Remote estimation of total suspended matter concentration in the Hangzhou Bay based on OLCI and its water color product applicability analysis
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摘要: 悬浮物含量及其时空分布是河口海岸环境中关心的热点问题。2016年2月16日，欧洲航天局发射了新一代海洋水色传感器(OLCI)，该传感器具有良好的时空及光谱分辨率。本研究结合2017年7月杭州湾同步采样数据，对比了6种大气校正算法和8种悬浮物浓度(TSM)估算模型，遴选和分析了适宜于杭州湾和OLCI数据的大气校正方法和TSM估算模型，验证了OLCI数据二级产品精度和适用性。结果表明：(1)基于紫外光谱的大气校正算法(UVAC)精度最高，同步4个采样点的大气校正平均相对误差(MAPE)分别为34.21%、13.11%、5.92%和20.28%。在除Oa1以外的14个波段的MAPE均值为15.23%，Oa4至Oa10波段的MAPE低于8%；(2)基于Oa16/Oa5的波段比值模型，具有良好的建模(MAPE为16.49%，RMSE为50.92 mg/L)和验证(MAPE为19.08%，RMSE为19.29 mg/L)精度及模型稳健性；(3)基于C2RCC算法的固有光学量和TSM含量产品及OLCI二级TSM含量产品在杭州湾精度较差，不适用于杭州湾TSM和固有光学量遥感监测应用；(4)空间上，TSM在杭州湾中部区域含量较低，在杭州湾南岸和湾口区域含量较高。Abstract: As a main carrier of nutrients and pollutants, total suspended matter (TSM) has a significant influence on water environment, especially on estuary water environment. The Ocean and Land Colour Instrument (OLCI) was onboard ESA Sentinel-3A satellite and launched in February 16, 2016, with fine spatial, temporal and spectral resolution. To find the best atmospheric correction method and TSM retrieval model for the application of OLCI in Hangzhou Bay (HZB), six atmospheric correction methods and eight TSM retrieval models were test based on in situ water color data collected from HZB on July 2017. In addition, the OLCI Level 2 product (e.g. TSM and inherent optical properties (IOP) data) was compared with in situ data to evaluate the accuracy and applicability of OLCI Level 2 product. The results show that the method of atmospheric correction based on ultraviolet wavelength (UVAC) and the TSM retrieval model based on band ratio have best performance. Specifically, the mean absolute percentage error (MAPE) of atmospheric correction in four match-up sites is 34.21%, 13.11%, 5.92% and 20.28%, respectively. In addition, the averaged MAPE of atmospheric correction in band Oa2 to Oa12 and Oa16 to Oa18 is 15.23%, and in band Oa4 to Oa10 is less than 8%. The band ratio (Oa16/Oa5) model has the best performance, with a MAPE of 16.49% and root mean square error (RMSE) of 50.92 mg/L in calibration stage, and a MAPE of 19.08% and RMSE of 19.29 mg/L in validation stage. However, the TSM and IOP product derived from C2RCC (case 2 regional coast colour) algorithm and the TSM product derived from OLCI Level 2 product has no linear relationship with in situ data. These results indicate that the above Level 2 product is unsuitable for HZB TSM and IOP remote estimation. Finally, the UVAC method and band ratio model are applied to OLCI imagery that is collected on July 23, 2017. Spatially, TSM shows a relative low value in the center of HZB and relative high value in the south and east part of HZB.
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Key words:
- Hangzhou Bay /
- OLCI data /
- total suspended matter /
- applicability analysis /
- atmospheric correction
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图 1 研究区及采样点分布示意
Fig. 1 The location of study area and the spatial distribution of sampling stations
图 2 杭州湾悬浮物浓度特征（a）及颗粒物吸收特性分析（b）
Fig. 2 Characteristics of TSM (a) and absorption of suspended matter (b)
图 3 遥感反射率与悬浮物浓度关系示意
Fig. 3 The relationship between remote sensing reflectance and TSM
图 4 不同方法大气校正精度评价
Fig. 4 Evaluation of the accuracy of different atmospheric correction method
图 5 基于C2RCC算法所得aph(443) (a)和ad(443) (b)的估算结果与实测值对比
Fig. 5 The C2RCC-derived aph(443) (a) and ad(443) (b) are compared with in situ value, respectively
图 6 基于C2RCC算法和OLCI Level 2产品的TSM估算结果与实测值对比
Fig. 6 The C2RCC-derived TSM and OLCI Level 2 TSM are compared with measured TSM
图 7 基于UVAC和C2RCC算法的TSM空间分布
Fig. 7 Spatial distribution of TSM based on UVAC and C2RCC algorithm
表 1 不同大气校正方法逐波段MAPE误差统计
Tab. 1 Error statistics in each band of different atmospheric correction method
波段（中心波长） 6S Flaash C2RCC MUMM BPAC UVAC Oa1(400 nm) 174.43% 118.94% 61.67% 70.46% 56.30% 62.39% Oa2(413 nm) 96.66% 53.00% 59.45% 72.68% 54.33% 32.88% Oa3(443 nm) 77.79% 64.59% 58.32% 55.37% 44.26% 20.95% Oa4(490 nm) 46.07% 31.41% 53.83% 43.93% 34.07% 7.67% Oa5(510 nm) 37.06% 23.53% 52.09% 41.24% 30.31% 4.81% Oa6(560 nm) 18.02% 10.56% 48.45% 28.25% 21.97% 4.93% Oa7(620 nm) 16.22% 13.77% 38.58% 21.45% 18.55% 6.09% Oa8(665 nm) 23.08% 18.11% 50.24% 18.45% 17.46% 6.85% Oa9(674 nm) 23.08% 18.53% 55.52% 17.47% 17.40% 6.69% Oa10(681 nm) 23.83% 19.14% 53.93% 28.23% 17.05% 7.06% Oa11(709 nm) 28.64% 26.72% 27.51% 29.95% 18.65% 16.89% Oa12(754 nm) 78.99% 70.45% 45.68% 30.47% 37.70% 14.66% Oa16(779 nm) 70.60% 62.73% 42.46% 29.17% 39.26% 16.24% Oa17(865 nm) 116.25% 114.58% 64.91% 39.70% 57.22% 31.13% Oa18(885 nm) 128.84% 137.43% 66.74% 47.37% 62.38% 36.43% 
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表 2 SERT模型中的参数α和β
Tab. 2 The value of parameters α and β in SERT model
波长/nm α β 560 0.042 2 35.334 8 620 0.064 3 0.147 6 709 0.074 9 0.036 7 779 0.086 7 0.007 6
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表 3 基于实测数据构建的各类最优反演模型及精度评价
Tab. 3 The various developed TSM retrieval algorithms and its accuracy assessment
模型 公式 自变量 R2 MAPE/% RMSE/mg·L−1 单波段 TSM = 10(27.761x + 1.421 2) Oa16 0.81 18.16 72.73 波段比值 TSM = 10(1.057 5x + 1.327 5) Oa16/Oa5 0.90 16.49 50.92 三波段 TSM = 10[2.049 2 + 9.686 4(x2 + x3)−0.149 8(x1/x2)] Oa1、Oa17、Oa18 0.87 18.87 65.88 多元回归 TSM=10(1.860 1 − 36.829 3x1 + 17.577 2x2 + 27.753 4x3) Oa5、Oa7、Oa12 0.91 17.79 42.87 SAI光谱指数 TSM = 10(1.827 7SAI + 1.007 2) Oa4、Oa6、Oa16 0.89 16.29 49.19 SERT — — — 24.56 62.07 3S TSM = −1 911.7x + 26.924 [Oa11−1−Oa12−1]−1 0.92 18.68 52.17 Nechad TSM = 910.02x − 7.624 7 Oa18/(0.093 − Oa18) 0.84 25.54 65.17
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表 4 基于OLCI数据的模型精度评价
Tab. 4 The accuracy assessment of various developed TSM retrieval algorithms based on OLCI data
模型 斜率 截距 R2 MAPE/% RMSE/mg·L−1 单波段 0.58 21.97 0.93 23.43 31.02 波段比值 0.82 11.86 0.91 19.08 19.29 三波段 0.66 −11.78 0.85 53.39 49.94 多元回归 0.72 14.88 0.92 19.74 23.07 SAI光谱指数 0.93 12.07 0.91 22.23 18.68 SERT 0.78 −6.31 0.84 31.10 35.32 3S 0.82 23.45 0.87 30.84 22.41 Nechad 0.79 −3.53 0.83 33.73 33.83
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表 5 模型在不同TSM浓度等级上的精度评价
Tab. 5 The performance of various developed TSM retrieval algorithms on different TSM level
模型 TSM<80 mg/L 80 mg/L≤TSM≤150 mg/L TSM>150 mg/L MAPE/% RMSE/mg·L−1 MAPE/% RMSE/mg·L−1 MAPE/% RMSE/mg·L−1 单波段 16.25 12.38 22.54 45.70 34.29 107.20 波段比值 14.71 10.97 14.60 22.94 21.35 68.46 三波段 18.23 12.60 17.73 31.76 24.52 77.42 多元回归 14.99 10.75 15.72 23.57 19.63 45.76 SAI光谱指数 17.26 13.19 14.49 23.24 20.94 51.45 SERT 25.53 17.22 26.07 59.43 34.67 81.86 3S 26.83 16.47 16.73 31.61 22.61 58.70 Nechad 26.74 16.71 26.79 51.70 31.58 78.43
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表 6 模型敏感性分析
Tab. 6 The sensitivity analysis of three developed TSM retrieval algorithms
模型 MAPE/% RMSE/mg·L−1 范围 均值 范围 均值 波段比值 16.34～16.62 16.50 50.14～51.83 51.02 多元回归 50.99～62.23 55.88 108.66～182.18 141.75 SAI光谱指数 16.20～16.66 16.43 48.74～50.22 49.42
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