sea, China based on ice type. International Journal of Remote Sensing, 30(17):4539-4552

Pearlman J S, Barry P S, Segal C C, et al. 2003. Hyperion, a space-based imaging spectrometer. IEEE Transactions on Geoscience and Remote Sensing, 41(6):1160-1173

Shi Lijian, Karvonen J, Cheng Bin, et al. 2014. Sea ice thickness retrieval from SAR imagery over Bohai sea. In:Proceedings of 2014 IEEE International Geoscience and Remote Sensing Sym-posium. New Jersey:Institute of Electrical and Electronics En-gineers, 4864-4867

Su Hua, Wang Yunpeng. 2012. Using MODIS data to estimate sea ice thickness in the Bohai Sea (China) in the 2009-2010 winter. Journal of Geophysical Research, 117(C10):C10018

Tong Qingxi, Zhang Bing, Zheng Lanfen. 2006. Hyperspectral Re-mote Sensing (in Chinese). Beijing:China Higher Education Press

Warren S G. 1984. Optical constants of ice from the ultraviolet to the microwave. Applied Optics, 23(8):1206-1225

Wu Kuiqiao, Xu Ying, Hao Yimeng. 2005. Application in sea ice remote sensing of MODIS data. Maritime Forecasts (in Chinese), 22(S):44-49

Wu Longtao, Wu Huiding, Sun Lantao, et al. 2006. Retrieval of sea ice in the Bohai Sea from MODIS data. Periodical of Ocean Uni-versity of China (in Chinese), 36(2):173-179

Xie Simei, Bao Chenglan, Jiang Dezhong, et al. 2001. Role of sea ice in air-sea exchange and its relation to sea fog. Chinese Journal of Polar Science, 12(2):119-132

Xie Feng, Gu Wei, Ha Si, et al. 2006. An experimental study on the spectral characteristics of one year-old sea ice in the Bohai Sea, China. International Journal of Remote Sensing, 27(14):3057-3063

Xie Feng, Gu Wei, Yuan Yi, et al. 2003. Estimation of sea ice thickness for sea ice resources in Liaodong Gulf using remote sensing. Resources Science (in Chinese), 25(3):17-23

Yang Guojin. 2000. Sea Ice Engineering (in Chinese). Beijing:China Petroleum Industry Press, 1-90, 455-480

Yuan Shuai, Gu Wei, Xu Yingjun, et al. 2012. The estimate of sea ice resources quantity in the Bohai Sea based on NOAA/AVHRR data. Acta Oceanologica Sinica, 31(1):33-40

Zege E P, Malinka A V, Katsev I L, et al. 2013. New approach for radi-ative transfer in sea ice and its application for sea ice satellite remote sensing. AIP Conference Proceedings, 1531(1):43-46

Zhang Xi. 2011. Research of sea ice thickness detection by polarimet-ric SAR in Bohai Sea (in Chinese)[dissertation]. Qingdao:Ocean University of China

Zhang Xi, Dierking W, Zhang Jie, et al. 2015. A polarimetric decom-position method for ice in the Bohai Sea using C-band PolSAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(1):47-66

Zhang Xi, Zhang Jie, Meng Junmin, et al. 2013. Analysis of multi-di-mensional SAR for determining the thickness of thin ice sea ice in the Bohai Sea. Chinese Journal of Oceanology and Limno-logy, 31(3):681-698

施引文献

资源附件(0)

访问统计

点击查看大图

计量

文章访问数: 1217
HTML全文浏览量: 64
PDF下载量: 591
被引次数: 0

基于高光谱遥感的渤海海冰厚度半经验模型

doi: 10.1007/s13131-017-0996-0

计量

出版历程

Towards a semi-empirical model of the sea ice thickness based on hyperspectral remote sensing in the Bohai Sea

计量

出版历程

目录