Study of spatial—temporal characteristics for CODMn in Shenzhen reservoir based on GF-1 WFV
- Vol. 26, Issue 8, Pages: 1562-1574(2022)
Published： 07 August 2022
DOI： 10.11834/jrs.20219380
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摘要
COD
Mn
是反映水体有机污染程度的一个重要水质参数。地表水有机污染遥感监测主要面临两个挑战：技术方法大多基于经验模型，依赖大量实测数据；有机污染评价的综合性指标，水质参数不明确。针对上述问题，本文从辐射传输机理出发，基于研究区水体特征，考虑悬浮泥沙、叶绿素、耗氧性有机物3大水质因子，提出一种反演机理清晰、参数意义明确的像元反射率与COD
Mn
浓度的物理模型。通过深圳市3大水库COD
Mn
浓度反演与验证，决定系数
R
2
达到0.832，模型方法可靠明。对3大水库COD
Mn
浓度时空分布特征研究，结果表明：（1）3大水库总体COD
Mn
浓度不高，受到轻度有机污染。（2）浓度高值区多在库角居民区附近，水库连接处未出现污染扩散。（3）2018年3月—2019年5月，库区水质改善，与2018年深圳市治水专项活动背景保持一致，建议水库水质保护核心是控制外源污染，避免丰水期污染源的输入。本文的模型方法是基于广东省典型水体光学参数，而水体固有光学特征具有季节差异，未来将进一步研究水体固光学特征变化模式，以提高模型的稳健性。此外，还可结合高分六号等谱段更多的卫星开展浅水区COD
Mn
浓度反演的研究。
Abstract
Permanganate index (COD
Mn
) 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 requires considerable manpower for data collection. Meanwhile
it has time and space limitations because 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. Thus
they 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.
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 and scattering coefficients of oxygen-consuming organic matter
and separate the contribution of the water column to the remote sensing signal from the effect of the bottom. 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 COD
Mn
was derived with the remote-sensing reflectance below the surface (
r
rs
).
The experiment on the GF-1 Wide Field of View (WFV) imageries of the three major reservoirs in Shenzhen shows that the model method is reliable with overall accuracy of
R
2
=0.832 and RMSE=46.4%. The spatial—temporal characteristics of the three major reservoirs in Shenzhen during 2018—2019 were investigated. The overall COD
Mn
concentration of the three major reservoirs is low with average COD
Mn
concentrations of less than 4 mg/L; it is affected by mild organic pollution. No pollution diffusion occurred at the junction of the reservoirs
and the peak concentration mostly appeared near the residential areas at the reservoir corner. The highest hotspot was observed in spring and autumn
whereas the lowest was in rainy summer From March 2018 to May 2019. The water quality improved
consistent with the background of Shenzhen’s special water treatment activities in 2018. The core of reservoir water quality protection is recommended to control external pollution and avoid the input of pollution sources during the flood season.
A distinct advantage of the models is broadly applicable due to their physical basis
which satisfied the application requirements. The model solving method is based on the inherent optical properties of typical water bodies in Guangdong Province
and these properties have seasonal variability. The seasonal variations of inherent optical properties of water bodies can improve the stability of the model. In addition
the spectrum of shallow waters is affected by the depth and the reflection at the bottom. COD
Mn
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 resource development and policy formulation in Shenzhen
but also a valuable reference for the evolution of inland water organic pollution in other regions.
关键词
CODMn有机污染吸收系数GF-1 WFV深圳水质遥感
Keywords
permanganate indexorganic pollutionabsorption coefficientGF-1 WFVShenzhenwater quality remote sensing
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