Satellite remote sensing monitoring of chlorophyll a mass concentration in a typical marine ranching area of Gouqi Island
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摘要: 海洋牧场水质监测对其高质量可持续发展具有关键意义。叶绿素a（chlorophyll a, Chl a）是浮游植物生物量和水体富营养化程度的重要指标，对环境评估与风险管理至关重要。卫星遥感具有快速观测、高时空覆盖等优势，目前尚缺乏针对海洋牧场区的专用遥感产品，且养殖设施会对卫星遥感信号造成影响，给Chl a质量浓度遥感反演带来误差。本文以浙江嵊泗枸杞岛海洋牧场（贻贝养殖区）为例，基于多个航次实测数据，构建了面向Landsat8 OLI影像的Chl a质量浓度反演模型，同时，分析了养殖设施对遥感反射率的影响，并对其进行了有效校正。验证结果表明反演模型具有良好的性能，进而利用校正后的卫星遥感反射率数据，反演获得了贻贝养殖区及毗邻海域的高精度Chl a质量浓度产品，并分析了其时空变化特征及潜在影响因素。本研究为海洋牧场区Chl a质量浓度的高精度遥感监测提供了方法支撑与技术基础。Abstract: Water quality monitoring in marine ranching areas is of vital importance for their high-quality and sustainable development. Chlorophyll a (Chl a), an important indicator of phytoplankton biomass and water eutrophication, plays a key role in environmental assessment and risk management. Satellite remote sensing, characterized by rapid observation and wide spatiotemporal coverage, offers significant advantages. However, dedicated remote sensing products for marine ranching areas remain lacking, and aquaculture facilities can interfere with satellite signals, introducing errors in the remote sensing inversion of Chl a mass concentration. Using the Gouqi Island marine ranching area (a mussel aquaculture ranching area) in Shengsi, Zhejiang Province, as a case study, Chl a mass concentration inversion models for Landsat8 OLI images were constructed based on in situ data acquired during multiple cruises. The influence of aquaculture facilities on remote sensing reflectance was analyzed and effectively corrected. Validation results demonstrated the good performance of the inversion model. Using the corrected reflectance data, high-precision Chl a mass concentration products were retrieved for the mussel aquaculture ranching area and adjacent waters, and their spatiotemporal variations and potential influencing factors were examined. This study provides methodological support and a technical foundation for high-precision remote sensing monitoring of Chl a mass concentration in mussel aquaculture ranching areas.
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图 1 航次调查站点分布
不同颜色标记表示不同时间航次的调查站点（黑色框线内为贻贝养殖区），背景为2021年11月16日的Landsat8 OLI真彩色遥感影像（由波段4、3和2合成）
Fig. 1 Distribution of survey sampling sites during different cruises
Different colors indicate sampling sites during different cruises (the area within the black box represents the mussel aquaculture ranching area), overlaid on a Landsat8 OLI true-color image on 16 November 2021 (composite of bands 4, 3, and 2)
图 2 养殖区内部和外部毗邻海域水体遥感反射率Rrs(λ)的直方分布图
Fig. 2 The histograms of remote sensing reflectance Rrs(λ) for areas within and adjacent to the mussel aquaculture ranching area
图 3 实测Chl a质量浓度分布
a. 贻贝养殖区内部Chl a质量浓度分布；b. 贻贝养殖区外部Chl a质量浓度分布
Fig. 3 Distribution of in situ measured Chl a mass concentration
a. Chl a mass concentration distribution inside the mussel aquaculture ranching area; b. Chl a mass concentration distribution outside the mussel aquaculture ranching area
图 4 现场实测的遥感反射率光谱曲线
a. 水体光谱曲线；b. 浮标光谱曲线。图b 中黑色实线表示平均光谱值，青色阴影区域表示光谱的1倍标准差变化范围
Fig. 4 Field-measured remote sensing reflectance spectra
a. Water spectral curves; b. buoy spectral curves. In panel b, the black solid line represents the mean spectral values, while the cyan shaded area indicates the range of one standard deviation
图 5 Chl a质量浓度反演模型拟合与交叉验证散点图
a和c分别为春夏季和秋冬季的模型拟合图；b和d分别为春夏季和秋冬季模型估算值与实测值的散点图
Fig. 5 Fitting and cross-validation of the Chl a mass concentration retrieval model
a and c represent the model fitting for spring-summer and autumn-winter, respectively; b and d present scatter plots comparing model estimates with observations for the corresponding seasons
图 6 OC3算法反演的Chl a质量浓度与实测数据的对比散点图
a. 春夏季节；b. 秋冬季节
Fig. 6 Scatter plot between Chl a mass concentrations retrieved by the OC3 algorithm and in situ mass measurements
a. Spring and summer; b. autumn and winter
图 7 卫星Rrs(443)/Rrs(655)与实测比值、Chl a质量浓度反演值与实测值之间的散点图及Chl a质量浓度分布图
a和c分别为校正前和校正后卫星观测遥感反射率比值与实测比值的散点图；b和f为基于校正前遥感反射率反演的Chl a质量浓度；d和g为基于校正后遥感反射率反演的Chl a质量浓度；e为2021年11月16日Landsat8 OLI的真彩色遥感影像（由波段4、3和2合成）
Fig. 7 Scatter plots of satellite-derived Rrs(443)/Rrs(655) and Chl a mass concentrations versus in situ values, and spatial distributions of Chl a mass concentrations
a and c show scatter plots of satellite-derived Rrs443/Rrs655 versus in situ values before and after correction, respectively; b and f present satellite Chl a mass concentrations retrieved from uncorrected remote sensing reflectance; d and g indicate satellite Chl a mass concentrations derived from corrected remote sensing reflectance; e shows a true-color Landsat 8 OLI image on 16 November 2021 (composite of bands 4, 3, and 2)
图 8 不同浮标光谱对Chl a质量浓度反演的敏感性分析
Fig. 8 Sensitivity analysis of Chl a mass concentration inversion with respect to different buoy spectra
图 9 贻贝养殖区及毗邻海域Landsat 8 OLI真彩色遥感影像（由波段4、3和2合成）及Chl a质量浓度分布
Fig. 9 Landsat 8 OLI true-color image (composed of bands 4, 3, and 2) and the Chl a mass concentration distribution in mussel aquaculture ranching area and adjacent seas
图 10 贻贝养殖区内部和外部毗邻海域的平均Chl a质量浓度
Fig. 10 The average Chl a mass concentration in the marine areas within and adjacent to the mussel aquaculture ranching area
表 1 春夏季节Chl a质量浓度最优反演模型及精度
Tab. 1 The optimal estimation models and accuracy of Chl a mass concentration during the spring and summer seasons
X 光谱形式 模型 R2 MAPE/% RMSE/(mg·m−3) Bias/(mg·m−3) X1 Rrs (443) y = −223.57x + 10.97 0.07 46.4 5.06 0.002 X2 Rrs (655) − Rrs (443) y = 16.49e184.83x 0.42 44.1 4.32 0.15 X3 ${\dfrac{{{R_{{\text{rs}}}}\left( {561} \right)}}{{{R_{{\text{rs}}}}\left( {483} \right)}}}$ y = 0.19e3.30x 0.87 25.4 2.29 0.08 X4 ${{\log _{10}}\left[ {\dfrac{{{R_{{\text{rs}}}}\left( {561} \right)}}{{{R_{{\text{rs}}}}\left( {483} \right)}}} \right]}$ y = 4.90e9.42x 0.88 26.6 2.41 0.14 X5 ${\dfrac{{{R_{{\text{rs}}}}\left( {655} \right) - {R_{{\text{rs}}}}\left( {443} \right)}}{{{R_{{\text{rs}}}}\left( {655} \right)/{R_{{\text{rs}}}}\left( {443} \right)}}}$ y = 16.24e90.02x 0.54 40.2 4.05 0.21 X6 ${\dfrac{{{R_{{\text{rs}}}}\left( {561} \right) - {R_{{\text{rs}}}}\left( {483} \right)}}{{{R_{{\text{rs}}}}\left( {561} \right) + {R_{{\text{rs}}}}\left( {483} \right)}}}$ y = 4.87e8.29x 0.88 26.7 2.42 0.15 
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表 2 秋冬季节Chl a质量浓度最优反演模型及精度
Tab. 2 The optimal estimation models and accuracy of Chl a mass concentration during the autumn and winter seasons
X 光谱形式 模型 R2 MAPE/% RMSE/(mg·m−3) Bias/(mg·m−3) X1 Rrs (655) y = 8.14e−61.50x 0.58 36.7 1.73 −0.02 X2 Rrs (865) − Rrs (655) y = 8.18e78.31x 0.61 34.4 1.67 −0.02 X3 ${ \dfrac{{{R_{{\text{rs}}}}\left( {443} \right)}}{{{R_{{\text{rs}}}}\left( {655} \right)}}} $ y = 2.49x1.53 0.65 31.3 1.59 −0.01 X4 ${{\log _{10}}\left[ {\dfrac{{{R_{{\text{rs}}}}\left( {655} \right)}}{{{R_{{\text{rs}}}}\left( {443} \right)}}} \right]}$ y = −12.81x + 2.91 0.65 35.1 1.62 0.0004 X5 ${\dfrac{{{R_{{\text{rs}}}}\left( {561} \right) - {R_{{\text{rs}}}}\left( {483} \right)}}{{{R_{{\text{rs}}}}\left( {561} \right)/{R_{{\text{rs}}}}\left( {483} \right)}}}$ y = 8.09e−234.70x 0.57 40.4 1.72 −0.03 X6 ${\dfrac{{{R_{{\text{rs}}}}\left( {655} \right) - {R_{{\text{rs}}}}\left( {443} \right)}}{{{R_{{\text{rs}}}}\left( {655} \right) + {R_{{\text{rs}}}}\left( {443} \right)}}}$ y = 17.85x2 − 8.43x + 2.35 0.67 29.7 1.58 0.0003
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