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) K_d(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 K_d(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.

Jasinski, M.; Stoll, J.; Cook, W.; Ondrusek, M.; Stengel, E., and Brunt, K., 2016. Inland and near-shore water profiles derived from the high-altitude Multiple Altimeter Beam Experiemental Lidar (MABEL). In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 44–55. Coconut Creek (Florida), ISSN 0749-0208.The Advanced Topographic Laser Altimeter System (ATLAS) on the Ice, Cloud, and Land Elevation Satellite (ICESat-2) mission is a six beam, low energy, high repetition rate, 532-nm laser transmitter with photon counting detectors. Although designed primarily for detecting height changes in ice caps, sea ice, and vegetation, the polar-orbiting satellite will observe global surface water during its designed three-year life span, including inland water bodies, coasts, and open oceans. In preparation for the mission, an ICESat-2 prototype, the Multiple Altimeter Beam Experimental Lidar (MABEL), was built and flown on high-altitude aircraft experiments over a range of inland and near-shore targets. The purpose was to test the ATLAS concept and to provide a database for developing an algorithm that detects along track surface water height and light penetration under a range of atmospheric and water conditions. The current analysis examines the datasets of three MABEL transects observed from 20 km above ground of coastal and inland waters conducted in 2012 and 2013. Transects ranged from about 2 to 12 km in length and included the middle Chesapeake Bay, the near-shore Atlantic coast at Virginia Beach, and Lake Mead. Results indicate MABEL's high capability for retrieving surface water height statistics with a mean height precision of approximately 5–7 cm per 100-m segment length. Profiles of attenuated subsurface backscatter, characterized using a Signal to Background Ratio written in Log10 base, or LSBR0, were observed over a range of 1.3 to 9.3 m, depending on water clarity and atmospheric background. Results indicate that observable penetration depth, although primarily dependent on water properties, was greatest when the solar background rate was low. Near-shore bottom reflectance was detected only at the Lake Mead site down to a maximum of 10 m under a clear night sky and low turbidity of approximately 1.6 Nephelometric Turbidity Units (NTU). The overall results suggest that the feasibility of retrieving operational surface water height statistics from space-based photon counting systems such as ATLAS is very high for resolutions down to about 100 m, even in partly cloudy conditions. The capability to observe subsurface backscatter profiles is achievable but requires much longer transects of several hundreds of meters.

Accurate and reliable bathymetric data are needed for a wide diversity of marine research and management applications. Satellite-derived bathymetry represents a time saving method to map large shallow waters of remote regions compared to the current costly in situ measurement techniques. This study aims to create very high-resolution (VHR) bathymetry and habitat mapping in Mayotte island waters (Indian Ocean) by fusing 0.5 m Pleiades-1 passive multispectral imagery and active ICESat-2 LiDAR bathymetry. ICESat-2 georeferenced photons were filtered to remove noise and corrected for water column refraction. The bathymetric point clouds were validated using the French naval hydrographic and oceanographic service Litto3D^® dataset and then used to calibrate the multispectral image to produce a digital depth model (DDM). The latter enabled the creation of a digital albedo model used to classify benthic habitats. ICESat-2 provided bathymetry down to 15 m depth with a vertical accuracy of bathymetry estimates reaching 0.89 m. The benthic habitats map produced using the maximum likelihood supervised classification provided an overall accuracy of 96.62%. This study successfully produced a VHR DDM solely from satellite data. Digital models of higher accuracy were further discussed in the light of the recent and near-future launch of higher spectral and spatial resolution satellites.

Terrestrial hydrologic trends over the conterminous United States are estimated for 1980–2015 using the National Climate Assessment Land Data Assimilation System (NCA-LDAS) reanalysis. NCA-LDAS employs the uncoupled Noah version 3.3 land surface model at 0.125° × 0.125° forced with NLDAS-2 meteorology, rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth, and irrigation products. Mean annual trends are reported using the nonparametric Mann–Kendall test at p < 0.1 significance. Results illustrate the interrelationship between regional gradients in forcing trends and trends in other land energy and water stores and fluxes. Mean precipitation trends range from +3 to +9 mm yr⁻¹ in the upper Great Plains and Northeast to −1 to −9 mm yr⁻¹ in the West and South, net radiation flux trends range from +0.05 to +0.20 W m⁻² yr⁻¹ in the East to −0.05 to −0.20 W m⁻² yr⁻¹ in the West, and U.S.-wide temperature trends average about +0.03 K yr⁻¹. Trends in soil moisture, snow cover, latent and sensible heat fluxes, and runoff are consistent with forcings, contributing to increasing evaporative fraction trends from west to east. Evaluation of NCA-LDAS trends compared to independent data indicates mixed results. The RMSE of U.S.-wide trends in number of snow cover days improved from 3.13 to 2.89 days yr⁻¹ while trend detection increased 11%. Trends in latent heat flux were hardly affected, with RMSE decreasing only from 0.17 to 0.16 W m⁻² yr⁻¹, while trend detection increased 2%. NCA-LDAS runoff trends degraded significantly from 2.6 to 16.1 mm yr⁻¹ while trend detection was unaffected. Analysis also indicated that NCA-LDAS exhibits relatively more skill in low precipitation station density areas, suggesting there are limits to the effectiveness of satellite data assimilation in densely gauged regions. Overall, NCA-LDAS demonstrates capability for quantifying physically consistent, U.S. hydrologic climate trends over the satellite era.

The hydrodynamics of low-lying riverine floodplains and wetlands play a critical role in hydrology and ecosystem processes. Because small topographic features affect floodplain storage and flow velocity, a hydrodynamic model setup of these regions imposes more stringent requirements on the input Digital Elevation Model (DEM) compared to upland regions with comparatively high slopes. This current study provides a systematic approach to evaluate the required relative vertical accuracy and spatial resolution of current and future satellite-based altimeters within the context of DEM requirements for 2-D floodplain hydrodynamic models. A case study is presented for the Atchafalaya Basin with a model domain of 1190 km². The approach analyzes the sensitivity of modeled floodplain water elevation and velocity to typical satellite-based DEM grid-box scale and vertical error, using a previously calibrated version of the physically-based flood inundation model (LISFLOOD-ACC). Results indicate a trade-off relationship between DEM relative vertical error and grid-box size. Higher resolution models are the most sensitive to vertical accuracy, but the impact diminishes at coarser resolutions because of spatial averaging. The results provide guidance to engineers and scientists when defining the observation scales of future altimetry missions such as the Surface Water and Ocean Topography (SWOT) mission from the perspective of numerical modeling requirements for large floodplains of O[10³] km² and greater.

A method is presented for estimating subaqueous integrated backscatter using near-nadir viewing satellite lidar. The algorithm takes into account specular reflection of laser light, laser scattering by wind-generated foam as well as sun glint and solar scattering from foam. The formulation is insensitive to the estimate of wind speed but sensitive to the estimate of transmittance used in the atmospheric correction. As a case study, CALIOP data over Tampa Bay were compared to MODIS 645 nm remote sensing reflectance, which previously has been shown to be nearly linearly related to turbidity. The results indicate good correlation on nearly all CALIOP cloud-free dates during the period 2006 through 2007, particularly those with relatively high atmospheric transmittance. The correlation decreases when data are composited over all dates but is still statistically significant, a possible indication of variability in the biogeochemical composition in the water. Overall, the favorable results show promise for the application of satellite lidar integrated backscatter in providing information about subsurface backscatter properties, which can be extracted using appropriate models.