Satellite-derived bathymetry using the ICESat-2 lidar and Sentinel-2 imagery datasets

Ma, Yi; Xu, Nan; Liu, Zhen; Wang, Jinhu; Yang, Fanlin; Wang, Xiaohua; Li, Song

doi:10.1016/j.rse.2020.112047

Cited by 214 publications

(150 citation statements)

References 84 publications

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“…However, early ICESat-2 prelaunch studies using high-altitude prototype ATLAS observations first identified the potential for identifying inland and near-shore bathymetry (Jasinski et al, 2016;Forfinski-Sarkozi and Parrish, 2016). Subsequently, initial on-orbit data analysis revealed ICESat-2's capability to provide potential seed elevation data for SDB (Albright and Glennie, 2020;Ma et al, 2020) based on the…”

Section: Accepted Articlementioning

confidence: 99%

ICESat‐2 Elevation Retrievals in Support of Satellite‐Derived Bathymetry for Global Science Applications

Babbel

Parrish

Magruder

2021

Geophysical Research Letters

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Section: Accepted Articlementioning

confidence: 99%

ICESat‐2 Elevation Retrievals in Support of Satellite‐Derived Bathymetry for Global Science Applications

Babbel

Parrish

Magruder

2021

Geophysical Research Letters

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“…Some studies have also shown that the ATLAS instrument can penetrate water and provide information on the bottom (Ma et al, 2020;Parrish et al, 2019). Since the Amazon River has a high concentration of sediments (Martinez et al, 2009), we assume that target information from the water represents only the water surface elevation.…”

Section: Icesat-2 Space-borne Datamentioning

confidence: 99%

Comment on essd-2021-32

Ligi¹

2021

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The characterization of estuarine hydrodynamics primarily depends on the knowledge of bathymetry and topography. Here we present the first comprehensive, high-resolution dataset of the topography and bathymetry of the Amazon River estuary, the world's largest estuary. Our product is based on an innovative approach combining space-borne remote sensing data, an extensive and processed river depth dataset, and auxiliary data. Our goal with this characterization is to promote the database usage in studies that require this information, such as hydrodynamic modeling or geomorphological assessments. Our twofold approach considered 500'000 sounding points digitized from 19 nautical charts for bathymetry estimation, in conjunction with a state-of-the-art topography dataset based on remote sensing, encompassing intertidal flats, riverbanks, and adjacent floodplains. Finally, our estimate can be accessed in a unified 30 m resolution regular grid referenced to EGM08, complemented both landward and seaward with land (MERIT DEM) and ocean (GEBCO2020) topography data. Extensive validation against independent and spatially-distributed data, from an airborne LIDAR survey, from ICESat-2 altimetric satellite data, and from various in situ surveys, shows a typical accuracy of 8.4 m (river bed) and 1.2 m (non-vegetated inter-tidal floodplains). The dataset is available at http://dx.doi.org/10.17632/3g6b5ynrdb.1 (Fassoni-Andrade et al., 2021). IntroductionThe Amazon River exports the largest discharge of freshwater (205'000 m 3 s -1 ; Callède et al., 2010) and the largest sedimentary supply (5-13 10 8 tons per year; Filizola et al., 2011) to the global ocean. However, there does not exist, up to now, any consistent, comprehensive, publicly available topographic dataset in the estuary that can support hydrodynamic, sedimentary, or ecological studies. The largest estuary in the world is home to energetic exchanges of momentum between the upstream river and the ocean, with a marked variability of the water level over a broad range of timescales, from the

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“…Normally, the signal photons from the Earth's surface concentrate in the middle of the vertical window while the noise photons distribute in the whole vertical window, i.e., in the top and bottom of the vertical window, nearly all photons are noise ones. The solar-induced background noise photons scattered by particles in atmosphere and water column were the dominant noise source [40], and the density of noise photons would be larger in water column. Since the underwater bottom corresponds to lower elevation values compared to the ground surface, the density of noise photons that locate in the lowest elevations should be larger.…”

Section: Detection Of Surface Profiles From Icesat-2 Datamentioning

confidence: 99%

“…In addition, benefitting from the bathymetric capacity and much denser ground/bottom points, the simulated ICESat-2 data (based on the airborne MABEL data) [51,52] or measured ICESat-2 data [40,[53][54][55] were used to obtain the along-track underwater bottom points and further generate the bathymetric maps with satellite images, e.g., the Landsat, Sentinel-2, and GSWD (Global Surface Water Dataset) products [4]. Although these used datasets and some steps of the method (e.g., detecting the signal photons) are similar to this study, the specific applications and other steps (e.g., the data fusion) are quite different.…”

Section: Difference From Classical Studies Using Satellite Lidarsmentioning

confidence: 99%

Monitoring Annual Changes of Lake Water Levels and Volumes over 1984–2018 Using Landsat Imagery and ICESat-2 Data

Zhang

et al. 2020

Remote Sensing

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With new Ice, Cloud, and land Elevation Satellite (ICESat)-2 lidar (Light detection and ranging) datasets and classical Landsat imagery, a method was proposed to monitor annual changes of lake water levels and volumes for 35 years dated back to 1980s. Based on the proposed method, the annual water levels and volumes of Lake Mead in the USA over 1984–2018 were obtained using only two-year measurements of the ICESat-2 altimetry datasets and all available Landsat observations from 1984 to 2018. During the study period, the estimated annual water levels of Lake Mead agreed well with the in situ measurements, i.e., the R2 and RMSE (Root-mean-square error) were 1.00 and 1.06 m, respectively, and the change rates of lake water levels calculated by our method and the in situ data were −1.36 km3/year and −1.29 km3/year, respectively. The annual water volumes of Lake Mead also agreed well with in situ measurements, i.e., the R2 and RMSE were 1.00 and 0.36 km3, respectively, and the change rates of lake water volumes calculated by our method and in situ data were −0.57 km3/year and −0.58 km3/year, respectively. We found that the ICESat-2 exhibits a great potential to accurately characterize the Earth’s surface topography and can capture signal photons reflected from underwater bottoms up to approximately 10 m in Lake Mead. Using the ICESat-2 datasets with a global coverage and our method, accurately monitoring changes of annual water levels/volumes of lakes—which have good water qualities and experienced significant water level changes—is no longer limited by the time span of the available satellite altimetry datasets, and is potentially achievable over a long-term period.

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Satellite-derived bathymetry using the ICESat-2 lidar and Sentinel-2 imagery datasets

Cited by 214 publications

References 84 publications

ICESat‐2 Elevation Retrievals in Support of Satellite‐Derived Bathymetry for Global Science Applications

ICESat‐2 Elevation Retrievals in Support of Satellite‐Derived Bathymetry for Global Science Applications

Comment on essd-2021-32

Monitoring Annual Changes of Lake Water Levels and Volumes over 1984–2018 Using Landsat Imagery and ICESat-2 Data

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