高空间分辨率海表CO2分压的卫星遥感反演算法:机器学习在秋季象山港的应用

doi:10.3969-j.issn.1001-909X.2023.01.007

摘要/Abstract

摘要：

近海海湾受人类活动及自然变化影响大,海水碳源汇格局变化影响机制极其复杂。由于海湾空间尺度小,需要使用宽波段的高空间分辨率卫星遥感对海-气CO₂通量进行监测评估。相对于传统公里级的水色卫星资料,海-气CO₂通量定量估算的关键参数——海表CO₂分压(sea surface partial pressure of CO₂, pCO₂)遥感反演在小尺度海湾具有极大的挑战性。该文以秋季象山港为例,利用走航观测pCO₂数据及近5年哨兵2号(Sentinel-2)卫星影像,采用支持向量机(support vector machine, SVM)机器学习的方法,基于Sentinel-2遥感反射率及其比值,建立了海表pCO₂的遥感反演算法。算法验证结果显示决定系数为0.92,均方根误差为23.23 μatm,遥感反演结果与实测值具有较高一致性。在此基础上,制作了2017—2021年秋季(9—11月)象山港海表pCO₂遥感产品,结果表明,象山港海表pCO₂整体上呈现从湾顶向湾口递减的趋势,均值为514.56 μatm,其中,湾内pCO₂均值为551.94 μatm,湾外pCO₂均值为477.19 μatm,整体呈现为大气CO₂的源,且5年间秋季pCO₂没有显著趋势性变化。结合多参数走航实测数据分析发现,2021年象山港秋季海表pCO₂受物理混合作用及生物活动的共同调控。海表温度(sea surface temperature, SST)与pCO₂具有良好的正相关关系,且SST对pCO₂的影响主要体现在碳酸盐热力学平衡作用上。此外,平均温度归一化的pCO₂(NpCO₂)与海水盐度和溶解氧饱和度均具有良好的负相关关系,NpCO₂与海水盐度的关系是潮汐作用下湾内和外海水体交换的结果;长时间序列的遥感数据分析也证实湾内、湾外pCO₂与平均潮高具有较为一致的变化趋势,且这种趋势在湾外强于湾内。该研究构建了一套海湾小尺度pCO₂遥感反演方法,为后续海-气CO₂通量大范围、长时序遥感监测奠定了良好基础。

关键词: 近海海湾, 海表pCO₂, 支持向量机, 高空间分辨率卫星遥感, 象山港

Abstract:

Coastal bays are greatly affected by human activities and natural changes, and the influence mechanism of variation in seawater carbon source and sink patterns is extremely complex. Due to the small spatial scale of the bay, it is necessary to use wide-bands high-spatial resolution satellite remote sensing for monitoring the air-sea CO₂ flux. Compared with the traditional kilometer-level ocean color satellite data, the retrieval of the sea surface partial pressure of CO₂ (pCO₂), the key parameter to calculate air-sea CO₂ flux, is extremely challenging in small-scale bays. Taking Xiangshan Bay in Zhejiang Province in autumn as an example, a satellite retrieval algorithm for sea surface pCO₂ was proposed based on the in situ pCO₂ data and Sentinel-2 satellite images in the past five years, using the machine learning method of support vector machine (SVM). The algorithm validation results showed a good performance with R² of 0.92 and RMSE of 23.23 μatm, and the satellite-derived results were consistent with the in situ values. On this basis, the satellite products of pCO₂ in Xiangshan Bay in autumn from 2017 to 2021 (September to November) were produced. The results revealed that the pCO₂ of Xiangshan Bay showed a decreasing trend from the top of the bay to the mouth of the bay, with an average value of 514.56 μatm, of which the average pCO₂ in the inner bay was 551.94 μatm and the average pCO₂ in the outer bay was 477.19 μatm, which implied that Xiangshan Bay was a source of atmospheric CO₂ as a whole. There was no significant trend change of pCO₂ in autumn in the past five years. Combined with the analysis of in situ data of multiple parameters, it was found that the sea surface pCO₂ of autumn in Xiangshan Bay in 2021 was jointly regulated by physical mixing and biological activities. Sea surface temperature (SST) had a good positive correlation with pCO₂, which was mainly reflected by the thermodynamic equilibrium of carbonate system. In addition, the normalized pCO₂(NpCO₂) with average temperature had a good negative correlation with seawater salinity and dissolved oxygen saturation. The relationship between NpCO₂ and salinity resulted from the exchange of sea water inside the bay and offshore coastal water under tidal effect. Long-time series satellite data analysis also confirmed that sea surface pCO₂ had a relatively consistent trend with the average tide height inside and outside the bay, and this trend was stronger in the outside bay than that in the inner bay. In this study, a set of pCO₂ remote sensing retrieval methods in the small-scale bay was constructed, which laid a good foundation for the subsequent long-time series satellite monitoring of sea-air CO₂ fluxes.

Key words: coastal bay, sea surface pCO₂, support vector machines, high spatial resolution satellite remote sensing, Xiangshan Bay

中图分类号:

P734
TP79

刘婷宇, 白雁, 朱伯仲, 李腾, 龚芳. 高空间分辨率海表CO₂分压的卫星遥感反演算法:机器学习在秋季象山港的应用[J]. 海洋学研究, 2023, 41(1): 82-95.

LIU Tingyu, BAI Yan, ZHU Bozhong, LI Teng, GONG Fang. Satellite retrieval algorithm of high spatial resolution sea surface partial pressure of CO₂: Application of machine learning in Xiangshan Bay in autumn[J]. Journal of Marine Sciences, 2023, 41(1): 82-95.

图/表 9

图1 研究区域、走航航线及采样站点 (图中红色三角形代表采样站位置;红色星形代表验潮站位置;黑色圆点代表pCO2极值点分布位置。)

Fig.1 The study area, cruise tracks and sampling stations (Red triangles represent the location of sampling stations; red starsrepresent the position of the tide gauging stations; black dots represent the location of the pCO2 extremums.)

表1 Sentinel-2 MSI及Landsat-8 OLI传感器波段信息

Tab.1 Band informations of Sentinel-2 MSI and Landsat-8 OLI sensor

Landsat-8 OLI				Sentinel-2 MSI
波段序号	波段信息	波长/nm	空间分辨率/m	波段序号	波段信息	波长/nm	空间分辨率/m
Band1	气溶胶	430~450	30	Band1	气溶胶	433~453	60
Band2	蓝光	450~515	30	Band2	蓝光	458~523	10
Band3	绿光	525~600	30	Band3	绿光	543~578	10
Band4	红光	630~680	30	Band4	红光	650~680	10
				Band5	植被红边	698~713	20
				Band6	植被红边	733~748	20
				Band7	植被红边	773~793	20
Band5	近红外	845~885	30	Band8	近红外	785~900	10
				Band8a	植被红边	855~875	20
				Band9	水蒸气	935~955	60
Band6	短波红外	1 560~1 660	30	Band10	短波红外	1 360~1 390	20
Band7	短波红外	2 100~2 300	30	Band11	短波红外	1 565~1 655	20
Band8	全色波段	503~676	15	Band12	短波红外	2 100~2 280	20

图2 象山港11个站点实测遥感反射率曲线 (粉色矩形区域代表Sentinel-2 MSI的8个波段范围,图顶端为波段序号;实心圆点代表各波段中心波长。)

Fig.2 Curve of in situ remote sensing reflectance of eleven stations in Xiangshan Bay (The pink rectangular areas represent the range of 8 bands of Sentinel-2 MSI, with band numbers at the top of the figure, and the solid dots represent the center wavelengths of each band.)

图3 pCO2与主要波段和波段比值遥感反射率的相关性矩阵图

Fig.3 Correlation matrix of pCO2 and remote sensing reflectance at major bands and band ratio

图4 算法验证结果 (图4a和4b分别为SVM训练集和验证集建模及验证结果,图中黑色直线为1∶1直线,橙色直线为整体拟合曲线。图4c和4d分别为走航实测(2021年11月10日)及遥感反演(2021年11月11日)的pCO2结果。图4e为对应经度下实测pCO2与反演pCO2的结果对比。)

Fig.4 Algorithm validation result (Fig.4a and fig.4b are modeling and validation results of the SVM training and validation dataset. The black straight line is the 1∶1 line; The orange line is the overall fitted curve. Fig.4c and fig.4d are pCO2 results of in situ measurement (Nov. 10, 2021) and satellite retrieval (Nov. 11, 2021), respectively. Fig.4e is the comparison between in situ pCO2 and satellite-derived pCO2 at corresponding longitude.)

图5 2017—2021年秋季象山港Sentinel-2 MSI pCO2遥感反演结果(a、c、e、g、i、k)与邻近时间Landsat-8 OLI遥感SST空间分布(b、d、f、h、j、l) (红色闪电代表大唐乌沙山发电厂位置。)

Fig.5 Spatial distribution of Sentinel-2 MSI satellite-derived pCO2 (a, c, e, g, i, k) and Landsat-8 OLI SST (b, d, f, h, j, l) of adjacent time in Xiangshan Bay in autumn 2017-2021 (Red lightning represents the location of Datang power Plant.)

图6 2017—2021年秋季象山港pCO2遥感反演空间分布 (红色闪电代表大唐乌沙山发电厂位置。)

Fig.6 Spatial distribution of satellite-derived pCO2 in Xiangshan Bay in autumn 2017-2021 (Red lightning represents the location of Datang power Plant.)

图7 2021年11月10日象山港现场走航观测要素分布及其与pCO2相关性分析 (图7a、7c、7e分别为走航测量的SST、盐度、溶解氧饱和度的空间分布;图7b为pCO2与SST的相关性,其中红色与蓝色点分别代表最靠近湾口与湾顶的数据;图7d和7f分别为NpCO2与盐度、溶解氧饱和度的相关性。图中黑色直线为全部数据拟合曲线,红色虚线为去除圆圈内数据的拟合曲线。)

Fig.7 Spatial distribution of underway measured parameters and their correlations with pCO2 in Xiangshan Bay in Nov. 10, 2021 (Fig.7a, fig.7c and fig.7e are the spatial distribution of SST, salinity and dissolved oxygen saturation, respectively. Fig.7b is the correlation between pCO2 and SST and the red and blue dots represent the data closest to the mouth and top of the bay. Fig.7d and fig.7f are the correlation between NpCO2 and salinity and dissolved oxygen saturation. The black straight lines are the fitting curves of total data, and the red dashed line is the fitting curve with the data in circles removed.)

图8 基于遥感数据的象山港秋季pCO2影响机制分析

Fig.8 Analysis of mechanism affecting satellite-derived pCO2in Xiangshan Bay in autumn

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高空间分辨率海表CO₂分压的卫星遥感反演算法:机器学习在秋季象山港的应用

Satellite retrieval algorithm of high spatial resolution sea surface partial pressure of CO₂: Application of machine learning in Xiangshan Bay in autumn

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