The Study of Spatial-Temporal Characteristics for CODMn in Shenzhen Reservoir Based on GF-1 WFV

被引:0
|
作者
Li J. [1 ,2 ,3 ]
Zhang W. [4 ]
Deng R. [1 ,2 ,3 ]
Lu Z. [4 ]
Liang Y. [1 ]
Shen X. [4 ]
Xiong L. [1 ]
Liu Y. [1 ]
机构
[1] School of Geography and Planning, Sun Yat-Sen University, Guangzhou
[2] Guangdong Engineering Research Center of Water Environment Remote Sensing Monitoring, Guangzhou
[3] Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, Guangzhou
[4] Huizhou branch of Guangdong Hydrology Bureau, Huizhou
基金
中国国家自然科学基金;
关键词
absorption coefficient; GF-1; WFV; Organic pollution; permanganate index; Shenzhen; water quality remote sensing;
D O I
10.11834/jrs.20219380
中图分类号
学科分类号
摘要
Permanganate index (CODMn) is an important water quality parameter to reflect the degree of organic pollution. At present, the retrieval of organic pollution by remote sensing technology is mostly based on empirical models and needs a lot of manpower for data collection. Meanwhile, it has time and space limitations since it cannot process each image under different imaging conditions adaptively. The integrated water quality index, CDOM, and DOC of the inverted parameters are not water quality indexes, which cannot be directly used for actual water quality evaluation. Therefore, a novel quantitative remote sensing technology method for the retrieval of water permanganate index with clear understanding on mechanism is proposed in this work. The method based on the radiation transmission process of electromagnetic waves and the characteristics of the water body in the study area, consider the three major water quality factors of suspended sediment, chlorophyll and oxygen-consuming organic, analyze the absorption coefficient and scattering coefficient of oxygen-consuming organic matter, separate the contribution of the water column to the remote sensing signal from the effect of the bottom, and the diffuse extinction coefficients (c) of water quality components are expressed as functions of in-water absorption (a) and scattering (b). Finally, the concentration of CODMn was derived with the remote-sensing reflectance below the surface (rrs). Experiment on the GF-1 Wide Field of View (WFV) imageries of the three major reservoirs in Shenzhen show that: the model method is reliable with overall accuracy of R2=0.832, RMSE=46.4%. The spatial-temporalcharacteristics of the three major reservoirs in Shenzhen during 2018–2019 were investigated. The overall CODMn concentration of the three major reservoirs is not high with average CODMn concentrations is less than 4 mg/L, and it is affect by mild organic pollution. No pollution diffusion occurred at the junction of the reservoirs, and the peak concentration mostly appeared near residential areas at the reservoir corner. The highest hot-spot was observed in spring and autumn, while the lowest was in Rainy summer From March 2018 to May 2019, the water quality improved, which is consistent with the background of Shenzhen's special water treatment activities in 2018. It is recommended that the core of reservoir water quality protection is to control external pollution and avoid the input of pollution sources during the flood season. In this paper, a distinct advantage of the models are broadly applicable due to their physical basis, which satisfied the requirements of the application. The model solving method is based on the inherent optical properties of typical water bodies in Guangdong Province, and inherent optical properties have seasonal variability. It is necessary to understand of the seasonal variations of inherent optical properties of water bodies would help to improve the stability of the model. In addition, the spectrum of shallow waters is affected by the depth and the reflection at the bottom, CODMn concentration inversion from satellite data with more spectrum bands remains underexplored. The RS scheme used in this study can not only provide support for inland water resources development and policy formulation in Shenzhen, but also provide a valuable reference for the evolution of inland water organic pollution in other regions. © 2022 Science Press. All rights reserved.
引用
收藏
页码:1562 / 1574
页数:12
相关论文
共 41 条
  • [1] AiKen G R, Mcknight D M, Wershaw R L., An introductio n to humic substances in soil, sediments, and water, 9, pp. 1215-1229, (1985)
  • [2] Bowers D G, Evans D, Thomas D N., Interpreting the colou r of anestuary, Estuarine, Coastal and Shelf Science, 59, 1, pp. 13-20, (2004)
  • [3] Chen C Q, Tang S L, Pan Z L, Zhan H G, Larson M, Jonss on L., Remotely sensed assessment of water quality lev els in the Pearl River Estuary, China, Marine Pollution Bullet in, 54, 8, pp. 1267-1272, (2007)
  • [4] Chen L X., Design of COD Concentration in the Eutrophica tion of Water Body Environment Remote Sensing Monitoring System, 25, pp. 59-62, (2017)
  • [5] Chen J S, He D, W Zhang Y., Is COD a suitable paramete r to evaluate the water pollution in The Yellow River, 22, 6, pp. 611-614, (2003)
  • [6] Deng R R, He Y Q, Qin Y, Chen Q D, Chen L., Mea suring pure water absorption coefficient in the near-infrared s pectrum (900-2500 nm), Journal of Remote Sensing, 16, pp. 192-206, (2012)
  • [7] Deng R R, He Y Q, Qin Y, Chen Q D, Chen L., Pure water absorption coefficient measurement after eliminating th e impact of suspended substance in spectrum from 400 nm t o 900 nm, Journal of Remote Sensing, 1, pp. 174-191, (2012)
  • [8] Deng R R, He Z J, Chen X X, Guan L J, Ke D., Quan titative Analysis on Water Pollution in the Pearl River Estua ry by Remote Sensing Method, Acta Scientiarum Naturalium Universitatis Sunyatseni, pp. 99-103, (2002)
  • [9] Deng R R, Qin Y, Liang Y H, He Y Q, Chen Q D, Xiong L H, Liu X L, Liu Y F, Lu S J, Liu Y M, Lin L., M ethod for simultaneously inverting turbidity, COD and chlorop hyll concentration of inland water bodies, (2015)
  • [10] Fichot C G, Downing B D, Bergamaschi B A, Windham-Myers L, Marvin-Dipasquale M, Thompson D R, Gierach M M., 2 016. High-Resolution Remote Sensing of Water Quality in th e San Francisco Bay-Delta Estuary, Environmental Science an d Technology, 50, 2, pp. 573-583