Monitoring intra-annual water quality variations using airborne hyperspectral remote sensing data in Iowa lakes

Ramanathan Sugumaran; Nathan Green; Maureen Clayton

doi:10.1117/1.2790445

1 September 2007 Monitoring intra-annual water quality variations using airborne hyperspectral remote sensing data in Iowa lakes

Ramanathan Sugumaran, Nathan Green, Maureen Clayton

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 1, Issue 1, 013533 (September 2007). https://doi.org/10.1117/1.2790445

Abstract

The goal of this research was to explore the intra-annual water quality variations using airborne hyperspectral remote sensing data in two lakes of Iowa, USA. Hyperspectral images were collected using the Airborne Imaging Spectroradiometer for Applications (AISA) on five dates from June through October 2004. Water samples and location information using Global Positioning System (GPS) were also collected nearly simultaneously with the hyperspectral images for these lakes. By analyzing relationships between principal components of reflectance at specific wavelength intervals and water quality data from on site sampling, prediction algorithms were developed for three major water quality constituents, chlorophyll a (Chl), turbidity and Secchi disk depth (SDD). The prediction algorithms were applied to the hyperspectral images to create spatially continuous water quality maps for all five months. Maps of Chl, turbidity and SDD were created for both lakes with an accuracy of R2 = 0.8911, 0.9373 and 0.8551 respectively. Using this data, it was found that Silver Lake was hypereutrophic on June 3, July 8 and July 31 and was borderline eutrophic/hypereutrophic on September 2 and October 5. Casey Lake was eutrophic on June 3, July 8 and July 31 and was borderline eutrophic/hypereutrophic on September 2 and October 5.

Citation Download Citation

Ramanathan Sugumaran, Nathan Green, and Maureen Clayton "Monitoring intra-annual water quality variations using airborne hyperspectral remote sensing data in Iowa lakes," Journal of Applied Remote Sensing 1(1), 013533 (1 September 2007). https://doi.org/10.1117/1.2790445

Published: 1 September 2007

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