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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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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