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Leveraging EO-1 to Evaluate Capability of New Generation of Landsat Sensors for Coastal/Inland Water Studies

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2 Author(s)
Nima Pahlevan ; Dep. of Environmental, Earth, and Ocean sciences, University of Massachusetts Boston, Boston, MA, USA ; John R. Schott

Monitoring coastal and inland waters, recognized as case II waters, using the existing Landsat technology is somewhat restricted because of its low signal-to-noise ratio (SNR) and its relatively poor radiometric resolution. The new generation of Landsat, Landsat Data Continuity Mission (LDCM) carrying the Operational Land Imager (OLI), has enhanced features allowing for a more lucid characterization of water constituents in either coastal or inland waters with respect to Landsat-7 (ETM+). This paper applies a physics-based approach to fully examine the potential of OLI in terms of its enhanced features in a water constituent retrieval framework. An EO-1 dataset, including Hyperion and the Advanced Land Imager (ALI), together with nearly coincident ETM+ imagery were atmospherically corrected using a data-driven approach. An in-water radiative transfer model, i.e., Hydrolight, was applied to generate a Look-Up-Table (LUT) of simulated surface reflectances for various combinations of water constituents. Using the Hyperion-derived concentration maps as validation sources, it was found that the simulated OLI imagery is superior to ETM+ on the order of 40%, 20%, and 28% when retrieving the concentrations of chlorophyll-a and total suspended solids (TSS), as well as the absorption of the colored dissolved organic matter (CDOM), respectively. It was also demonstrated that the simulated OLI imagery outperforms the simulated ALI and the recorded ALI datasets in the retrieval of chlorophyll-a and CDOM absorption. It is concluded that the new generation of Landsat enables mapping and monitoring of case II waters with accuracies not achieved with the previous Landsat satellite series.

Published in:

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  (Volume:6 ,  Issue: 2 )