Remote sensing of Lake Chicot, Arkansas: Monitoring suspended sediments, turbidity, and Secchi depth with Landsat MSS data. Harrington, J. A, Schiebe, F. R, & Nix, J. F Remote Sensing of Environment, 39(1):15–27, January, 1992. ![Remote sensing of Lake Chicot, Arkansas: Monitoring suspended sediments, turbidity, and Secchi depth with Landsat MSS data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This research used water quality data from Lake Chicot, Arkansas and a corresponding set of Landsat MSS data to compare the ability of satellite-based sensor systems to monitor suspended sediment concentration, Secchi disk depth, and nephelometric turbidity. Lake Chicot was selected, in part, because of the availability of a wide range of water quality conditions. Secchi disk depth and nephelometric turbidity are both optical measures of water quality and differ from suspended sediment concentration, which is a measure of the weight of inorganic particulates suspended in the water column. four different models for these relationships between the satellite data and the water quality data were tested: 1) simple linear regression analysis with the satellite data transformed to exo-atmospheric reflectance, 2) a simple linear regression involving a natural logarithm transformation of the satellite and water quality variables, 3) simple linear regression analysis of the digital chromaticity transformation of the satellite data and the natural logarithm of the water quality data, and 4) optimized curve fitting of a theoretically derived exponential model for the relationship between exoatmospheric reflectance and the water quality data. Two different solar spectral irradiance curves and an orbital eccentricity correction factor are tested using the exponential model. Results suggest: 1) Remote sensing from space-based platforms can provide meaningful information on water quality variability; 2) an exponential model best characterizes the relationship between the satellite data and the water quality measures investigated; 3) slight differences result from using the solar curve proposed by the World Radiation Center (as opposed to the NASA standard); and 4) predictions based on optical measures of water quality, rather than measures of the weight of particles in the water column, are slightly better when using Landsat MSS data.
@article{harrington_remote_1992,
title = {Remote sensing of {Lake} {Chicot}, {Arkansas}: {Monitoring} suspended sediments, turbidity, and {Secchi} depth with {Landsat} {MSS} data},
volume = {39},
issn = {0034-4257},
shorttitle = {Remote sensing of {Lake} {Chicot}, {Arkansas}},
url = {http://www.sciencedirect.com/science/article/pii/0034425792901379},
doi = {10.1016/0034-4257(92)90137-9},
abstract = {This research used water quality data from Lake Chicot, Arkansas and a corresponding set of Landsat MSS data to compare the ability of satellite-based sensor systems to monitor suspended sediment concentration, Secchi disk depth, and nephelometric turbidity. Lake Chicot was selected, in part, because of the availability of a wide range of water quality conditions. Secchi disk depth and nephelometric turbidity are both optical measures of water quality and differ from suspended sediment concentration, which is a measure of the weight of inorganic particulates suspended in the water column. four different models for these relationships between the satellite data and the water quality data were tested: 1) simple linear regression analysis with the satellite data transformed to exo-atmospheric reflectance, 2) a simple linear regression involving a natural logarithm transformation of the satellite and water quality variables, 3) simple linear regression analysis of the digital chromaticity transformation of the satellite data and the natural logarithm of the water quality data, and 4) optimized curve fitting of a theoretically derived exponential model for the relationship between exoatmospheric reflectance and the water quality data. Two different solar spectral irradiance curves and an orbital eccentricity correction factor are tested using the exponential model. Results suggest: 1) Remote sensing from space-based platforms can provide meaningful information on water quality variability; 2) an exponential model best characterizes the relationship between the satellite data and the water quality measures investigated; 3) slight differences result from using the solar curve proposed by the World Radiation Center (as opposed to the NASA standard); and 4) predictions based on optical measures of water quality, rather than measures of the weight of particles in the water column, are slightly better when using Landsat MSS data.},
number = {1},
urldate = {2015-11-03TZ},
journal = {Remote Sensing of Environment},
author = {Harrington, John A and Schiebe, Frank R and Nix, Joe F},
month = jan,
year = {1992},
pages = {15--27}
}
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Secchi disk depth and nephelometric turbidity are both optical measures of water quality and differ from suspended sediment concentration, which is a measure of the weight of inorganic particulates suspended in the water column. four different models for these relationships between the satellite data and the water quality data were tested: 1) simple linear regression analysis with the satellite data transformed to exo-atmospheric reflectance, 2) a simple linear regression involving a natural logarithm transformation of the satellite and water quality variables, 3) simple linear regression analysis of the digital chromaticity transformation of the satellite data and the natural logarithm of the water quality data, and 4) optimized curve fitting of a theoretically derived exponential model for the relationship between exoatmospheric reflectance and the water quality data. 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