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.
引用
收藏
页码:4177 / 4199
页数:23
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