Remote Sensing Technology and Application, Volume. 39, Issue 5, 1064(2024)

Research on Optical Characterization and Remote Sensing Identification of Typical Black and Odorous Water in Rural Areas

Li FU, Ge LIU, Kaishan SONG, and Yongjin CHEN
Author Affiliations
  • Satellite Application Center for Ecology and Environment,MEE, Beijing100094,China
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    References(21)

    [2] WEN Shuang. Remote sensing recognition of urban black and odorous water bodies based on GF-2 images-A case study in Nanjing(2018).

    [3] YAO Huanmei, LU Yannan, GONG Zhuqing. Remote sensing identification of urban black and odorous water body based on PlanetScope images:A case study in Qinzhou,Guangxi. Environmental Engineering, 37, 35-43(2019).

    [4] YAO Yue, SHEN Qian, ZHU Li et al. Remote sensing identification of urban black-odor water bodies in Shenyang city based on GF-2 image. Journal of Remote Sensing, 23, 230-242(2019).

    [5] LI Jiaqi, LI Jiaguo, ZHU Li et al. Remote sensing identification and validation of urban black and odorous water in Taiyuan city. Journal of Remote Sensing, 23, 773-784(2019).

    [6] ZHANG Xue, LAI Jibao, LI Jiaguo et al. Remote sensing recognition of black-odor waterbodies in Shenzhen city based on GF-1 satellite. Science Technology and Engineering, 19, 268-274(2019).

    [7] QI Keke, SHEN Qian, LUO Xiaojun et al. Remote sensing classification and recognition of black and odorous water in Shenyang based on GF-2 image. Remote Sensing Technology and Application, 35, 424-434(2020).

    [8] CAO Hongye. Study on analysis of optical properties and romote sensing identification models of black and odorous water in typical cities in China(2017).

    [9] LI lingling, LI Yunmei, Heng LÜ et al. Remote sensing classification of urban black-odor water based on decision tree. Environmental Science, 41, 5060-5072(2020).

    [10] YANG Ziqian, LIU Huaiqing, Heng LÜ et al. A comprehensive classification method of urban water by remote sensing based on high-resolution images. Environmental Science, 42, 2213-2222(2021).

    [11] TANG Junwu, TIAN Guoliang, WANG Xiaoyong et al. The methods of water spectra measurement and analysisⅠ:Above-water method. Journal of Remote Sensing, 8, 37-44(2004).

    [12] MOBLEY C D. Estimation of the remote-sensing reflectance from above-surface measurements. Applied Optics, 38, 7442-7455(1999).

    [13] LIANG Xiaowen, SHAO Tiantian, WANG Tao. CDOM optical characteristics and related environmental factors of high turbidity waters on the Loess Plateau. Environmental Science, 41, 1217-1226(2020).

    [14] SONG K S, LIU G, WANG Q et al. Quantification of lake clarity in China using Landsat OLI imagery data. Remote Sensing of Environment, 243, 111800(2020).

    [15] CHEN Jiji, GUO Jing, Xu Sushi et al. Concentration and carbon isotope composition of DOC and DIC in the Miyun Reservoir watershed in summer. Environmental Science, 41, 4905-4913(2020).

    [16] WANG Shenglei. Large-scale and long-time water quality remote sensing monitoring over lakes based on water color index(2018).

    [17] GUO Wenwen. Research on the optical characteristics and remote sensing recognition of black and odorous water bodies in Changchun city.

    [18] JI Gang. Research and application on black and odorous water body by remote sensing(2017).

    [19] LI Jiaqi, DAI Huayang, LI Jiaguo et al. Remote sensing identification of heavily polluted water in urban areas. Bulletin of Surveying and Mapping, 54-58(2018).

    [20] HU Guoqing, CHEN Donghua, LIU Congfang et al. Dynamic monitoring of urban black-odor water bodies based on GF-2 image. Remote Sensing for Land and Resources, 33, 30-37(2021).

    [21] SHAO Huxiang, Ding Feng, YANG Jian et al. Model of extracting remotely-sensed information of black and odorous water based on deep learning. Journal of Yangtze River Scientifie Research Institute, 39, 156-162(2022).

    [22] ZHANG Chun, GE Yi, REN Yue et al. Semantic segmentation of rural black and odorous water body based on improved Deeplabv3+Network with remote sensing images. Remote Sensing Technology and Application, 38, 1433-1444(2023).

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    Li FU, Ge LIU, Kaishan SONG, Yongjin CHEN. 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

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    Paper Information

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    Received: May. 29, 2023

    Accepted: --

    Published Online: Jan. 7, 2025

    The Author Email:

    DOI:10.11873/j.issn.1004-0323.2024.5.1064

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