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River bathymetry retrieval from Landsat-9 images based on neural networks and comparison to SuperDove and Sentinel-2

June 22, 2022
The Landsat mission has kept an eye on our planet, including water bodies, for 50 years. With the launch of Landsat-9 and its onboard Operational Land Imager 2 (OLI-2) in September 2021, more subtle variations in brightness (14-bit dynamic range) can be captured than previous sensors in the Landsat series (e.g., 12-bit Landsat-8). The enhanced radiometric resolution of OLI-2 appeals to the aquatic remote sensing community because the instrument might be capable of resolving smaller differences in water-leaving radiance. This study evaluates the potential to map river bathymetry from Landsat-9 imagery. We employ a neural network (NN)-based regression model for bathymetry retrieval and compare the results with optimal band ratio analysis (OBRA). The effect of Landsat-9 pan-sharpening on depth retrieval is also examined. In addition, we perform an intersensor comparison with Sentinel-2 and newly available 8-band SuperDoves from the PlanetScope constellation. Depth retrieval results from the Colorado and Potomac Rivers imply that Landsat-9 provided more accurate bathymetry across a range of depths up to 20 m, particularly when pan-sharpened. Downsampling the SuperDove data improved bathymetry retrieval due to enhanced signal-to-noise ratio, most notably in deep waters (maximum detectable depth increased from ∼15 to ∼20 m). Similarly, the enhanced spectral resolution of 8-band SuperDoves improved depth retrieval relative to 4-band Doves. The NN-based model outperformed OBRA by incorporating more spectral information.

Citation Information

Publication Year 2022
Title River bathymetry retrieval from Landsat-9 images based on neural networks and comparison to SuperDove and Sentinel-2
DOI 10.1109/JSTARS.2022.3187179
Authors Milad Niroumand-Jadidi, Carl J. Legleiter, Francesca Bovolo
Publication Type Article
Publication Subtype Journal Article
Series Title Journal of Selected Topics in Applied Earth Observation and Remote Sensing
Index ID 70232699
Record Source USGS Publications Warehouse
USGS Organization WMA - Observing Systems Division

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