基于SMAP卫星雷达资料的海冰密集度反演技术研究

闻斌; 周旋; 种劲松; 石立坚; 叶小敏

doi:10.3969/j.issn.0253-4193.2018.06.003

基于SMAP卫星雷达资料的海冰密集度反演技术研究

doi: 10.3969/j.issn.0253-4193.2018.06.003

1.
中国人民解放军 61741 部队, 北京 100094
2.
中国人民解放军 61741 部队, 北京 100094;中国科学院电子学研究所, 北京 100190
3.
中国科学院电子学研究所, 北京 100190
4.
国家卫星海洋应用中心, 北京 100081

基金项目: 国家自然科学基金项目（41276185，41406215）。

计量
- 文章访问数: 735
- HTML全文浏览量: 12
- PDF下载量: 421
- 被引次数: 0
出版历程
- 收稿日期: 2017-06-29
- 修回日期: 2017-10-19

Sea ice concentration retrieval based on SMAP radar data

1.
61741 Troops of PLA, Beijing 100094, China
2.
61741 Troops of PLA, Beijing 100094, China;Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
3.
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
4.
National Satellite Ocean Application Service, Beijing 100081, China

摘要

摘要: SMAP是美国于2015年初发射的一颗卫星，搭载了L波段的雷达。它采用圆锥扫描方式，具有固定的入射角、较大的幅宽和千米级的分辨率，在海冰监测方面具有独特的优势。本文利用SMAP卫星雷达资料分别与德国Bremen大学海冰密集度产品和美国国家冰雪数据中心（NSIDC）海冰密集度产品建立3.125 km和25 km匹配数据集，分析了L波段雷达后向散射系数、极化比和归一化极化差与海冰密集度之间相关性，建立基于人工神经网络的海冰密集度反演算法。为了验证SMAP卫星雷达资料反演海冰密集度的精度，本文选择德国Bremen大学和美国冰雪数据中心发布的海冰密集度产品分别与SMAP海冰密集度产品进行对比分析，SMAP海冰密集度与Bremen海冰密集度的偏差为0.07、均方根误差为0.14；与NSIDC海冰密集度的偏差为0.04、均方根误差为0.18，这表明SMAP海冰密集度产品与现有业务化海冰密集度产品具有很好的一致性。
- SMAP卫星雷达资料 /
- 海冰密集度 /
- 反演 /
- 神经网络
Abstract: The Soil Moisture Active Passive (SMAP) was launched by National Aeronautics and Space Administration (NASA) in early 2015, which carried an L-band Radar with a conical scanning antenna. It has the constant incidence angle, the wide swath and the km-scale resolution. And thus it has a significant potential for observing sea ice. The paper establishes the 3.125 km matchup data set by using the University of Bremen sea ice concentration and SMAP radar data, and the 25 km matchup data set by using the National Snow and Ice Data Center (NSIDC) sea ice concentration and SMAP radar data. A sea ice concentration algorithm based on artificial neural network is proposed by the relation between sea ice concentration and HH-polarized NRCS, HH/HV polarized ratio, HH-polarized difference. The retrieved sea ice concentration is validated by using sea ice concentrations from NSDIC and the University of Bremen. The root mean square differences (RMSDs) and biases of sea ice concentration between SMAP Radar and the University of Bremen are 0.14 and 0.07, respectively. The RMSDs and biased of SMAP sea ice concentration versus NSDIC ones are 0.18 and 0.04, respectively. The results show that SMAP sea ice concentration is basically consistent with the operational sea ice concentration.
- Soil Moisture Active Passive (SMAP) Radar /
- sea ice concentration /
- retrieval /
- neural network

HTML全文

参考文献(14)

Karvonen J. Baltic sea ice concentration estimation based on C-band HH-polarized SAR data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(6):1874-1884.

Karvonen J. Baltic sea ice concentration estimation based on C-Band dual-polarized SAR Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(9):5558-5566.

Karvonen J. A sea ice concentration estimation algorithm utilizing radiometer and SAR data[J]. The Cryosphere, 2014, 8(5):1639-1650.

Berg A, Eriksson L E B. SAR algorithm for sea ice concentration-evaluation for the baltic sea[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(5):938-942.

Aldenhoff W, Berg A, Eriksson L E B. Sea ice concentration estimation from sentinel-1 synthetic aperture radar images over the fram strait[C]//2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing:IEEE, 2016:7675-7677.

Wang Lei, Scott K A, Xu Linlin, et al. Sea ice concentration estimation during melt from Dual-Pol SAR scenes using deep convolutional neural networks:a case study[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8):4524-4533.

Parkinson C L, Cavalieri D J, Gloersen P, et al. Arctic sea ice extents, areas, and trends, 1978-1996[J]. Journal of Geophysical Research:Oceans, 1999, 104(C9):20837-20856.

Synder J P. Map Projection Used by the U. S. Geological Survey[M]. Washington, D.C.:U.S. Geological Survey Bulletin, 1982.

Spreen G, Kalechke L, Heygster G. Sea ice remote sensing using AMSR-E 89 GHz channels[J]. Journal of Geophysical Research:Oceans, 2008, 113(C2):C02S03.

Haykin S S. Neural Networks, A Comprehensive Foundation[M]. 2nd ed. Englewood Cliffs:Prentice-Hall, 1999:183-196.

Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators[J]. Neural Network, 1989, 2(5):359-366.

Kaleschke L, Lüpkes C, Vihma T, et al. SSM/I sea ice remote sensing for mesoscale ocean-atmosphere interaction analysis[J]. Canadian Journal of Remote Sensing, 2001, 27(5):526-537.

Markus T, Cavalieri D J. An enhancement of the NASA team sea ice algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(3):1387-1398.

Zhou Xuan, Chong Jinsong, Yang Xiaofeng, et al. Ocean surface wind retrieval using SMAP L-Band SAR[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(1):65-74.

施引文献

资源附件(0)

访问统计