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United States Department of Agriculture

Agricultural Research Service

Title: Estimating Water Quality with Airborne and Ground-Based Hyperspectral Sensing

Authors
item Sudduth, Kenneth
item Jang, Gab-Sue - U OF MO
item Lerch, Robert
item Sadler, Edward

Submitted to: ASAE Annual International Meeting
Publication Type: Abstract Only
Publication Acceptance Date: May 16, 2005
Publication Date: July 18, 2005
Citation: Sudduth, K.A., Jang, G., Lerch, R.N., Sadler, E.J. 2005. Estimating water quality with airborne and ground-based hyperspectral sensing [CDROM]. American Society of Agricultural Engineers Annual International Meeting. Abstract No. 052006.

Technical Abstract: Remotely sensed estimates of water quality parameters would facilitate efforts in spatial and temporal monitoring. In this study we collected hyperspectral water reflectance data with airborne and ground-based sensing systems for multiple arms of Mark Twain Lake, a large man-made reservoir in northeast Missouri. Water samples were also collected and analyzed in the laboratory for chlorophyll, nutrients, and turbidity. Full-spectrum (i.e., partial least squares regression) and wavelength-selection (i.e., stepwise multiple regression) were used to develop relationships between spectral and water quality data. Within the single measurement date of this study, all measured water quality parameters were strongly related (R2 > 0.6) to reflectance data from the ground system. Relationships between water quality parameters and airborne reflectance data were generally somewhat lower, with R2 > 0.5. Previously developed narrow-band reflectance indices also worked well to estimate Chlorophyll a concentration in this dataset. Wide-band, multispectral reflectance, simulating Landsat data, was strongly related only to turbidity and those other parameters (e.g., phosphorus) highly correlated to turbidity in this dataset. Thus, hyperspectral sensing, coupled with calibration sampling, can be used to estimate lake water quality differences, and appears to have advantages over multispectral sensing in this application.

Last Modified: 4/23/2014
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