Multi-decadal improvement in U.S. lake water clarity

Topp, S.; Pavelsky, T.; Stanley, Emily H.; Xiao, Yang; Griffin, C. W.; Ross, Matthew K.

doi:10.31223/x5f01t

Cited by 1 publication

(2 citation statements)

References 51 publications

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“…In addition, the relationship between the Landsat surface reflectance product and the measured TSM performed well (R 2 = 0.82). Calculating the validation error metrics using the random validation data set data (n = 38) can reaffirm the sensor correction procedure [31]. The validation metrics for the TSM retrieval model showed the RMSE of 8.23 mg/L, MAPE of 30% and bias of −2.35 mg/L.…”

Section: A Assessment Of the Landsat-based Modelmentioning

confidence: 64%

“…While the atmospheric corrections used to generate these surface reflectance products were originally developed for terrestrial applications, a growing body of research shows that they can be used to accurately estimate inland water quality parameters [29], [30] and perform on par with water-specific atmospheric correction algorithms [31]. The Landsat surface reflectance product (from LEDAPS and LaSRC) have been used to estimate chlorophyll, clarity and CDOM in lakes, reservoirs and rivers at different reginal scales [29], [32]- [34].…”

Section: A Assessment Of the Landsat-based Modelmentioning

confidence: 99%

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Using Remote Sensing to Understand the Total Suspended Matter Dynamics in Lakes Across Inner Mongolia

Song

Wang

Liu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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An exponential function model was developed based on the red band from Landsat and in situ data. The model performed well, the calibration determination coefficient (R 2 ) was 0.82 and validation metrics root-mean-square error, mean absolute percent error, and bias was 8.23 mg/L, 30% and −2.35 mg/L, respectively. Then, the time series of total suspended matter (TSM) concentrations for the lakes across Inner Mongolia were presented using the Landsat data from 1984-2019. The results showed that the number of the lakes with TSM decrease was slightly more than that with TSM increase (57% versus 43%). A total of 70.72% of those lakes had a changing rate less than 1 mg/L/yr in TSM, while the other 29.28% had a changing rate more than 1 mg/L/yr. In some lakes (24.2%), the coefficient of variation was greater than 102% indicating significant spatial variation. The TSM less than 20 mg/L mainly appeared in the reservoirs. Based on different environmental backgrounds, this study showed comprehensive estimates (increasing and decreasing regions) of TSM concentrations. Finally, the relative roles of several factors to the TSM changes were quantified and examined at different scales. The responses of TSM changes to NDVI representing vegetation coverage indicated that vegetation played an important role for most lakes. Whilst wind speed and precipitation significantly affected a few lakes. For most lakes, the TSM were affected by multiple factors, where a single factor could not be highlighted. Nevertheless, a comprehensive analysis of the factors surrounding a lake remains relevant for determining the possible trend (better or worse) of its TSM variation.

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Section: A Assessment Of the Landsat-based Modelmentioning

confidence: 64%