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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #180879
Title: ESTIMATING WATER QUALITY WITH AIRBORNE AND GROUND-BASED HYPERSPECTRAL SENSING

Author
item Sudduth, Kenneth - Ken
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: 5/16/2005
Publication Date: 7/18/2005
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.

Interpretive Summary:

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.