Machine learning-based remote sensing retrievals of dissolved organic carbon in the Yangtze River Estuary

doi:10.3969/j.issn.1000-5641.2024.04.012

Abstract

Abstract:

Dissolved organic carbon (DOC) is the largest reservoir of active organic matter in the ocean. Accurate characterization of the spatial and temporal patterns of DOC in large-river estuaries and neighboring coastal margins will help improve our understanding of biogeochemical processes and the fate of fluvial DOC across the estuary−coastal ocean continuum. By retrieving the absorption properties of colored dissolved organic matter (CDOM) in the dissolved organic matter (DOM) pool using machine learning models, and based on the correlation between CDOM absorption and DOC concentrations, we developed an ocean DOC algorithm for the GOCI satellite. The results indicated that the Nu-Supporting Vector Regression model performed best in retrieving CDOM absorption properties, with mean absolute percent differences (MAPD) of 32% and 8.6% for the CDOM absorption coefficient at 300 nm (a_CDOM(300)) and CDOM spectral slope over the wavelength range of 275 ~ 295 nm (S_275–295). Estimates of DOC concentrations based on the seasonal linear relationship between a_CDOM(300) and DOC were achieved with high retrieval accuracy, with MAPD of 11% and 14% for the training dataset using field measurements and validation datasets on satellite platforms, respectively. Application of the DOC algorithm to GOCI satellite imagery revealed that DOC levels varied dramatically at both seasonal and hourly scales. Elevated surface DOC concentrations were largely associated with summer and lower DOC concentrations in winter as a result of seasonal cycles of Yangtze River discharges. The DOC also changed rapidly on an hourly scale due to the influence of the tide and local wind regimes. This study provides a useful method to improve our understanding of DOC dynamics and their environmental controls across the estuarine −coastal ocean continuum.

Key words: GOCI (geostationary ocean color imager), colored dissolved organic matter, machine learning, the Yangtze River Estuary, dissolved organic carbon

CLC Number:

P733.3

Hao CHEN, Xianqiang HE, Run LI, Fang CAO. Machine learning-based remote sensing retrievals of dissolved organic carbon in the Yangtze River Estuary[J]. Journal of East China Normal University(Natural Science), 2024, 2024(4): 123-136.

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References 38

1	HANSELL D A, CARLSON C A.. Deep ocean gradients in dissolved organic carbon concentrations. Nature, 1998, 395 (6699): 263- 266.
2	戴民汉, 翟惟东, 鲁中明, 等.. 中国区域碳循环研究进展与展望. 地球科学进展, 2004, 19 (1): 120- 130.
3	潘德炉, 刘琼, 白雁.. DOC遥感研究进展——基于全球大河DOC与CDOM保守性特征. 海洋学报(中文版), 2012, 34 (4): 1- 11.
4	FICHOT C G, BENNER R.. The spectral slope coefficient of chromophoric dissolved organic matter (S275–295) as a tracer of terrigenous dissolved organic carbon in river-influenced ocean margins. Limnology and Oceanography, 2012, 57 (5): 1453- 1466.
5	MASSICOTTE P, ASMALA E, STEDMON C, et al.. Global distribution of dissolved organic matter along the aquatic continuum: Across rivers, lakes and oceans. Science of the Total Environment, 2017, 609, 180- 191.
6	GRIFFIN C G, MCCLELLAND J W, FREY K E, et al.. Quantifying CDOM and DOC in major Arctic rivers during ice-free conditions using Landsat TM and ETM + data. Remote Sensing of Environment, 2018, 209, 395- 409.
7	CHEN J, ZHU W, TIAN Y Q, et al.. Monitoring dissolved organic carbon by combining Landsat-8 and Sentinel-2 satellites: Case study in Saginaw River estuary, Lake Huron. The Science of the Total Environment, 2020, 718, 137374.
8	MANNINO A, RUSS M E, HOOKER S B.. Algorithm development and validation for satellite-derived distributions of DOC and CDOM in the U. S. Middle Atlantic Bight. Journal of Geophysical Research, 2008, 113 (C7): C07051.
9	HELMS J R, STUBBINS A, RITCHIE J D, et al.. Absorption spectral slopes and slope ratios as indicators of molecular weight, source, and photobleaching of chromophoric dissolved organic matter. Limnology and Oceanography, 2008, 53 (3): 955- 969.
10	FICHOT C G, BENNER R.. A novel method to estimate DOC concentrations from CDOM absorption coefficients in coastal waters. Geophysical Research Letters, 2011, 38 (3): L03610.
11	VANTREPOTTE V, DANHIEZ F, LOISEL H, et al.. CDOM-DOC relationship in contrasted coastal waters: Implication for DOC retrieval from ocean color remote sensing observation. Optics Express, 2015, 23 (1): 33- 54.
12	YU X, SHEN F, LIU Y.. Light absorption properties of CDOM in the Changjiang (Yangtze) estuarine and coastal waters: An alternative approach for DOC estimation. Estuarine, Coastal and Shelf Science, 2016, 181, 302- 311.
13	CAO F, TZORTZIOU M, HU C, et al.. Remote sensing retrievals of colored dissolved organic matter and dissolved organic carbon dynamics in North American estuaries and their margins. Remote Sensing of Environment, 2018, 205, 151- 165.
14	BAI Y, HE X, PAN D, et al.. Summertime Changjiang River plume variation during 1998–2010. Journal of Geophysical Research: Oceans, 2014, 119 (9): 6238- 6257.
15	WU H, SHEN J, ZHU J, et al.. Characteristics of the Changjiang plume and its extension along the Jiangsu Coast. Continental Shelf Research, 2014, 76, 108- 123.
16	YANG B, CAO L, LIU S, et al.. Biogeochemistry of bulk organic matter and biogenic elements in surface sediments of the Yangtze River Estuary and adjacent sea. Marine Pollution Bulletin, 2015, 96 (1): 471- 484.
17	GUO L, SANTSCHI P H, WARNKEN K W.. Dynamics of dissolved organic carbon (DOC) in oceanic environments. Limnology and oceanography, 1995, 40 (8): 1392- 1403.
18	RUDDICK K G, DE CAUWER V, PARK Y, et al.. Seaborne measurements of near infrared water-leaving reflectance: The similarity spectrum for turbid waters. Limnology and Oceanography, 2006, 51 (2): 1167- 1179.
19	RYU J, HAN H, CHO S, et al.. Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS). Ocean Science Journal, 2012, 47 (3): 223- 233.
20	HE X, BAI Y, PAN D, et al.. Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters. Optics Express, 2012, 20 (18): 20754- 20770.
21	HE X, BAI Y, PAN D, et al.. Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters. Remote Sensing of Environment, 2013, 133, 225- 239.
22	LIU D, BAI Y, HE X, et al.. Satellite estimation of particulate organic carbon flux from Changjiang River to the estuary. Remote Sensing of Environment, 2019, 223, 307- 319.
23	ZHAO J, CAO W, XU Z, et al.. Estimating CDOM concentration in highly turbid estuarine coastal waters. Journal of Geophysical Research: Oceans, 2018, 123 (8): 5856- 5873.
24	吴志明, 李建超, 王睿, 等.. 基于随机森林的内陆湖泊水体有色可溶性有机物(CDOM)浓度遥感估算. 湖泊科学, 2018, 30 (4): 979- 991.
25	孙璐, 蒋锦刚, 朱渭宁.. 基于GOCI影像的长江口及其邻近海域CDOM遥感反演及其日内变化研究. 海洋学报, 2017, 39 (9): 133- 145.
26	LIU H, LI Q, BAI Y, et al.. Improving satellite retrieval of oceanic particulate organic carbon concentrations using machine learning methods. Remote Sensing of Environment, 2021, 256, 112316.
27	SHEN M, LUO J, CAO Z, et al.. Random forest: An optimal chlorophyll-a algorithm for optically complex inland water suffering atmospheric correction uncertainties. Journal of Hydrology, 2022, 615, 128685.
28	SUN X, ZHANG Y, ZHANG Y, et al.. Machine learning algorithms for chromophoric dissolved organic matter (CDOM) estimation based on Landsat 8 images. Remote Sensing, 2021, 13 (18): 3560.
29	MOREL A, GENTILI B.. A simple band ratio technique to quantify the colored dissolved and detrital organic material from ocean color remotely sensed data. Remote Sensing of Environment, 2009, 113 (5): 998- 1011.
30	SISWANTO E, TANG J, YAMAGUCHI H, et al.. Empirical ocean-color algorithms to retrieve chlorophyll-a, total suspended matter, and colored dissolved organic matter absorption coefficient in the Yellow and East China Seas. Journal of Oceanography, 2011, 67 (5): 627- 650.
31	MANNINO A, NOVAK M G, HOOKER S B, et al.. Algorithm development and validation of CDOM properties for estuarine and continental shelf waters along the northeastern US coast. Remote Sensing of Environment, 2014, 152, 576- 602.
32	MÉLIN F, ZIBORDI G, BERTHON J, et al.. Assessment of MERIS reflectance data as processed with SeaDAS over the European seas. Optics Express, 2011, 19 (25): 25657- 25671.
33	MOON J, AHN Y, RYU J, et al.. Development of ocean environmental algorithms for Geostationary Ocean Color Imager (GOCI). Korean Journal of Remote Sensing, 2010, 26 (2): 189- 207.
34	高源, 明玥, 高磊.. 2019年长江口及其邻近海域溶解有机物的分布和季节变化特征. 海洋环境科学, 2022, 41 (1): 40- 47.
35	张淑坤, 明玥, 高磊.. 2020年夏季长江流域特大洪水期间长江口POC和DOC的分布特征. 海洋环境科学, 2022, 41 (5): 653- 659.
36	李倩, 张珊珊, 线薇微.. 长江口有机碳的时空分异及耦合行为. 海洋环境科学, 2022, 41 (1): 24- 31.
37	叶君, 姚鹏, 徐亚宏, 等.. 长江口盐度梯度下不同形态碳的分布、来源与混合行为. 海洋学报, 2019, 41 (4): 15- 26.
38	SONG S, GAO L, GE J, et al.. Tidal effects on variations in organic and inorganic biogeochemical components in Changjiang (Yangtze River) Estuary and the adjacent East China Sea. Journal of Marine Systems, 2022, 227, 103692.

模型	训练集(N = 49)			验证集(N = 22)
模型	RMSE/m^?1	MAPD/%	Bias/%	RMSE/m^?1	MAPD/%	Bias/%
SVR	0.67	27	2.68	1.00	38	5.78
NuSVR	0.75	26	?2.97	1.05	32	?1.89
RF	0.45	16	0.76	1.42	45	?5.14
BPNN	0.54	19	?0.01	0.92	34	4.55
XGB	0.24	6	?4.15	1.23	39	?7.35
Moon	??	??	??	3.56	95	?96.15

模型	训练集 (N = 49)			验证集 (N = 22)
模型	RMSE /nm^-1	MAPD /%	Bias /%	RMSE /nm^-1	MAPD /%	Bias /%
SVR	0.0020	7.2	?0.342	0.0029	8.8	?2.712
NuSVR	0.0022	7.3	?0.934	0.0031	8.6	?3.260
RF	0.0012	4.4	?0.350	0.0030	9.5	?2.277
BPNN	0.0015	5.8	?0.003	0.0034	10.5	?2.224
XGB	0.0008	3.2	1.314	0.0030	10.0	?1.266

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