华东师范大学学报（自然科学版） >

2024 , Vol. 2024 >Issue 4: 123 - 136

DOI: https://doi.org/10.3969/j.issn.1000-5641.2024.04.012

遥感与地理信息系统

基于机器学习的长江口表层水体溶解有机碳遥感反演研究

  • 陈灏 ,
  • 何贤强 ,
  • 李润 ,
  • 曹芳
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  • 1. 华东师范大学 河口海岸学国家重点实验室, 上海　200241
    2. 自然资源部 第二海洋研究所 卫星海洋环境动力学国家重点实验室, 杭州　310012

收稿日期: 2023-03-22

  录用日期: 2023-04-18

  网络出版日期: 2024-07-23

基金资助

国家自然科学基金 (41906145)

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

  • Hao CHEN ,
  • Xianqiang HE ,
  • Run LI ,
  • Fang CAO
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  • 1. State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai　200241, China
    2. State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou　310012, China

Received date: 2023-03-22

  Accepted date: 2023-04-18

  Online published: 2024-07-23

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摘要

溶解有机碳 (dissolved organic carbon, DOC) 是海洋中最大的活跃有机碳库. 精确刻画大河河口及其近海水体表层DOC浓度的时空分布, 有助于更好地理解河流输送的有机碳在河口近海经历的生物地化过程及在该区域的归宿. 本研究采用机器学习方法, 通过反演水体溶解态有机碳库中的有色溶解有机物 (colored dissolved organic matter, CDOM) 的吸收光谱信息, 并基于其与水体DOC浓度的相关关系, 发展了基于地球静止轨道水色成像仪 (geostationary ocean color imager, GOCI) 的DOC遥感反演模型. 结果表明, Nu支持向量回归 (nu-supporting vector regression, NuSVR) 方法可准确反演CDOM光谱吸收特性 (如验证集CDOM在300 nm处的吸收系数aCDOM(300)和275 ~ 295 nm处的光谱斜率S275–295的平均绝对误差 (mean absolute percent differences, MAPD) 分别为32%和8.6%). 分别基于该区域表层水体CDOM光谱吸收特性与DOC浓度之间表现的3种不同的相关关系进行DOC算法构建, 结果表明, 基于aCDOM(300)与DOC浓度之间的线性相关, 并考虑这一相关关系的季节性差异所构建的DOC反演算法可较为准确地反演水体DOC浓度, DOC反演现场数据验证集和卫星验证集的MAPD分别为11%和14%. 将构建的DOC算法模型应用到GOCI卫星图像上, 结果显示, 受长江径流影响, 季节尺度上, 长江口夏季水体表层DOC浓度显著高于冬季; 而受潮汐、风场等因素的影响, 小时尺度上河口近岸海域DOC分布呈现逐时高动态变化. 本研究利用卫星遥感反演河口近海水体DOC浓度, 为进一步在不同时间尺度上研究该区域水体DOC动态变化及驱动因素提供了有效手段.

关键词： 地球静止轨道水色成像仪; 有色溶解有机物; 机器学习; 长江口; 溶解有机碳

本文引用格式

陈灏 , 何贤强 , 李润 , 曹芳 . 基于机器学习的长江口表层水体溶解有机碳遥感反演研究[J]. 华东师范大学学报（自然科学版）, 2024 , 2024(4) : 123 -136 . DOI: 10.3969/j.issn.1000-5641.2024.04.012

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 (aCDOM(300)) and CDOM spectral slope over the wavelength range of 275 ~ 295 nm (S275–295). Estimates of DOC concentrations based on the seasonal linear relationship between aCDOM(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

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