A newly developed ocean significant wave height retrieval method from Envisat ASAR wave mode imagery

FAN Chenqing; WANG Xiaochen; ZHANG Xudong; GAO Dong

doi:10.1007/s13131-019-1480-2

Volume 38 Issue 9

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Article Navigation > Acta Oceanologica Sinica > 2019 > 38(9): 120-127

FAN Chenqing, WANG Xiaochen, ZHANG Xudong, GAO Dong. A newly developed ocean significant wave height retrieval method from Envisat ASAR wave mode imagery[J]. Acta Oceanologica Sinica, 2019, 38(9): 120-127. doi: 10.1007/s13131-019-1480-2

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FAN Chenqing, WANG Xiaochen, ZHANG Xudong, GAO Dong. A newly developed ocean significant wave height retrieval method from Envisat ASAR wave mode imagery[J]. Acta Oceanologica Sinica, 2019, 38(9): 120-127. doi: 10.1007/s13131-019-1480-2

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A newly developed ocean significant wave height retrieval method from Envisat ASAR wave mode imagery

doi: 10.1007/s13131-019-1480-2

1.
First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
2.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China;Laboratory of Target Microwave Properties, Deqing Academy of Satellite Applications, Deqing 313200, China
3.
First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China;College of information and engineering, Ocean University of China, Qingdao 266071, China

Received Date: 2018-01-19

Abstract

Abstract

The main objective of this paper is to propose a newly developed ocean Significant Wave Height (SWH) retrieval method from Envisat Advanced Synthetic Aperture Radar (ASAR) imagery. A series of wave mode imagery from January, April and May of 2011 are collocated with ERA-Interim reanalysis SWH data. Based on the matched datasets, a simplified empirical relationship between 22 types of SAR imagery parameters and SWH products is developed with the Genetic Algorithms Partial Least-Squares (GA-PLS) model. Two major features of the backscattering coefficient σ₀ and the frequency parameter S₁₀ are chosen as the optimal training feature subset of SWH retrieval by using cross validation. In addition, we also present a comparison of the retrieval results of the simplified empirical relationship with the collocated ERA-Interim data. The results show that the assessment index of the correlation coefficient, the bias, the root-mean-square error of cross validation (RMSECV) and the scattering index (SI) are 0.78, 0.07 m, 0.76 m and 0.5, respectively. In addition, the comparison of the retrieved SWH data between our simplifying model and the Jason-2 radar altimeter data is proposed in our study. Moreover, we also make a comparison of the retrieval of SWH data between our developed model and the well-known CWAVE_ENV model. The results show that satisfying retrieval results are acquired in the low-moderate sea state, but major bias appears in the high sea state, especially for SWH>5 m.
- significant wave height,
- Envisat ASAR,
- GA-PLS,
- optimal feature subset

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References

Bruck M, Lehner S. 2015. TerraSAR-X/TanDEM-X Sea state measurements using the XWAVE algorithm. International Journal of Remote Sensing, 36(15):3890-3912, doi: 10.1080/01431161.2015.1051630

Collard F, Ardhuin F, Chapron B. 2005. Extraction of coastal ocean wave fields from SAR images. IEEE Journal of Oceanic Engineering, 30(3):526-533, doi: 10.1109/JOE.2005.857503

Engen G, Johnsen H. 1995. SAR-ocean wave inversion using image cross spectra. IEEE Transactions on Geoscience and Remote Sensing, 33(4):1047-1056, doi: 10.1109/36.406690

Geladi P, Kowalski B P. 1986. Partial least-squares regression:a tutorial. Analytica Chimica Acta, 185:1-17, doi: 10.1016/0003-2670(86)80028-9

Gonzalez F I, Beal R C, Brown W E, et al. 1979. Seasat synthetic aperture radar:ocean wave detection capabilities. Science, 204(4400):1418-1421, doi: 10.1126/science.204.4400.1418

Grieco G, Lin Wenming, Migliaccio M, et al. 2016. Dependency of the sentinel-1 azimuth wavelength cut-off on significant wave height and wind speed. International Journal of Remote Sensing, 37(21):5086-5104, doi: 10.1080/01431161.2016.1226525

Hasselmann K, Hasselmann S. 1991. On the nonlinear mapping of an ocean wave spectrum into a synthetic aperture radar image spectrum and its inversion. Journal of Geophysical Research:Oceans, 96(C6):10713-10729, doi: 10.1029/91JC00302

He Yijun, Perrie W, Xie Tao, et al. 2004. Ocean wave spectra from a linear polarimetric SAR. IEEE Transactions on Geoscience and Remote Sensing, 42(11):2623-2631, doi: 10.1109/TGRS.2004.836813

Kawamura K, Watanabe N, Sakanoue S, et al. 2010. Testing genetic algorithm as a tool to select relevant wavebands from field hyperspectral data for estimating pasture mass and quality in a mixed sown pasture using partial least squares regression. Grassland Science, 56(4):205-216, doi: 10.1111/grs.2010.56.issue-4

Leardi R, González A L. 1998. Genetic algorithms applied to feature selection in PLS regression:how and when to use them. Chemometrics and Intelligent Laboratory Systems, 41(2):195-207, doi: 10.1016/S0169-7439(98)00051-3

Li Bingyan, Gong Huaze, Shao Yun, et al. 2016. The consistency between Na content distribution at the subsurface and the lake body's movement in Lop Nur. International Journal of Digital Earth, 9(7):662-675, doi: 10.1080/17538947.2015.1112439

Li Xiaoming, Lehner S, Bruns T. 2011. Ocean wave integral parameter measurements using envisat ASAR wave mode data. IEEE Transactions on Geoscience and Remote Sensing, 49(1):155-174, doi: 10.1109/TGRS.2010.2052364

Li Lin, Ustin S L, Riano D. 2007. Retrieval of fresh leaf fuel moisture content using genetic algorithm partial least squares (GA-PLS) modeling. IEEE Geoscience and Remote Sensing Letters, 4(2):216-220, doi: 10.1109/LGRS.2006.888847

Long R B. 1980. The statistical evaluation of directional spectrum estimates derived from pitch/roll buoy data. Journal of Physical Oceanography, 10(6):944-952, doi: 10.1175/1520-0485(1980)010<0944:TSEODS>2.0.CO;2

Longuet-Higgins M S, Cartwright D E, Smith N D. 1963. Observations of the directional spectrum of sea waves using the motions of a floating buoy. In:Ocean Wave Spectrum. Englewood Cliffs, NJ:Prentice-Hall Inc, 111–136

Mastenbroek C, de Valk C F. 2000. A semiparametric algorithm to retrieve ocean wave spectra from synthetic aperture radar. Journal of Geophysical Research:Oceans, 105(C2):3497-3516, doi: 10.1029/1999JC900282

Monaldo F. 1988. Expected differences between buoy and radar altimeter estimates of wind speed and significant wave height and their implications on buoy-altimeter comparisons. Journal of Geophysical Research:Oceans, 93(C3):2285-2302, doi: 10.1029/JC093iC03p02285

Pleskachevsky A L, Rosenthal W, Lehner S. 2016. Meteo-marine parameters for highly variable environment in coastal regions from satellite radar images. ISPRS Journal of Photogrammetry and Remote Sensing, 119:464-484, doi: 10.1016/j.isprsjprs.2016.02.001

Schulz-Stellenfleth J, K?nig T, Lehner S. 2007. An empirical approach for the retrieval of integral ocean wave parameters from synthetic aperture radar data. Journal of Geophysical Research:Oceans, 112(C3):C03019

Shao Weizeng, Wang Jing, Li Xiaofeng, et al. 2017. An empirical algorithm for wave retrieval from Co-polarization X-band SAR imagery. Remote Sensing, 9(7):711, doi: 10.3390/rs9070711

Shao Weizeng, Zheng Zhang, Li Xiaofeng, et al. 2016. Ocean wave parameters retrieval from sentinel-1 SAR imagery. Remote Sensing, 8(9):707, doi: 10.3390/rs8090707

Song Kaishan, Li Lin, Li Shuai, et al. 2012. Hyperspectral retrieval of phycocyanin in potable water sources using genetic algorithm-partial least squares (GA-PLS) modeling. International Journal of Applied Earth Observation and Geoinformation, 18:368-385, doi: 10.1016/j.jag.2012.03.013

Stopa J E, Mouche A. 2017. Significant wave heights from sentinel-1 SAR:Validation and applications. Journal of Geophysical Research:Oceans, 122(3):1827-1848, doi: 10.1002/2016JC012364

Wan Yong, Zhang Jie, Meng Junmin, et al. 2015. Exploitable wave energy assessment based on ERA-Interim reanalysis data-a case study in the East China Sea and the South China Sea. Acta Oceanologica Sinica, 34(9):143-155, doi: 10.1007/s13131-015-0641-8

Wang He, Mouche A, Husson R, et al. 2016. A global distribution of crossing swell from Envisat ASAR Wave Mode data based on swell propagation. In:Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing, China:IEEE, 4657–4660

Wang He, Zhu Jianhua, Yang Jingsong. 2014. Error Analysis on ESA's envisat ASAR wave mode significant wave height retrievals using triple collocation model. Remote Sensing, 6(12):12217-12233, doi: 10.3390/rs61212217

Wang He, Zhu Jianhua, Yang Jingsong, et al. 2012. A semiempirical algorithm for SAR wave height retrieval and its validation using Envisat ASAR wave mode data. Acta Oceanologica Sinica, 31(3):59-66, doi: 10.1007/s13131-012-0206-z

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