Validation of ICESat-2 ATLAS Bathymetry and Analysis of ATLAS's Bathymetric Mapping Performance

NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in September, 2018. The satellite carries a single instrument, ATLAS (Advanced Topographic Laser Altimeter System), a green wavelength, photon-counting lidar, enabling global measurement and monitoring of elevation with a primary focus on the cryosphere. Although bathymetric mapping was not one of the design goals for ATLAS, pre-launch work by our research team showed the potential to map bathymetry with ICESat-2, using data from MABEL (Multiple Altimeter Beam Experimental Lidar), NASA’s high-altitude airborne ATLAS emulator, and adapting the laser-radar equation for ATLAS specific parameters. However, many of the sensor variables were only approximations, which limited a full assessment of the bathymetric mapping capabilities of ICESat-2 during pre-launch studies. Following the successful launch, preliminary analyses of the geolocated photon returns have been conducted for a number of coastal sites, revealing several salient examples of seafloor detection in water depths of up to ~40 m. The geolocated seafloor photon returns cannot be taken as bathymetric measurements, however, since the algorithm used to generate them is not designed to account for the refraction that occurs at the air–water interface or the corresponding change in the speed of light in the water column. This paper presents the first early on-orbit validation of ICESat-2 bathymetry and quantification of the bathymetric mapping performance of ATLAS using data acquired over St. Thomas, U.S. Virgin Islands. A refraction correction, developed and tested in this work, is applied, after which the ICESat-2 bathymetry is compared against high-accuracy airborne topo-bathymetric lidar reference data collected by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). The results show agreement to within 0.43—0.60 m root mean square error (RMSE) over 1 m grid resolution for these early on-orbit data. Refraction-corrected bottom return photons are then inspected for four coastal locations around the globe in relation to Visible Infrared Imaging Radiometer Suite (VIIRS) Kd(490) data to empirically determine the maximum depth mapping capability of ATLAS as a function of water clarity. It is demonstrated that ATLAS has a maximum depth mapping capability of nearly 1 Secchi in depth for water depths up to 38 m and Kd(490) in the range of 0.05–0.12 m−1. Collectively, these results indicate the great potential for bathymetric mapping with ICESat-2, offering a promising new tool to assist in filling the global void in nearshore bathymetry.

[1]  Vasily V. Titov,et al.  Real-Time Tsunami Forecasting: Challenges and Solutions , 2003 .

[2]  Kelly M. Brunt,et al.  Performance Analysis of Airborne Photon- Counting Lidar Data in Preparation for the ICESat-2 Mission , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[3]  M. Liceaga-Correa,et al.  Assessment of coral reef bathymetric mapping using visible Landsat Thematic Mapper data , 2002 .

[4]  Menghua Wang,et al.  Retrieval of diffuse attenuation coefficient in the Chesapeake Bay and turbid ocean regions for satellite ocean color applications , 2009 .

[5]  Marjorie Rawls Roberts,et al.  United States Virgin Islands , 2007, World Statistics Pocketbook (Ser. V).

[6]  Norbert Pfeifer,et al.  Topo-Bathymetric LiDAR for Monitoring River Morphodynamics and Instream Habitats - A Case Study at the Pielach River , 2015, Remote. Sens..

[7]  Robert A. Leathers,et al.  Ocean color remote sensing of seagrass and bathymetry in the Bahamas Banks by high‐resolution airborne imagery , 2003 .

[8]  David J. Harding,et al.  The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): Science requirements, concept, and implementation , 2017 .

[9]  C. Wayne Wright,et al.  Depth Calibration and Validation of the Experimental Advanced Airborne Research Lidar, EAARL-B , 2016, Journal of Coastal Research.

[10]  Jay Gao Bathymetric mapping by means of remote sensing : methods , accuracy and limitations , 2009 .

[11]  Nathaniel G. Plant,et al.  Analysis of the scale of errors in nearshore bathymetric data , 2002 .

[12]  Simon J. Pittman,et al.  Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reef ecosystems , 2009 .

[13]  Fred J. Tanis,et al.  Multispectral bathymetry using a simple physically based algorithm , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[14]  C. Briese,et al.  AIRBORNE LASER BATHYMETRY FOR DOCUMENTATION OF SUBMERGED ARCHAEOLOGICAL SITES IN SHALLOW WATER , 2015 .

[15]  D. Morris Optical Properties of Water , 2021, Reference Module in Earth Systems and Environmental Sciences.

[16]  Christopher Parrish,et al.  Active-Passive Spaceborne Data Fusion for Mapping Nearshore Bathymetry , 2019, Photogrammetric Engineering & Remote Sensing.

[17]  Viktor Feygels,et al.  Particularities of hydro lidar missions in the Asia-Pacific region , 2014, Asia-Pacific Environmental Remote Sensing.

[18]  R. Stumpf,et al.  Determination of water depth with high‐resolution satellite imagery over variable bottom types , 2003 .

[19]  N. K. Hojerslev,et al.  Visibility Of The Sea With Special Reference To The Secchi Disc , 1986, Other Conferences.

[20]  T. Smith,et al.  Potential role of viruses in white plague coral disease , 2013, The ISME Journal.

[21]  Kelly M. Brunt,et al.  Inland and Near-Shore Water Profiles Derived from the High-Altitude Multiple Altimeter Beam Experimental Lidar (MABEL) , 2016, Journal of Coastal Research.

[22]  Norbert Pfeifer,et al.  Exponential Decomposition with Implicit Deconvolution of Lidar Backscatter from the Water Column , 2017, PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science.

[23]  S. Charlot,et al.  WebPlotDigitizer, a polyvalent and free software to extract spectra from old astronomical publications: application to ultraviolet spectropolarimetry , 2017, 1708.02025.

[24]  G. Guenther Airborne Laser Hydrography: System Design and Performance Factors , 1985 .

[25]  W. Philpot,et al.  Bathymetric mapping with passive multispectral imagery. , 1989, Applied Optics.

[26]  Zhigang Pan,et al.  Weak Echo Detection from Single Photon Lidar Data Using a Rigorous Adaptive Ellipsoid Searching Algorithm , 2018, Remote. Sens..

[27]  Fabian C. Polcyn,et al.  TECHNIQUES FOR THE EXTRACTION OF WATER DEPTH INFORMATION FROM LANDSAT DIGITAL DATA , 1979 .

[28]  D. R. Lyzenga,et al.  Remote bathymetry and shoal detection with ERTS: ERTS water depth , 1975 .

[29]  Charmien Johnson Msfc Ice, Cloud, and Land Elevation Satellite , 2013 .

[30]  Jean-François Crétaux,et al.  Remote Sensing-Derived Bathymetry of Lake Poopó , 2013, Remote. Sens..

[31]  Christopher E. Parrish,et al.  Analysis of MABEL Bathymetry in Keweenaw Bay and Implications for ICESat-2 ATLAS , 2016, Remote. Sens..

[32]  H R Gordon,et al.  Some relationships between Secchi depth and inherent optical properties of natural waters. , 1978, Applied optics.

[33]  Lori A. Magruder,et al.  The Potential Impact of Vertical Sampling Uncertainty on ICESat-2/ATLAS Terrain and Canopy Height Retrievals for Multiple Ecosystems , 2016, Remote. Sens..