Skip to main content
ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #293480

Title: Long-term agroecosystem research in the Central Mississippi River Basin: hyperspectral remote sensing of reservoir water quality

item Sudduth, Kenneth - Ken
item JANG, GAB-SUE - Yeungnam University
item Lerch, Robert
item Sadler, Edward

Submitted to: Journal of Environmental Quality
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/24/2014
Publication Date: 1/8/2015
Publication URL:
Citation: Sudduth, K.A., Jang, G., Lerch, R.N., Sadler, E.J. 2015. Long-term agroecosystem research in the Central Mississippi River Basin: hyperspectral remote sensing of reservoir water quality. Journal of Environmental Quality. 44:71-83. DOI:10.2134/jeq2014.02.0060.

Interpretive Summary: To assess the effects of existing and new agricultural practices over large watersheds, we need to find more efficient ways to measure water quality parameters such as chlorophyll content, turbidity (cloudiness of the water, caused mainly by sediment), and nutrients such as nitrogen and phosphorus. One method that can collect data quickly over large areas and has been used successfully for estimating water quality is remote sensing. In this study, we evaluated the use of remote sensing to estimate chlorophyll, turbidity, and nutrients in Mark Twain Lake, a large manmade reservoir in northeast Missouri. Using data collected over seven dates, we obtained good relationships between remote sensing data in the visible and near-infrared wavelength ranges and most of the water quality parameters. These findings will help us and other researchers develop ways to more efficiently estimate differences in water quality across large watersheds and within reservoirs.

Technical Abstract: In-situ methods for estimating water quality parameters would facilitate efforts in spatial and temporal monitoring, and optical reflectance sensing has shown potential in this regard, particularly for chlorophyll, suspended sediment and turbidity. The objective of this research was to develop and evaluate relationships between hyperspectral remote sensing and lake water quality parameters – chlorophyll, turbidity, and nitrogen and phosphorus species. Proximal hyperspectral water reflectance data was obtained on seven sampling dates for multiple arms of Mark Twain Lake, a large man-made reservoir in northeast Missouri. Aerial hyperspectral data were also obtained on one date. Water samples were collected and analyzed in the laboratory for chlorophyll, nutrients, and turbidity. Previously reported reflectance indices and full-spectrum (i.e., partial least squares regression) methods were used to develop relationships between spectral and water quality data. With the exception of dissolved ammonia, all measured water quality parameters were strongly related (R2= 0.7) to proximal reflectance across all measurement dates. Aerial hyperspectral sensing was only slightly less accurate than proximal sensing for the one measurement date where both were obtained. Although full-spectrum calibrations were more accurate for chlorophyll and turbidity than results from previously reported models, those models performed better for an independent test set. Because extrapolation of estimation models to dates other than those used to calibrate the model greatly increased estimation error for some parameters, collection of calibration samples at each sensing date would be required for the most accurate remote sensing estimates of water quality.