Remote Sensing Technology and Application, Volume. 40, Issue 4, 909(2025)
Reviews of Remote Sensing Monitoring of Urban Black and Odorous Water
[3] [3] HUC M, HACKETTK E, CALLAHANM K, et al. The 2002 ocean color anomaly in the Florida bight: A cause of local coral reef decline [J]. Geophysical Research Letters, 2003, 30(3):511-514. DOI: 10.1029/2002GL016479
[4] [4] KUTSERT, PAAVELB, VERPOORTERC, et al. Remote sensing of black lakes and using 810 nm reflectance peak for retrieving water quality parameters of optically complex waters[J]. Remote Sensing, 2016, 8(6): 497. DOI: 10.3390/rs8060497
[5] [5] KUTSERT, TRANVIKL, PIERSOND C. Variations in colored dissolved organic matter between boreal lakes studied by satellite remote sensing[J]. Journal of Applied Remote Sensing, 2009, 3(1): 33538. DOI: 10.1117/1.3184437
[6] [6] ZHAOJ, HUC M, LAPOINTEB, et al. Satellite-observed black water events off southwest Florida: Implications for coral reef health in the Florida keys national marine sanctuary[J]. Remote Sensing, 2013, 5(1): 415-431. DOI: 10.3390/rs5010415
[7] [7] FENGQiang, YIJing, LIUShumin, et al. The pollution situation, treatment techniques and countermeasures of urban black and odors water in China[J]. Environmental Engineering, 2020, 38(8): 82-88.
[8] [8] WANGXu, WANGYonggang, SUNChanghong, et al. Formation mechanism and assessment method for urban black and odorous water body: A review[J]. Chinese Journal of Applied Ecology, 2016, 27(4): 1331-1340.
[9] [9] DINGXiaolei, LIYunmei, HengLÜ , et al. Analysis of absorption characteristics of urban black-odor water[J]. Environmental Science, 2018, 39(10): 4519-4529.
[10] [10] ChunjianLÜ, GAOHongjie, LIXiaojie, et al. DOM components and optical properties of black-odorous rivers in Shenyang city, China[J]. Chinese Journal of Environmental Engineering, 2019, 13(3): 559-568.
[11] [11] ZHUWenfei, LIXiaojie, LIUChangfeng, et al. Analysis of DOM ultraviolet spectrum characteristics of surface water in black and odorous water body of Shenyang city, China[J]. Chinese Journal of Environmental Engineering, 2019, 13(3): 569-576.
[12] [12] LIJiaqi, LIJiaguo, ZHULi, et al. Remote sensing identification and validation of urban black and odorous water in Taiyuan city[J]. Journal of Remote Sensing, 2019, 23(4): 773-784.
[13] [13] WENShuang, WANGQiao, LIYunmei, et al. Remote sensing identification of urban black-odor water bodies based on high-resolution images: A case study in Nanjing[J]. Environmental Science, 2018, 39(1): 57-67.
[14] [14] YAOHuanmei, LUYannan, GONGZhuqing. Remote sensing identification of urban black and odorous water body based on PlanetScope images: A case study in Qinzhou, Guangxi[J]. Environmental Engineering, 2019, 37(10): 35-43.
[15] [15] YAOYue, SHENQian, ZHULi, et al. Remote sensing identification of urban black-odor water bodies in Shenyang city based on GF-2 image[J]. Journal of Remote Sensing, 2019, 23(2): 230-242.
[16] [16] ZHANGChun, GEYi, RENYue, et al. Semantic segmentation of rural black and odorous water body based on improved Deeplabv3+ network with remote sensing images[J]. Remote Sensing Technology and Application, 2023, 38(6): 1433-1444.
[17] [17] LIUBing, LITianhong. Research on remote sensing identification methods of urban black and odorous water bodies with Gaofen images[J]. Journal of Basic Science and Engineering, 2024, 32(2): 314-330.
[18] [18] LILingling, LIYunmei, HengLÜ , et al. Remote sensing classification of urban black-odor water based on decision tree[J]. Environmental Science, 2020, 41(11): 5060-5072.
[19] [19] QIKeke, SHENQian, LUOXiaojun, et al. Remote sensing classification and recognition of black and odorous water in Shenyang based on GF-2 image[J]. Remote Sensing Technology and Application, 2020, 35(2): 424-434.
[20] [20] FULi, LIUGe, SONGKaishan, et al. Research on optical characterization and remote sensing identification of typical black and odorous water in rural areas[J]. Remote Sensing Technology and Application, 2024, 39(5): 1064-1074.
[21] [21] ZHANGBaodong, WANGBiao, WUYanlan, et al. Analysis and identification of characteristics of rural black and odorous water bodies in Anhui Province[J]. Ecology and Environmental Sciences, 2024, 33(8): 1257-1268.
[22] [22] WANGQiao, ZHULi. Urban black and odorous water monitoring technique and application using remote sensing[M]. Beijing: China Environmental Publishing Group, 2018.
[23] [23] ZHANGBing, LIJunsheng, SHENQian, et al. Recent research progress on long time series and large scale optical remote sensing of inland water[J]. National Remote Sensing Bulletin, 2021, 25(1): 37-52.
[24] [24] SUNYongbin, LUHuixiong, LIQiliang, et al. A high-resolution remote sensing identification method for small, black and odorous water bodies coordinated by “algorithm-flag”[J]. Journal of Geo-Information Science, 2024, 26(12): 2788-2804.
[25] [25] CAOJianing.Research on remote sensing interpretation algorithm of black and odorous water based on fuzzy decision tree [D]. Langfang: North China Institute of Aerospace Engineering, 2021.
[26] [26] LIXiaojie. Pollutant characteristics and remote sensing identification of typical black and odorous water bodies in Shenyang[D]. Xi’an: Chang’an University, 2018.
[27] [27] CAOHongye. Study on analysis of optical properties and remote sensing identifiable models of black and malodorous water in typical cities in China[D]. Chengdu: Southwest JiaoTong University, 2017.
[28] [28] GORDONH R, WANGM. Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: A preliminary algorithm[J]. Applied Optics, 1994, 33(3): 443-452. DOI: 10.1364/AO.33.000443
[29] [29] MOBLEYC. Light and Water: Radiative transfer in natural waters [M]. New York: Academic Press, 1994.
[30] [30] ZOULei. Measurement and application of inherent and polarized optical properties in inland water [D].Beijing: University of Chinese Academy of Sciences, 2013.
[31] [31] FULi, GUOWenwen, SONGKaishan, et al. Remote sensing identification of black and odorous water bodies in Changchun City in 2020[J]. Wetland Science, 2022, 20(4): 537-547.
[32] [32] HANWencong, ZHANGXiaoyu, CHENJiaxing, et al. Remote sensing monitoring of urban black and odorous water based on GF-2 image[J].Environmental Ecology,2021,3(1):63-71.
[33] [33] WANGXiaochen, YANGXingzhong, BinbinLÜ , et al. Community structure of the phytoplankton and its relationship with environmental factors in lower reaches of the Yellow River[J]. Journal of Anhui Agricultural Sciences, 2012, 40(18): 9819-9821.
[34] [34] GUJiayan, HEGuofu, ZHANLinghua, et al. Spectrum characteristics analysis of black and odorous waters in Shanghai and study of remote sensing recognition[J]. Research of Environmental Sciences, 2022, 35(1): 70-79.
[35] [35] ZHANLinghua. Study on recognition models of urban black and odorous water bodies based on optical characteristics[D]. Shanghai: East China Normal University, 2019.
[36] [36] SHENQ, YAOY, LIJ S, et al. A CIE color purity algorithm to detect black and odorous water in urban rivers using high-resolution multispectral remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9): 6577-6590. DOI: 10.1109/TGRS.2019.2907283
[37] [37] WEIC F, ZHENGQ Y, SHANGY H, et al. Black and odorous water monitoring by using GF series remote sensing data[C]∥Proceedings of the 9th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE, 2021: 1-6. DOI: 10.1109/agro-geoinformatics50104.2021.9530312
[38] [38] LIJiaqi, DAIHuayang, LIJiaguo, et al. Remote sensing identification of heavily polluted water in urban areas[J]. Bulletin of Surveying and Mapping, 2018(5): 54-58.
[39] [39] WANGRu, SHENQian, PENGHongchun, et al. Study on the applicability of multi-source high-resolution satellite images for monitoring black and odorous water body[J]. National Remote Sensing Bulletin, 2022, 26(1): 179-192.
[40] [40] TURSUNMarhaba, LIUZhenyu, ZHAOWenjing, et al. Recognition of urban black and odorous water by UAV hyperspectral remote sensing[J]. Journal of South-Central Minzu University (Natural Science Edition), 2022, 41(6): 668-675.
[41] [41] WEIL F, HUANGC, WANGZ X, et al. Monitoring of urban black-odor water based on nemerow index and gradient boosting decision tree regression using UAV-borne hyperspectral imagery[J]. Remote Sensing, 2019, 11(20): 2402. DOI: 10.3390/rs11202402
[42] [42] CHENGXin, XUJie, LIYunmei, et al. Discrimination method of unmanned aerial vehicle hyperspectral for the types of pollution sources of black-odor rivers in cities[J]. National Remote Sensing Bulletin, 2024, 28(8): 1914-1926.
[43] [43] HUGuoqing, CHENDonghua, LIUCongfang, et al. Dynamic monitoring of urban black-odor water bodies based on GF-2 image[J]. Remote Sensing for Land & Resources, 2021, 33(1): 30-37.
[44] [44] JIANGYuwen, ZHOUNing, ZHOUYanyan, et al. Research on remote sensing monitoring of urban black and odorous water[J]. Bulletin of Surveying and Mapping, 2019(S1): 98-104.
[45] [45] JIGang. Research and application on black and odorous water body by remote sensing[D]. Lanzhou: Lanzhou JiaoTong University, 2017.
[46] [46] ZHANGXue, LAIJibao, LIJiaguo, et al. Remote sensing recognition of black-odor waterbodies in Shenzhen City based on GF-1 satellite[J]. Science Technology and Engineering, 2019, 19(4): 268-274.
[47] [47] GAOLi, ZHANGLulu, YEMai, et al. Remote sensing recognition of black-odor waterbodies in Guangzhou City based on GF-6 image[J]. Environmental Ecology, 2021, 3(5): 13-18.
[48] [48] HUANGQiyu, XIAOHan, YUZhifeng, et al. Remote sensing identification of black-odorous water in Hangzhou based on GF-6 images[J]. Journal of Hangzhou Normal University (Natural Science Edition), 2022, 21(5): 542-552.
[49] [49] QIKeke. Remote sensing classification and recognition of urban black and odorous water based on multi-source high-resolution images[D]. Chengdu: Southwest JiaoTong University, 2019.
[50] [50] ZHAOQ C, DONGX X, LIG H, et al. Classification and regression tree models for remote recognition of black and odorous water bodies based on sensor networks[J]. Scientific Programming, 2022, 2022(1): 7390098. DOI: 10.1155/2022/7390098
[51] [51] DONGXuxin, ZHAOQichao, LIJiaguo, et al. Construction and application of CART model for remote sensing recognition of black and odorous water[J]. Remote Sensing Information, 2022, 37(5): 63-69.
[52] [52] CHENShuai. Remote sensing recognition of black and odorous water bodies based on Landsat-8 images[D]. Changsha: Changsha University of Science & Technology, 2021.
[53] [53] YUZ F, HUANGQ Y, PENGX X, et al. Comparative study on recognition models of black-odorous water in Hangzhou based on GF-2 satellite data[J]. Sensors, 2022, 22(12): 4593. DOI: 10.3390/s22124593
[54] [54] ZHANGLing. Urban black and odorous water recognition principle and accuracy evaluation method based on remote sensing satellite image[J]. China-Arab States Science and Technology Forum, 2020(7): 151-153.
[55] [55] XUJiafeng, LIYunmei, XUJie, et al. Adaptive threshold for surface shadow detection of black and odor water[J]. Journal of Geo-Information Science, 2020, 22(10): 1959-1970.
[56] [56] WUT X, LIM Y, WANGS D, et al. Urban black-odor water remote sensing mapping based on shadow removal: A case study in Nanjing[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 9584-9596.
[57] [57] ZHANGQ, WANGB W, WANGS F, et al. Urban black-odor water body dynamic analysis with high-resolution remote sensing image[J]. IOP Conference Series: Earth and Environmental Science, 2019, 344(1): 012149. DOI: 10.1088/1755-1315/344/1/012149
[58] [58] LIWenqiang, HANLiusheng. Remote sensing identification of black and odor water body in Guangzhou based on Sentinel-2A[J]. Science and Technology & Innovation, 2023(3): 44-48.
[59] [59] ZHANGNingning. Dynamic monitoring and evaluation of black and odorous water body in Harbin based on GF-2[D]. Harbin: Harbin Normal University, 2022.
[60] [60] GUOWenwen. Research on the optical characteristics and remote sensing recognition of black and odorous water bodies in Changchun city[D]. Liaocheng: Liaocheng University, 2021.
[61] [61] LILingling. Study on remote sensing classification method of urban black and odorous water based on water color classification[D]. Nanjing: Nanjing Normal University, 2021.
[62] [62] ZHOUX Y, HUANGZ Q, WANY C, et al. A new method for continuous monitoring of black and odorous water body using evaluation parameters: A case study in Baoding[J]. Remote Sensing, 2022, 14(2): 374. DOI: 10.3390/rs14020374
[63] [63] JINHaixia, PANJian. Urban black-odor water body remote sensing monitoring based on GF-2 satellite data fusion[J]. Scientific and Technological Management of Land and Resources, 2017, 34(4): 107-117.
[64] [64] GUJiayan, HEGuofu, ZHANLinghua, et al. Construction of retrieval model for characteristic water quality indicators of black and odorous water in Shanghai based on hyperspectral remote sensing[J]. Environmental Pollution & Control, 2022, 44(8): 1030-1034.
[65] [65] ZHAOQian. Evaluation model construction and pollution source traceability of black and odorous water body in Guangzhou[D]. Zibo: Shandong University of Technology, 2021.
[66] [66] ZHULi, LIYunmei, ZHAOShaohua, et al. Remote sensing monitoring of Taihu Lake water quality by using GF- 1 satellite WFV data[J]. Remote Sensing for Land & Resources, 2015,27(1):113-120.
[67] [67] CAOYun, HANGXin, GAOYi, et al. Remote sensing monitoring of urban black and odorous water bodies using GF-2 images: Taking the main urban area of Nanjing as an example[J]. Sichuan Environment, 2023, 42(1): 208-217.
[68] [68] WANGChen. Research on remote sensing image recognition of black and odorous water based on fuzzy decision tree algorithm [D]. Langfang: North China Institute of Aerospace Engineering, 2022.
[69] [69] DONGXuxin. Research on remote sensing image recognition of black and smelly water based on fuzzy decision tree algorithm[D]. Tianjin: Hebei University of Technology, 2022.
[70] [70] YASRABR, ZHANGJ C, SMYTHP, et al. Predicting plant growth from time-series data using deep learning[J]. Remote Sensing, 2021, 13(3): 331. DOI: 10.3390/rs13030331
[71] [71] HUGuoqing. Research on remote sensing monitoring of black-odor water bodies based on deep learning[D]. Wuhu: Anhui Normal University, 2021.
[72] [72] SHAOHuxiang, DINGFeng, YANGJian, et al. Model of extracting remotely-sensed information of black and odorous water based on deep learning[J]. Journal of Yangtze River Scientific Research Institute, 2022, 39(4): 156-162.
[73] [73] HUANGJ J,XUJ D,CHONGQ P,et al. Black and odorous water detection of remote sensing images based on improved deep learning[J]. Canadian Journal of Remote Sensing,2023,49(1):1-14. DOI:10.1080/07038992.2023.2237591
[74] [74] ZHENGG Z, ZHAOY Y, PANZ X, et al. Fanet: A deep learning framework for black and odorous water extraction[J]. European Journal of Remote Sensing, 2023, 56(1):22340771-16. DOI: 10.1080/22797254.2023.2234077
[75] [75] HEHongshu. Urban black and odorous water body recognition based on improved U-Net network remote sensing semantic segmentation[D]. Beijing: Aerospace Information Research Institute, Chinese Academy of Sciences, 2020.
Get Citation
Copy Citation Text
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
Category:
Received: Nov. 16, 2024
Accepted: --
Published Online: Aug. 26, 2025
The Author Email: Zhenghua CHEN (chen.zhenghua@163.com)