Submitted to: ASAE Annual International Meeting
Publication Type: Proceedings
Publication Acceptance Date: 7/18/2005
Publication Date: 11/1/2005
Citation: Sudduth, K.A., Jang, G., Lerch, R.N., Sadler, E.J. 2005. Estimating water quality with airborne and ground-based hyperspectral sensing. Proceedings of the American Society of Agricultural Engineers Annual International Meeting. Paper No. 052006. Interpretive Summary: Impairment of the environment by agricultural activities is an ongoing concern of agriculturalists, environmentalists, and the general public. To assess the effects of existing and new agricultural practices over large watersheds, we need to find more efficient ways to measure various water quality variables 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 on a single date in 2004, we obtained good relationships between remote sensing data in the visible and near-infrared wavelength ranges and the water quality variables. To estimate chlorophyll and nutrients, it was important to use hyperspectral remote sensing data, which consists of measurements at many (in this case more than 100) wavelengths. We were able to estimate turbidity with data at only 4 wavelengths (so-called multispectral data). This research demonstrates that our approach to using remote sensing to estimate water quality could provide good results, at least for measurements taken on a single date. 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: 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. Wavelength-selection (i.e., stepwise multiple regression) methods and previously reported indices 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, but still with R2 > 0.6. Previously developed narrow-band reflectance indices also worked well to estimate chlorophyll concentration. 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