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Temporal and spatial variability of turbidity in a highly productive and turbid shallow lake (Chascomús, Argentina) using a long time-series of Landsat and Sentinel-2 data
被引:3
|作者:
Gayol, Maira Patricia
[1
]
Dogliotti, Ana Ines
[1
]
Lagomarsino, Leonardo
[2
]
Zagarese, Horacio Ernesto
[2
]
机构:
[1] Univ Buenos Aires UBA, Consejo Nacl Invest Cient & Tecn CONICET, Inst Astron & Fis Espacio IAFE, C1428EGA, Buenos Aires, Argentina
[2] Univ Nacl San Martin UNSAM, Inst Tecnol Chascomus INTECH, CONICET, B7130IWA Chascomus, Buenos Aires, Argentina
关键词:
Turbidity;
Remote sensing algorithm;
Pampean lakes;
Water transparency;
ATMOSPHERIC CORRECTION;
MODEL;
PRECIPITATION;
CHASCOMUS;
PATTERNS;
D O I:
10.1007/s10750-024-05574-7
中图分类号:
Q17 [水生生物学];
学科分类号:
071004 ;
摘要:
This work aims to study the spatio-temporal variability of turbidity in Lake Chascom & uacute;s using 34 years (1987-2020) of Landsat (TM, ETM + , and OLI) and Sentinel-2-MSI optical data and to understand this variability in terms of environmental variables. A semi-analytical algorithm, using reflectance in the red and near-infrared bands, was calibrated for Landsat and Sentinel-2 bands and tested using in situ turbidity measurements. The best performance was found using only the near-infrared band with 12.84% median accuracy and -12.84% bias when comparing in situ radiometric measurements and field data. When satellite-derived turbidity was compared to in situ values, the median accuracy was 31.8% and the bias 13.22%. Monthly climatological turbidity maps revealed spatial heterogeneity in Lake Chascom & uacute;s, with differences observed between the north-west and south-east regions, particularly in summer and winter. Turbidity showed marked seasonal dynamics, with a minimum in autumn and a maximum in spring. Annual climatological turbidity maps showed significant inter-annual variability. Generalized linear models showed turbidity was positively associated with wind speed and photosynthetic active radiation (26.2% of the variability explained). Remote sensing was found to be a fundamental complement to traditional field-based methods for monitoring water quality parameters and allowing a better description of their spatio-temporal variability.
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页码:4177 / 4199
页数:23
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