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
Black and odorous water occur frequently in rural China, and research into monitoring them by employing remote sensing technology has only recently begun, with many technical issues to be resolved.The samples were collected in rural areas of Jilin, Yunnan, and Guangxi provinces for this study, and 75 water samples from black and odorous water and 85 water samples from normal water were collected between 2021 and 2022, and their water quality parameters as well as optical properties were analyzed separately. We analyzed the image spectral characteristics of black and odorous water and normal water using GF-2 images, and observed that the reflectance of rural black and odorous water has an increasing trend in the red and near-infrared bands, whereas the reflectance of red and green bands was very low and the difference was small. Based on the two typical spectral characteristics of black and odorous water, the MBOI (Multi-spectral black and odorous water index) was developed, with a high identification accuracy. The following are the main research findings: (1) Black and odorous water have a higher concentration of total suspended particulate matter than normal water, and the concentration of organic carbon in black and odorous water bodies is 1.82 times higher compared to normal water. (2) At 440 nm, the absorption coefficients of all the materials, including algal particulate matter, non-pigmented particulate matter, and colored dissolved organic matter of black and odorous water were greater than those of normal water. (3) The data modeling and model verification are carried out by using the spectral reflectance data of the image after Rayleigh correction. When the MBOI value is between 0 and 0.18, it is determined as a black and odorous water body, and the model accuracy meets the requirements of black and odorous water body recognition.
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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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Received: May. 29, 2023
Accepted: --
Published Online: Jan. 7, 2025
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