Utilizing NASA and ESA Earth Observations to Monitor Turbidity Distribution in the San Francisco Bay-Delta

Authors: Katheirne Cavanaugh*, NASA DEVELOP Program - JPL, Leah Kucera, NASA DEVELOP Program - JPL, Molly Spater, NASA DEVELOP Program - JPL
Topics: Earth Science, Remote Sensing
Keywords: Earth Science, Remote Sensing, GIS, Water Quality, Turbidity, San Francisco Bay-Delta
Session Type: Paper
Day: 4/11/2018
Start / End Time: 5:20 PM / 7:00 PM
Room: Studio 1, Marriott, 2nd Floor
Presentation File: No File Uploaded


Water quality is a critical element of freshwater supply, particularly in times and areas of drought. Limited water resources can be further strained if water quality concerns are not effectively and efficiently addressed. While there are measures in place to protect human and environmental health from poor and risky water quality conditions, implementation of these measures is frequently reliant on physical water samples and fixed station data, both of which have gaps in spatial and temporal coverage of water quality conditions. This consideration is especially important in environments that are highly complex and heterogeneous, such as the San Francisco Bay-Delta. As a result, accurate remotely-sensed data can help supplement existing data, supporting more informed water management practices and representing a wealth of information that has yet to be fully leveraged. In the context of the San Francisco Bay-Delta, turbidity is a parameter of special concern, as it can be used to monitor habitat conditions for aquatic species. Water supply operations in the Bay-Delta are linked to turbidity levels, with values exceeding 12 FNU signaling habitat for the endangered Delta Smelt and triggering pump shutoffs to minimize entrainment. In this project, we evaluated the application of remotely sensed turbidity from Landsat 8, Sentinel-2, and AVIRIS-NG in the San Francisco Bay-Delta from 2013 to 2017. We conducted comparisons with in situ turbidity data from USGS and CDEC water quality stations against turbidity derived with the Semi-Empirical Single Band Blended Turbidity Algorithm (Dogliotti et al., 2015).

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