2019 Remote Sensing and Cloud Computing to Support Lake Tahoe Nearshore Monitoring

No Project associated with this Finding

Finding Details

Major Findings
• Historical data analysis shows large seasonal variability with highest concentrations in the spring and winter
• The development of a universal algorithm that had acceptable correlation with in-situ periphyton chlorophyll-a concentrations was not possible due to variations in bottom type, spatial heterogeneity at each in-situ sampling site, low signal to noise of surface reflectance from water, image geolocation accuracy, and image spatial resolution
• Site and season specific algorithms using multiple Landsat surface reflectance bands and multivariate regression had higher correlation with in-situ periphyton chlorophyll-a concentrations
• Predictions of in-situ periphyton chlorophyll-a illustrate the potential for operational monitoring and gap filling of historic datasets, where the predictions have the same amount of temporal variability as the in-situ observations
• Relative periphyton chlorophyll concentration and biomass can be achieved through anomaly mapping and analysis of various Landsat water quality indices via the Climate Engine cloud computing application (
• Historical and operational monitoring of nearshore periphyton chlorophyll-a and biomass may be possible at specific sites throughout the Tahoe Basin
• Targeted sampling during satellite overpass days, documenting GPS locations for each in-situ sample would likely improve site specific correlations and prediction accuracy
• Integrating the use of new free and operational satellite data, such as Sentinel 2 and Sentinel 3, with Landsat and into cloud computing platforms such as Climate Engine has the potential to greatly improve monitoring through improved temporal and spectral resolution