Reviews of Remote Sensing Monitoring of Urban Black and Odorous Water

Zhenghua CHEN; Sixiang LAN; Jinshui ZHANG; Wei ZHANG; Huade LI; Lifang ZHAO

doi:10.11873/j.issn.1004-0323.2025.4.0909

Remote Sensing Technology and Application, Volume. 40, Issue 4, 909(2025)

Reviews of Remote Sensing Monitoring of Urban Black and Odorous Water

Zhenghua CHEN^1、*, Sixiang LAN¹, Jinshui ZHANG², Wei ZHANG¹, Huade LI¹, and Lifang ZHAO³

Author Affiliations

¹School of Marine Sciences， Guangxi University， Nanning530004， China

²State Key Laboratory of Remote Sensing Science， Beijing Normal University， Beijing100875， China

³Aerospace Information Research Institute， Chinese Academy of Sciences， Beijing100094， China

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Fig. 1. Publication of relevant research articles

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Fig. 2. Main keyword co-occurrence map

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Fig. 3. Color characteristics of GF-2 images of black and odorous water in 2016 and normal water in 2020

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Fig. 4. Remote sensing reflectance of black and odorous water and normal water^［35］

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Fig. 5. Equivalent satellite remote sensing reflectance of black and odorous water and normal water^［35］

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Fig. 6. The corresponding relationship between black odor degree and water color^{［18，61］}

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Table 1. Table 1Black and odorous water pollution degree classification standard
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Table 1. Table 1Black and odorous water pollution degree classification standard
特征指标（单位）轻度黑臭重度黑臭
透明度/cm 25~10* <10*
溶解氧/（mg/L） 0.2~2.0 <0.2
氧化还原电位/mV -200~50 <-200
氨氮/（mg/L） 8.0~15 >15

Table 2. Table 2Multi-source satellite image parameter comparison table

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Table 2. Table 2Multi-source satellite image parameter comparison table

序号	卫星类型	分辨率/m		幅宽 /km	重访周期/天	识别像元尺度/个	光谱范围/μm					参考文献
序号	卫星类型	全色	多光谱	幅宽 /km	重访周期/天	识别像元尺度/个	蓝波段	绿波段	红波段	近红外	全色	参考文献
1	GF-2 PMS	1	4	45	5	≥5	0.45~0.52	0.52~0.59	0.63~0.69	0.77~0.89	0.45~0.9	[13,15,19,32,43-44]
2	GF-1 PMS	2	8	60	4	≥5	0.45~0.52	0.52~0.59	0.63~0.69	0.77~0.89	0.45~0.9	[12,45-46]
3	GF-6 PMS	2	8	≥90	4	≥5	0.45~0.52	0.52~0.6	0.63~0.69	0.76~0.89	0.45~0.9	[37,47-48]
4	BJ-2 DMC3	0.8	3.2	24	1	≥5	0.44~0.51	0.51~0.59	0.6~0.67	0.79~0.91	0.45~0.65	[39,49]
5	ZY-3	2.1	5.8	50	5	≥5	0.45~0.52	0.52~0.59	0.63~0.69	0.77~0.89	0.45~0.8	[49]
6	SuperView-1	0.2	2	12	2	≥5	0.45~0.52	0.52~0.59	0.63~0.69	0.77~0.89	0.45~0.89	[39,49]
7	GeoEye-1	0.41	1.65	15.2	≤3	≥5	0.45~0.51	0.51~0.58	0.655~0.69	0.78~0.92	0.45~0.8	[39]
8	WorldView-2	0.46	1.8	16.4	1.1	≥5	0.45~0.51	0.51~0.58	0.63~0.69	0.77~0.895	0.45~1.04	[39]
9	Planet Scope	/	3	24×16	1~2	≥5	0.455~0.515	0.5~0.59	0.59~0.67	0.78~0.86	/	[14,18]
10	Sentinel-2	/	10	290	5	≥5	0.458~0.523	0.543~0.578	0.65~0.68	0.785~0.90	/	[50-51]
11	Landsat-8	15	30	185	16	≥5	0.45~0.515	0.525~0.6	0.63~0.68	0.845~0.885	0.5~0.68	[52]

Table 3. Table 3Discrimination model for spectral indices of black and odorous water

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Table 3. Table 3Discrimination model for spectral indices of black and odorous water

序号	指数	判别模型公式	阈值	正确率	实验影像	实验地点	参考文献
1	Green	$N_{1} \leq R_{r s} (G r e e n) \leq N_{2}$	N₁=0; N₂=0.019	50%	GF-2	南京	[13]
2	NIR	$N_{1} \leq R_{r s} (N I R)$	N₁=0.12	35.71%	Planet Scope	钦州	[14]
3	DBWI	$N_{1} \leq R_{r s} (G r e e n) - R_{r s} (B l u e) \leq N_{2}$	N₁=0; N₂=0.003 6	75%	GF-2	南京	[13]
4	HCI1	$N_{1} \leq R_{r s} (R e d) + R_{r s} (N I R) - R_{r s} (B l u e)$	N₁=0.85	95.37%	GF-2/GF-1	北京	[45]
5	HCI2	$N_{1} \leq R_{r s} (R e d) + R_{r s} (N I R) - R_{r s} (G r e e n)$	N₁=0.87	93.80%	GF-2/GF-1	北京	[45]
6	BOW	$(R_{r s} (G r e e n) - R_{r s} (B l u e)) \times (R_{r s} (G r e e n) - R_{r s} (N I R)) \leq N_{1}$	N₁=0.1	85.7%	GF-2	南京	[56]
7	NDBWI	$N_{1} \leq \frac{R_{r s} (G r e e n) - R_{r s} (R e d)}{R_{r s} (G r e e n) + R_{r s} (R e d)} \leq N_{2}$	N₁=0.06;N₂=0.115	100%	GF-2	南京	[13]
8	BOI	$N_{1} \leq \frac{R_{r s} (G r e e n) - R_{r s} (R e d)}{R_{r s} (B l u e) + R_{r s} (G r e e n) + R_{r s} (R e d)} \leq N_{2}$	N₁=0; N₂=0.065	100%	GF-2	沈阳	[15]
9	UBOW	$N_{1} \leq \frac{R_{r s} (G r e e n) - R_{r s} (R e d)}{R_{r s} (B l u e) + R_{r s} (G r e e n) + R_{r s} (R e d)} \leq N_{2}$	N₁=0.04; N₂=0.07	81%	GF-2	北京	[57]
10	HI	$\frac{R_{r s} (G r e e n) - R_{r s} (B l u e)}{R_{r s} (G r e e n) + R_{r s} (B l u e)} \leq N_{1}$	N₁=0.065	57.14%	Planet Scope	钦州	[14]
11	NDBWI	$\frac{R_{r s} (B l u e) + R_{r s} (G r e e n) + R_{r s} (R e d) - R_{r s} (N I R)}{R_{r s} (B l u e) + R_{r s} (G r e e n) + R_{r s} (R e d) + R_{r s} (N I R)} \leq N_{1}$	N₁=0.45	92.86%	Planet Scope	钦州	[14]
12	BOWI	$\frac{R_{r s} (G r e e n)}{R_{r s} (B l u e) + R_{r s} (R e d)} \leq N_{1}$	N₁=0.65	100%	Sentinel-2	广州	[58]
13	HCI3	$N_{1} \leq \frac{R_{r s} (R e d) + R_{r s} (N I R) - R_{r s} (B l u e)}{R_{r s} (R e d) + R_{r s} (N I R) + R_{r s} (B l u e)}$	N₁=0.4	89.05%	GF-2/GF-1	北京	[45]
14	HCI4	$N_{1} \leq \frac{R_{r s} (R e d) + R_{r s} (N I R) - R_{r s} (G r e e n)}{R_{r s} (R e d) + R_{r s} (N I R) + R_{r s} (G r e e n)}$	N₁=0.45	85.42%	GF-2/GF-1	北京	[45]
15	WCI	$N_{1} \leq \frac{\|\frac{(R_{r s} (G r e e n) - R_{r s} (B l u e))}{(λ_{G} - λ_{B})}\|}{\|\frac{(R_{r s} (R e d) - R_{r s} (G r e e n))}{(λ_{R} - λ_{G})}\|} \leq N_{2}$	N₁=0; N₂=1	92.86%	GF-1	太原	[12]
16	BOCI	$N_{1} \leq \frac{R_{r s} (G r e e n) - {R^{'}}_{r s} (G r e e n)}{R_{r s} (R e d)} \leq N_{2}$ ${R^{'}}_{r s} (G r e e n) = R_{r s} (B l u e) + \frac{(R_{r s} (R e d) - R_{r s} (B l u e)) \times (λ_{G} - λ_{B})}{λ_{R} - λ_{B}}$	N₁=0.12; N₂=0.26	87.18%	GF-2/ZY-3 /BJ-2/SV-1	沈阳	[19]
17	BOIM	$N_{1} \leq \frac{R_{r s} (R e d) - {R^{'}}_{r s} (G r e e n)}{R_{r s} (R e d) + {R^{'}}_{r s} (G r e e n)} \leq N_{2}$ ${R^{'}}_{r s} (G r e e n) = R_{r s} (B l u e) + \frac{(R_{r s} (R e d) - R_{r s} (B l u e)) \times (λ_{G} - λ_{B})}{λ_{R} - λ_{B}}$	N₁=0.014; N₂=0.122	87.88%	GF-2	哈尔滨	[59]

Table 4. Table 4Remote sensing inversion model of typical water quality parameters

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Table 4. Table 4Remote sensing inversion model of typical water quality parameters

水质参数	反演模型公式	参考文献
Chl-a	$4.089 \times {(b_{4} / b_{3})}^{2} - 0.746 \times (b_{4} / b_{3}) + 29.733$	[63,66]
Chl-a	$208.39 \times {(\frac{b_{4} - b_{3}}{b_{4} + b_{3}})}^{2} + 256.99 \times (\frac{b_{4} - b_{3}}{b_{4} + b_{3}}) + 95.477$	[35]
TSS	$119.62 \times {(b_{3} / b_{2})}^{6.0823}$	[63,66]
SD	$C_{S D} = 284.15 \times C_{T S S}^{- 0.67}$	[63,66]
CODcr	$34.038 \times {(\frac{b_{3} - b_{4}}{b_{3} + b_{4}})}^{2} - 64.448 \times (\frac{b_{3} - b_{4}}{b_{3} + b_{4}}) + 43.441$	[35]
COD	$(b_{3} / b_{2} - 0.5777) / 0.007$	[65]
DO	$15.73229 - 30.80257 \times (b_{2} + b_{3})$	[65]
NH₃-N	$(b_{3} / b_{2} - 0.661) / 0.07$	[65]

Table 5. Table 5The advantages and disadvantages of urban black and odorous water identification methods

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Table 5. Table 5The advantages and disadvantages of urban black and odorous water identification methods

方法	适用性	优点	缺点
光谱指数法	适用于简单的水体类型分类	简单易行、计算量小；易于理解和应用；可以快速获取黑臭水体的大致位置和分布；成本较低。	依赖经验阈值，主观因素影响大；对于复杂的水体环境、混杂的光谱信息，识别精度较低。
水体色度法	适用于颜色变化较为显著的情况	颜色直观，可以快速判断色彩异常的水体，初步评估的水质情况。	受光照条件影响，难以处理光照变化较大的情况；只能对颜色明显变化的水体有效；不能准确判断黑臭程度和污染源。
水质参数法	适用于需要定量水体污染程度的情况	能够提供定量的水质信息和较准确的水质参数估计；更精确地全面评估水体污染程度。	部分水质参数的反演模型构建困难；需要实地采样和分析，耗时耗力；对数据要求较高。
决策树	可融合多种特征，适用于实现复杂水体的分类	适应能力强，可以处理多个特征之间的复杂关系；人为因素和噪声影响小，具有一定的自动化和可解释性。	需要事先建立训练数据集，对于复杂情况可能需要更多的训练样本；模型的构建依赖选择特征的质量，否则容易出现过拟合问题。
深度学习	适用于复杂情况下的黑臭水体分割和分类	可以从大量数据中学习复杂的特征表示；具有较高的自动化和预测准确性。	需要大量的标注数据进行训练，对计算资源要求较高。

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Zhenghua CHEN, Sixiang LAN, Jinshui ZHANG, Wei ZHANG, Huade LI, Lifang ZHAO. Reviews of Remote Sensing Monitoring of Urban Black and Odorous Water[J]. Remote Sensing Technology and Application, 2025, 40(4): 909

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Paper Information

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Received: Nov. 16, 2024

Accepted: --

Published Online: Aug. 26, 2025

The Author Email: Zhenghua CHEN (chen.zhenghua@163.com)

DOI:10.11873/j.issn.1004-0323.2025.4.0909

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Table 1Black and odorous water pollution degree classification standard

Table 1. Table 1Black and odorous water pollution degree classification standard

Table 2. Table 2Multi-source satellite image parameter comparison table

Table 2. Table 2Multi-source satellite image parameter comparison table

Table 3. Table 3Discrimination model for spectral indices of black and odorous water

Table 3. Table 3Discrimination model for spectral indices of black and odorous water

Table 4. Table 4Remote sensing inversion model of typical water quality parameters

Table 4. Table 4Remote sensing inversion model of typical water quality parameters

Table 5. Table 5The advantages and disadvantages of urban black and odorous water identification methods

Table 5. Table 5The advantages and disadvantages of urban black and odorous water identification methods