Authors: Hongxing Liu*, University of Alabama, Min Xu, University of Alabama, Richard Beck, University of Cincinnati, Molly Reif, JALBTCX of USACE, Erich Emery, Great Lakes and Ohio River Division of USACE
Topics: Water Resources and Hydrology, Remote Sensing
Keywords: water quality, lakes, remote sensing
Session Type: Poster
Start / End Time: 9:55 AM / 11:35 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: No File Uploaded
Recent advances in satellite remote sensing systems have provided inexpensive or free multispectral images with appropriate spatial resolutions, necessary spectral bands, and regular revisits, which have enabled water quality assessments for lakes and reservoirs at a regional scale. In the previous studies, Landsat TM, MSS, and ETM+ have been extensively used to assess the regional water clarity of lake rich states, including Wisconsin, Minnesota, Maine, and Michigan. This research aims to derive water quality parameters of lakes and reservoirs in the USACE Louisville District during 2013-2017 by combining time-series satellite multispectral image data acquired by Landsat-8 and Sentinel-2A and -2B. Clear-sky multispectral images (cloud cover < 10%) will be identified, and lake and reservoir masks in the National Hydrography Dataset will be used to select the lakes and reservoirs larger than 10,000 m2 (100 m by 100m) for this analysis. After atmospheric correction of multispectral imagery, water-leaving reflectance values will be used together with the in situ measurements to construct an ensemble model based on multiple empirical algorithms. in situ water quality measurements from multiple sources within a 7-day time window of satellite overpass will be utilized for model calibration. Both turbidity/Secchi disk depth and Chl-a will be derived for lakes and reservoirs in the USACE Louisville District during 2013-2017. The spatial pattern and temporal variability of water quality parameters of lakes and reservoirs in this region will be examined, and lakes and reservoirs will be classified into different types according to their properties.