基于BRDF校正的上海近海水体叶绿素a浓度反演

doi:10.11988/ckyyb.20230373

院报 ›› 2024, Vol. 41 ›› Issue (9): 169-177.DOI: 10.11988/ckyyb.20230373

基于BRDF校正的上海近海水体叶绿素a浓度反演

韩震¹^,²(), 陈帅康¹, 李丕学³, 陈浩程¹

¹ 上海海洋大学海洋科学学院,上海 201306
² 上海河口海洋测绘工程技术研究中心,上海 201306
³ 上海市海洋环境监测预报中心,上海 200062

收稿日期:2023-04-10 修回日期:2023-07-15 出版日期:2024-09-01 发布日期:2024-09-20
作者简介:
韩震(1969-),男,山东德州人,教授,博士,主要研究方向为海洋信息探测与应用。E-mail:zhhan@shou.edu.cn
基金资助:
上海市海洋局科研专项(沪海科2019-03); 教育部产学合作协同育人项目(202102245031)

Inversion of Chlorophyll-a Concentration in Shanghai Offshore Waters Based on BRDF Correction

HAN Zhen¹^,²(), CHEN Shuai-kang¹, LI Pi-xue³, CHEN Hao-cheng¹

¹ School of Marine Science, Shanghai Ocean University, Shanghai 201306, China
² Shanghai EstuarineMarine Mapping Engineering Technology Research Center, Shanghai 201306, China
³ Shanghai MarineEnvironment Monitoring and Forecasting Center, Shanghai 200062, China

Received:2023-04-10 Revised:2023-07-15 Published:2024-09-01 Online:2024-09-20

摘要/Abstract

摘要：

为了探究上海近海区水体生态状况,采用水色遥感技术,对叶绿素a浓度进行反演,考虑BRDF对反演的影响,基于Landsat-8遥感数据和水质实测数据,结合Lee模型和QAA算法对上海近海水体进行BRDF校正。结果表明:悬浮泥沙浓度是影响水体BRDF的主要原因之一;日光在非垂直入射时,角度对水体BRDF影响较小;BRDF校正后模型R²平均值达到0.9,提高了22.2%,RMSE平均值降低至0.74;BRDF校正前上海近海区叶绿素a浓度平均被高估2 mg/m³。通过BRDF校正提高了叶绿素a反演精度,为近海水体水色反演研究提供新思路。

关键词: 叶绿素a, BRDF校正, 近海水体, Landsat-8

Abstract:

To investigate the ecological status of Shanghai’s offshore waters, we applied the water color remote sensing technology to estimate chlorophyll-a concentration. Considering the impact of BRDF (Bidirectional Reflectance Distribution Function) on inversion accuracy, we utilized LandSat-8 remote sensing data and measured water quality data in association with Lee’s model and QAA (Quasi-analytical algorithm) for BRDF correction. Results indicate that suspended sediment concentration is a major factor affecting BRDF. The angle of sunlight has minimal impact on BRDF when the sun is not vertically incident. After BRDF correction, the mean value of R² increased by 22.2% to 0.9, whereas mean RMSE decreased to 0.74. Before the correction, chlorophyll-a concentrations in Shanghai’s offshore waters were overestimated by an average of 2 mg/m³. BRDF correction notably enhances chlorophyll-a inversion accuracy, presenting innovative insights for offshore water color inversion.

Key words: chlorophyll-a, BRDF correction, offshore waters, Landsat-8

中图分类号:

X87环境遥感

韩震, 陈帅康, 李丕学, 陈浩程. 基于BRDF校正的上海近海水体叶绿素a浓度反演[J]. 院报, 2024, 41(9): 169-177.

HAN Zhen, CHEN Shuai-kang, LI Pi-xue, CHEN Hao-cheng. Inversion of Chlorophyll-a Concentration in Shanghai Offshore Waters Based on BRDF Correction[J]. Journal of Yangtze River Scientific Research Institute, 2024, 41(9): 169-177.

图/表 15

图1 方法技术流程

Fig.1 Technical flow chart of the method

图2 水面光谱反射示意图

Fig.2 Schematic diagram of water surface spectral reflection

表1 不同角度下 G 0 w(Ω)、 G 1 w(Ω)、 G 0 p(Ω)和 G 1 p(Ω)值

Table 1 Values of G 0 w(Ω), G 1 w(Ω), G 0 p(Ω), G 1 p(Ω) at different sunlight angles

θ_s/(°)	θ_v/(°)	$φ$ /(°)	$G 0 w$ (Ω)	$G 1 w$ (Ω)	$G 0 p$ (Ω)	$G 1 p$ (Ω)
0	0	0	0.060 4	0.040 6	0.040 2	0.131 0
0	30	90	0.059 6	0.051 6	0.040 8	0.142 0
15	30	90	0.059 0	0.056 2	0.041 1	0.146 1
30	30	90	0.058 4	0.060 1	0.041 8	0.149 2
0	40	135	0.058 1	0.058 1	0.041 4	0.145 8
15	40	135	0.061 4	0.052 4	0.042 5	0.140 8
30	40	135	0.062 4	0.052 4	0.043 4	0.140 6

表1 不同角度下 G 0 w(Ω)、 G 1 w(Ω)、 G 0 p(Ω)和 G 1 p(Ω)值

Table 1 Values of G 0 w(Ω), G 1 w(Ω), G 0 p(Ω), G 1 p(Ω) at different sunlight angles

θ_s/(°)	θ_v/(°)	$φ$ /(°)	$G 0 w$ (Ω)	$G 1 w$ (Ω)	$G 0 p$ (Ω)	$G 1 p$ (Ω)
0	0	0	0.060 4	0.040 6	0.040 2	0.131 0
0	30	90	0.059 6	0.051 6	0.040 8	0.142 0
15	30	90	0.059 0	0.056 2	0.041 1	0.146 1
30	30	90	0.058 4	0.060 1	0.041 8	0.149 2
0	40	135	0.058 1	0.058 1	0.041 4	0.145 8
15	40	135	0.061 4	0.052 4	0.042 5	0.140 8
30	40	135	0.062 4	0.052 4	0.043 4	0.140 6

表2 QAA_v6算法步骤

Table 2 Steps of QAA_v6 algorithm

步骤	表达式
1	r_rs(λ)=R_rs(λ)/(0.52+1.7R_rs(λ))
2	u(λ)= $- g 0 + g 0 2 + 4 g 1 r r s (λ) 2 g 1$ ,g₀=0.089,g₁=0.124 5
3	a(λ₀)=a(670)=a_w(670)+0.39 $R r s (670) R r s (443) + R r s (490) 1.14$ , a_w(670)=0.439
4	b_bw(λ)=0.000 95(λ/550)^-4.3
5	b_bp(λ₀)=b_bp(670)= $u (λ 0) a (λ 0) 1 - u (λ 0)$ -b_bw(670)
6	η=2 $1 - 1.2 e x p - 0.9 r r s (443) r r s (555)$
7	b_bp(λ)=b_bp(λ₀) $λ 0 λ η$
8	a(λ)= $1 - u (λ) u (λ)$ b_bw(λ)+b_bp(λ)

表2 QAA_v6算法步骤

Table 2 Steps of QAA_v6 algorithm

步骤	表达式
1	r_rs(λ)=R_rs(λ)/(0.52+1.7R_rs(λ))
2	u(λ)= $- g 0 + g 0 2 + 4 g 1 r r s (λ) 2 g 1$ ,g₀=0.089,g₁=0.124 5
3	a(λ₀)=a(670)=a_w(670)+0.39 $R r s (670) R r s (443) + R r s (490) 1.14$ , a_w(670)=0.439
4	b_bw(λ)=0.000 95(λ/550)^-4.3
5	b_bp(λ₀)=b_bp(670)= $u (λ 0) a (λ 0) 1 - u (λ 0)$ -b_bw(670)
6	η=2 $1 - 1.2 e x p - 0.9 r r s (443) r r s (555)$
7	b_bp(λ)=b_bp(λ₀) $λ 0 λ η$
8	a(λ)= $1 - u (λ) u (λ)$ b_bw(λ)+b_bp(λ)

图3 BRDF校正流程注:G(Ω)、G(0)分别表示引入QAA算法及Lee模型处理数据。

Fig.3 Flowchart of BRDF correction

图4 上海近海及采样点分布

Fig.4 Offshore of Shanghai and distribution of sampling points

图5 不同水质浓度下bb/a分布

Fig.5 Distribution of bb/a under different water quality concentrations

图6 不同入射角度下bb/a分布

Fig.6 Distribution of bb/a under different sunlight incident angles

图7 高悬浮泥沙浓度水体BRDF校正对比

Fig.7 Distribution of bb/a for water bodies with high suspended sediment concentration before and after BRDF correction

图8 单波段相关性分析

Fig.8 Single-band correlation analysis

图9 波段组合相关性分析

Fig.9 Correlation analysis of band combinations

图10 实测叶绿素a浓度与反演结果对比

Fig.10 Comparison of chlorophyll-a concentration between measurements and inversion results

表3 叶绿素a反演模型精度评价结果

Table 3 Evaluation results for the accuracy of the chlorophyll-a inversion model

方法	R²	MRE/%	RMSE/(mg·m^-3)
校正前_经验法	0.759 8	36.48	1.16
校正后_经验法	0.926 3	25.40	0.63
校正前_NDCI	0.787 4	43.69	1.06
校正后_NDCI	0.904 8	26.21	0.71
校正前_WCI	0.669 0	47.72	1.32
校正后_WCI	0.868 1	19.84	0.87

图11 BRDF校正前后叶绿素a浓度空间分布

Fig.11 Spatial distribution of chlorophyll-a concentration before and after BRDF correction

图12 BRDF校正前后叶绿素a浓度差值

Fig.12 Difference in chlorophyll-a concentration before and after BRDF correction

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基于BRDF校正的上海近海水体叶绿素a浓度反演

Inversion of Chlorophyll-a Concentration in Shanghai Offshore Waters Based on BRDF Correction

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