Analysis of the regional spectral properties in northwestern South China Sea based on an empirical orthogonal function

Dongyang Fu; Yuye Huang; Dazhao Liu; Shan Liao; Guo Yu; Xiaolong Zhang

doi:10.1007/s13131-020-1625-x

Volume 39 Issue 7

Jul. 2020

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Dongyang Fu, Yuye Huang, Dazhao Liu, Shan Liao, Guo Yu, Xiaolong Zhang. Analysis of the regional spectral properties in northwestern South China Sea based on an empirical orthogonal function[J]. Acta Oceanologica Sinica, 2020, 39(7): 107-114. doi: 10.1007/s13131-020-1625-x

Citation:

Dongyang Fu, Yuye Huang, Dazhao Liu, Shan Liao, Guo Yu, Xiaolong Zhang. Analysis of the regional spectral properties in northwestern South China Sea based on an empirical orthogonal function[J]. Acta Oceanologica Sinica, 2020, 39(7): 107-114. doi: 10.1007/s13131-020-1625-x

Citation:

Dongyang Fu, Yuye Huang, Dazhao Liu, Shan Liao, Guo Yu, Xiaolong Zhang. Analysis of the regional spectral properties in northwestern South China Sea based on an empirical orthogonal function[J]. Acta Oceanologica Sinica, 2020, 39(7): 107-114. doi: 10.1007/s13131-020-1625-x

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Analysis of the regional spectral properties in northwestern South China Sea based on an empirical orthogonal function

doi: 10.1007/s13131-020-1625-x

1.
College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
2.
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: The Key Projects of the Guangdong Education Department under contract No. 2019KZDXM019; the Fund of Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang) under contract No. ZJW-2019-08; High-Level Marine Discipline Team Project of Guangdong Ocean University under contract No. 002026002009; the Guangdong Graduate Academic Forum Project under contract No.230420003; the "First Class" discipline construction platform project in 2019 of Guangdong Ocean University under contract No. 231419026.

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Corresponding author: hyuye0626@163.com
Received Date: 2019-08-30
Accepted Date: 2019-10-08

Available Online: 2020-12-28

Publish Date: 2020-07-25

Abstract

Abstract

This study presents an analysis of the spectral characteristics of remote sensing reflectance (R_rs) in northwestern South China Sea based on the in situ optical and water quality data for August 2018. R_rs was initially divided into four classes, classes A to D, using the max-classification algorithm, and the spectral properties of whole R_rs were characterized using the empirical orthogonal function (EOF) analysis. Subsequently, the dominant factors in each EOF mode were determined.The results indicated that more than 95% of the variances of R_rs are partly driven by the back-scattering characteristics of the suspended matter. The initial two EOF modes were well correlated with the total suspended matter and back–scattering coefficient. Furthermore, the first EOF modes of the four classes of R_rs (A–D R_rs–EOF₁) significantly contributed to the total variances of each R_rs class. In addition, the correlation coefficients between the amplitude factors of class A–D R_rs–EOF₁ and the variances of the relevant water quality and optical parameters were better than those of the unclassified ones. The spectral shape of class A R_rs–EOF₁ was governed by the absorption characteristic of chlorophyll a and colored dissolved organic matter (CDOM). The spectral shape of class B R_rs–EOF₁ was governed by the absorption characteristic of CDOM since it exhibited a high correlation with the absorption coefficient of CDOM (a_g (λ)), whereas the spectral shape of class C R_rs–EOF₁ was governed by the back-scattering characteristics but not affected by the suspended matter. The spectral shape of class D R_rs–EOF₁ exhibited a relatively good correlation with all the water quality parameters, which played a significant role in deciding its spectral shape.
- northwestern South China Sea,
- remote sensing reflectance,
- empirical orthogonal function analysis,
- ocean color control factors

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References(20)

References

[1]	Allan T D. 1985. Remote assessment of ocean color for interpretation of satellite visible imagery: In Gordon H R and Morel A Y, eds. Lecture Notes on Coastal and Estuarine Studies. Berlin: Springer-Verlag, 114.
[2]	Dickey T D. 2001. Inherent optical properties and irradiance. In: Steele J H, ed. Encyclopedia of Ocean Sciences. Oxford, UK: Academic Press, 244–253
[3]	Fu Dongyang, Zhang Ying, Liu Dazhao, et al. 2015. Evaluation model of coastal water quality and application research based on principal component analysis—a case of Leizhou Peninsula waters. Journal of Marine Sciences (in Chinese), 33(1): 45–50
[4]	Gordon H R, Brown O B, Jacobs M M. 1975. Computed relationships between the inherent and apparent optical properties of a flat homogeneous ocean. Applied Optics, 14(2): 417–427. doi: 10.1364/AO.14.000417
[5]	Huang Yichen, Li Yan, Shao Hao, et al. 2008. Seasonal variations of sea surface temperature, chlorophyll-a and turbidity in Beibu gulf, MODIS imagery study. Journal of Xiamen University (Natural Science) (in Chinese), 47(6): 856–863
[6]	IOCCG. 2000. Remote sensing of ocean colour in coastal, and other optically-complex, waters. Dartmouth, Canada: IOCCG
[7]	Kari E, Kratzer S, Beltrán-Abaunza J M, et al. 2017. Retrieval of suspended particulate matter from turbidity–model development, validation, and application to MERIS data over the Baltic Sea. International Journal of Remote Sensing, 38(7): 1983–2003. doi: 10.1080/01431161.2016.1230289
[8]	Kim S W, Saitoh S I, Ishizaka J, et al. 2000. Temporal and spatial variability of phytoplankton pigment concentrations in the Japan Sea derived from CZCS images. Journal of Oceanography, 56(5): 527–538. doi: 10.1023/A:1011148910779
[9]	Li Qinzheng, Chen Peng, Sun Langlang, et al. 2018. A global weighted mean temperature model based on empirical orthogonal function analysis. Advances in Space Research, 61(6): 1398–1411. doi: 10.1016/j.asr.2017.12.031
[10]	Liu Zilin, Ning Xiuren, Cai Yuming. 1998. Distribution characteristics of size-fractionated chlorophyll a and productivity of phytoplankton in the Beibu Gulf. Haiyang Xuebao (in Chinese), 20(1): 50–57
[11]	Lubac B, Loisel H. 2007. Variability and classification of remote sensing reflectance spectra in the eastern English Channel and southern North Sea. Remote Sensing of Environment, 110(1): 45–58. doi: 10.1016/j.rse.2007.02.012
[12]	Ma Ronghua, Kong Weijuan, Duan Hongtao, et al. 2009. Quantitative estimation of phycocyanin concentration using MODIS imagery during the period of cyanobacterial blooming in Taihu Lake. China Environmental Science (in Chinese), 29(3): 254–260
[13]	Mobley C D. 1999. Estimation of the remote-sensing reflectance from above-surface measurements. Applied Optics, 38(36): 7442–7455. doi: 10.1364/AO.38.007442
[14]	North G R, Bell T L, Cahalan R F, et al. 1982. Sampling errors in the estimation of empirical orthogonal functions. Monthly Weather Review, 110(7): 699–706. doi: 10.1175/1520-0493(1982)110<0699:SEITEO>2.0.CO;2
[15]	Ta Zhijie, Yu Ruide, Chen Xi, et al. 2018. Analysis of the spatio-temporal patterns of dry and wet conditions in central Asia. Atmosphere, 9(1): 7. doi: 10.3390/atmos9010007
[16]	Tang Shilin, Larouche P, Niemi A, et al. 2013. Regional algorithms for remote-sensing estimates of total suspended matter in the Beaufort Sea. International Journal of Remote Sensing, 34(19): 6562–6576. doi: 10.1080/01431161.2013.804222
[17]	Xia Huayong, Li Shuhua, Shi Maochong. 2001. Three-D numerical simulation of wind-driven current and density current in the Beibu Gulf. Acta Oceanologica Sinica, 20(4): 455–472
[18]	Xie Fei, Guo Ziqi, Tian Ye, et al. 2014. The preliminary inquiry of Chlorophyll-a inversion algorihtms applicable to guanting reservoir. In: Proceedings of 2013 IEEE International Geoscience and Remote Sensing Symposium. Melbourne, VIC, Australia: IEEE, 3785–3788. doi: 10.1109/IGARSS.2013.6723655
[19]	Ye Huping, Li Junsheng, Li Tongji, et al. 2016. Spectral classification of the Yellow Sea and implications for coastal ocean color remote sensing. Remote Sensing, 8(4): 321. doi: 10.3390/rs8040321
[20]	Zhang Tinglu, Shi Yingni. 2005. A method to classify case I and case II waters. Periodical of Ocean University of China (in Chinese), 35(5): 849–853