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Abstract
In shallow nearshore waters, seafloor heights and properties can be accurately measured by the current generation of space-based elastic backscatter lidars: CALIOP, flying aboard the CALIPSO satellite and ATLAS aboard ICESat-2. CALIOP’s 532 nm volume depolarization ratios, together with the ratios of the attenuated backscatter coefficients measured at 532 nm and 1064 nm, can efficiently distinguish optically shallow waters from nearby land surfaces and deep oceans. ATLAS’s high vertical resolution photon measurements can accurately determine seafloor depths in shallow water bodies, characterize seafloor reflectance, and provide assessments of ocean biomass concentrations in the intervening water column. By adding bathymetry, seafloor optical properties (e.g., reflectance, depolarization ratio and attenuated backscatter), and nighttime observations, space lidar measurements obtained in nearshore waters can provide a wealth of unique information to complement existing satellite-based ocean color remote sensing capabilities. The results reported here demonstrate the feasibility of using satellite lidars for nearshore seafloor ecosystem analyses, which in turn provide critical insights for studies of coastal navigation and seabed topography changes due to disasters, as well as the temporal and spatial morphological evolution of coastal systems.
© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
In the nearshore regions of our planet, reliable bathymetric data and seafloor properties are fundamental parameters required to support the safety of surface and sub-surface navigation, aid in habitat and fisheries protection, and model the morpho-hydrodynamics of coastal areas for flood forecasting and coastal zone management [1–4]. A number of algorithms are available for nearshore bathymetry studies using either active sensor measurements (e.g., sonar and lidar) or passive multispectral imagery [5–9]. Recent advances in satellite passive remote sensors capabilities (e.g., WorldView2 [6], Landsat-8 [7,8], and Sentinel-2 [8,9]) have motivated the development of the so-called Satellite-Derived Bathymetry (SDB) family of retrieval methods, which are being widely adopted as a cost-effective and spatially extensive new survey technique [3]. The fundamental principle used by SDB to extract bathymetric information from satellite imagery is that different wavelengths of the solar spectrum penetrate the water bodies to different depths. Therefore, different wavelength combinations (e.g., blue and green bands) are used by SDB to determine the bathymetry. Although the traditional bathymetric mapping using ship-borne acoustic or air-borne lidar surveys can provide highly accurate data, these approaches impose specific cost and time constraints compared to the extensive coverage and high-frequency monitoring of SDB. However, the preexisting ‘seed water depths’, together with spectrally-dependent atmospheric corrections, are required by most of SDB empirical methods to retrieve accurate water depths from satellite multispectral imagery [5].
The space lidar has the potential to complement the SDB by directly measuring the required reference water (or bottom) depth in shallow waters and by measuring the intervening atmospheric conditions over all waters. Recent studies [10–17] indicate that ocean subsurface optical properties can be obtained from two space-based lidars: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite [18–20] and the Advanced Topographic Laser Altimeter System (ATLAS) onboard the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) satellite [21,22].
Since launch, newly developed applications of CALIOP data for global scale plankton retrievals have provided a first glimpse into a ‘new lidar era in satellite oceanography’ [23–26]. However, the use of CALIOP lidar measurements for nearshore bathymetry and seafloor studies is still rare. With a smaller footprint than CALIOP and highly acurate photon counting detectors [27], ATLAS affords a powerful new tool for addressing nearshore bathymetry [28–31], water level changes in reservoirs [32], sea surface height anomalies [33,34], and ocean wave characteristics [35], as well as melt pond depths over ice shelves [36] and sea ice [37]. At present, however, there are very few studies using ATLAS attenuated backscatter measurements to characterize seafloor reflectance. Compared with ATLAS high resolution elevation measurements, CALIOP’s coarse vertical resolution (30 m in the atmosphere, 22.5 m in the water) and its non-ideal transient response of the 532 nm detectors [38–40] present substantial challenges in retrieving high accuracy bathymetry and melt pond depths directly from the CALIOP measurements. However, CALIOP provides 532 nm depolarization ratios and attenuated backscatter color ratios (532 nm/1064 nm) that are not available from ATLAS, and these can provide a wealth of unique information to complement ATLAS high resolution altimetry results.
The present paper aims at presenting the bathymetry and seafloor optical properties (e.g., depolarization ratio, attenuated backscatter, and reflectance) from two space lidars: CALIOP/CALIPSO and ATLAS/ICESat-2 over the Great Bahama Bank (GBB) located in the Atlantic Ocean (latitude: 21°−27°N, longitude: 75°−81°W). In section 2 we present datasets used in this study including CALIOP [41–44] and ATLAS [27] lidar data, the General Bathymetric Chart of the Oceans (GEBCO) [45], Landsat-8 [7] bathymetric products, Moderate Resolution Imaging Spectroradiometer (MODIS) images [46,47] and ocean color data [48], as well as ancillary information such as ocean surface wind speeds and ocean tides. The retrieved bathymetry results and seafloor properties over GBB shallow water areas from the two space lidars and their comparisons with bathymetry products and MODIS ocean color records are given in sections 3 and 4, respectively. Finally, section 5 presents some brief conclusions.
2. Datasets
2.1 CALIOP data
CALIOP is a dual-wavelength (532 nm and 1064 nm), polarization sensitive (at 532 nm) elastic backscatter lidar that has been making measurements between 82°S and 82°N since June 2006 [18,19]. The fundamental sampling resolution of the CALIOP lidar is 30 meters vertical and 333 meters horizontal with a laser beam diameter of 70 meters at the Earth’s surface.
The analysis and results presented in this work use CALIOP Level 1 (L1) version 4.1 data products [43,44], in which the calibration of the attenuated backscatter coefficients ($\beta $, sr−1 km−1) at 532 nm [41,42] and 1064 nm [49] is significantly improved with respect to previous versions. For this study, we only select CALIOP measurements over ocean surface under almost clear sky conditions (e.g., the integrated attenuated backscatter at 532 nm of the atmospheric column above the ocean surface lower than 0.02 sr−1) [13] from June 2006 to November 2021 in the studied GBB area. The ocean layer-integrated attenuated backscatter (IAB, sr−1) is calculated using [13],
In this paper, optically shallow waters are defined as aquatic areas where the lidar signals and subsurface remote sensing reflectance (Rrs, sr−1) are affected by the bottom substrate. Due to CALIOP’s coarse vertical resolution and the non-ideal transient response of its 532 nm detectors [18], subsurface components of the backscattered signals cannot be separated from the ocean surface signals. Consequently, CALIOP total IAB532 measurements in optically shallow waters include contributions from the ocean surface and the subsurface that includes the water column and as well abackscattered signals from the seafloor. But since Fresnel reflection does not change the polarization state of the incident laser light, surface reflection of the laser pulse does not contaminate CALIOP’s 532 nm perpendicular channel signal. Consequently, CALIOP’s IAB532s measurement is dominated by subsurface reflection and the IAB532p measurement is dominated by ocean surface reflection. For optically shallow water, the magnitude of the IAB532s measured in CALIOP’s perpendicular channel is mostly from the seafloor, which can be more than 5 times higher than the water column contributions, depending on bathymetry and seafloor properties estimated from ATLAS in section 4.
2.2 ATLAS data
ATLAS is a 532 nm photon-counting laser altimeter with a 10 kHz pulse repetition rate, a footprint diameter of ∼11 m, and an along-track sampling interval of 0.7 m at the Earth’s surface [21,51,52]. In this work, we use version 5 of ICESat-2 ATL03 geolocated photon data [27], which are publicly available through NSIDC [53]. The ATL03 product was designed to be a single source for all photon data and ancillary information (e.g., the ATLAS impulse response function) required by one or more higher-level data products [54].
ATLAS measured ocean cases with surface photon counts between 1 and 12 per pulse from October 2018 to November 2021 are selected. Cases for which the ocean surface return is greater than 12 counts/pulse or less than 1 count/pulse are removed in the retrieval process due to possible saturation from specular reflection and low signal-to-noise ratio, respectively.
One of the major challenges for passive ocean color remote sensing (e.g., MODIS) and SDB is accurately accounting for atmospheric contributions to the measured top-of-atmosphere radiances. This atmospheric correction issue for the determination of bottom depth is largely eliminated in ATLAS retrievals because atmosphere signals, ocean surface, and subsurface signals are separated in time (or photon heights), allowing equivalent retrieval accuracies under clear sky, cloudy, or heavy-aerosol-load conditions. To retrieve the ocean subsurface attenuated backscatter from spaceborne lidar measurements, atmospheric attenuation can be easily calibrated through a comparison of ATLAS measured ocean surface photon counts and the theoretical ocean surface reflectance (Rtheory, sr−1) at 532 nm [15]. Then, ICESat-2 retrieved seafloor attenuated backscatter (Rrsb, sr−1) originated from seafloor reflectance (Rb) at bottom depth z can be calculated as,
whereIn Eq. (4), S(0) and S(z) represent ATLAS measured photon counts from, respectively, the sea surface and the seafloor, both corrected for the detector’s after pulsing effects [55]. The theoretical ocean surface reflectance, Rtheory, is estimated using Eq. (5), where Fλ is the Fresnel reflectance coefficient for seawater (0.0209 at 532 nm), $\sigma _{ws}^2(w )$ is the ocean surface wave slope variance as a function of wind speed (w, m/s) [56], and $\theta \; $ is the ATLAS lidar off-nadir pointing angle. Note that ICESat-2 derived seafloor attenuated backscatter Rrsb is a function of seafloor (or bottom) reflectance (Rb), two-way transmittance through the water column ($T_w^2$), and air-water transmittance at 532 nm ($t \approx $ 0.98) as,
The bottom reflectance is not directly observable since it is modified by the effects of absorption and scattering in the overlying water column. Assuming a constant lidar attenuation coefficient ($\alpha $, m−1) from surface (at 0 m) to water bottom (at z m), the two-way transmittance through the water column is $T_w^2$=exp(-2 $\alpha $ z). For the spot size of the ICESat-2 lidar, the lidar attenuation coefficient $\alpha $ is equal to the diffuse attenuation coefficient (kd, m−1) [15]. With MODIS diffuse attenuation coefficient product (kd) at 490 nm scaled to 532 nm [13], the bottom reflectance (Rb) can be estimated using Eq. (6) with ICESat-2 retrieved bottom depths (z) in section 3 and seafloor attenuated backscatter (Rrsb) in section 4. Note that differences in water column properties would only modify the values of $T_w^2$ for retrieving bottom reflectance and would not modify the Eq. (6) used to retrieve bottom reflectance.
2.3 GEBCO and Landsat-8 bathymetry datasets
GEBCO is a joint project of the International Hydrographic Organization and the Intergovernmental Oceanographic Commission. The purpose of GEBCO is to provide the most authoritative publicly available bathymetry of the world’s oceans [45]. GEBCO produces a range of bathymetric data products, where the gridded data sets consist of grid cells at equally spaced intervals of longitude and latitude. The GEBCO’s 1 arc-minute (∼2 km resolution) and 15 arc-second (∼500 m resolution) grid products are used in this study.
Landsat-8 bathymetry datasets used in this paper are from Landsat-8 Operational Land Imager (OLI) multiband images made on Sep. 1, Dec. 6, 13, 31, 2020. An innovative neural network (NN) model has been established on a large training data matchup between Landsat-8 and ICESat-2 covering a wide range of water and bottom properties [7]. The Landsat-8 Rayleigh corrected top of atmosphere reflectance spectrum (443-2300 nm) were used as an input and the ICESat-2 retrieved water depths (after correcting tidal differences) as the ‘true’ bottom depth [7]. The NN model was applied to the Landsat-8 images over the studied shallow waters to retrieve the GBB bathymetry. Compared with the CALIOP and ATLAS line measurements in Fig. 1(a) (red and purple lines, respectively), the Landsat-8 OLI images have the 30 m multi-spectral (visible, NIR, SWIR) ground sample resolutions along a 185 km wide swath across the Earth’s surface [57].
Fig. 1. (a) CALIPSO (red line) and ICESat-2 (purple) ground tracks, the green color indicates the shallow water area where the seafloor can be detected by the space lidars. The background image is from MODIS on NASA’s Aqua satellite [46]; (b) CALIOP depolarization ratios measured along the ground track shown in red in (a), with depolarization intervals represented by the colors shown in the color bar; (c) The ICESat-2 photon heights along the ground track shown in purple in (a) with the colors representing the photon rates per pulse, and clearly indicating the water surface, seafloor, and reef edges.
2.4 Other datasets
MODIS-Aqua level 3 mapped monthly remote sensing reflectance (Rrs, sr−1) products at 531 nm were used to compare with the space lidar retrieved results (CALIOP/CALIPSO IAB532s and ATLAS/ICESat-2 Rrsb) over shallow water areas of the GBB. MODIS-Aqua level 3 mapped annual mean of diffuse attenuation coefficient (kd) at 490 nm were used to calculate the shallow water column two-way transmittance ($T_w^2$). MODIS ocean color data are provided by the NASA Ocean Color Website [58]. The second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) from Global Modeling and Assimilation Office (GMAO) is a NASA atmospheric reanalysis that begins in 1980 [59]. Wind speeds at 10 m above the surface from MERRA-2 1-hourly instantaneous two-dimensional data products [60] are used for calibration of the ICESat-2 measured photons (Eqs. (4) and (5)). Moreover, the ocean tides from Oregon State University (OSU) barotropic tidal models [61,62] are applied to the bathymetry results depending on the acquisition time of the space lidars’ measurements.
3. Bathymetry results
3.1 Shallow water detection
The study area in Fig. 1(a) shows the large swaths of ocean water glowing peacock blue that was captured by MODIS on NASA’s Aqua satellite on February 20, 2009 under relatively clear skies [46]. These waters owe their peacock blue color to their shallow depths. The red lines in Fig. 1(a) show the CALIPSO ground tracks on March 30, 2021, and the purple lines show ICESat-2 tracks on March 28, 2019, with the green dots indicating passage over shallow waters. The corresponding CALIOP measured depolarization ratios and ATLAS measured photon heights along the ground tracks are given in Fig. 1(b) and 1(c), respectively.
The first step of nearshore bathymetry study is to distinguish optically shallow water from optically deep water and nearby land surfaces from lidar measurements. As shown in Fig. 1(b), the shallow water is identified when the CALIOP measured surface depolarization ratio is higher than 0.1 (e.g., green, yellow, and red colors), while the deep water is identified as blue color with values of depolarization ratio less than 0.1. From ATLAS measured photons shown in Fig. 1(c), the shallow water can be clearly separated from the deep water when the second layer (seafloor) is detected. The green dots in Fig. 1(a) indicate the shallow waters observed by CALIOP and ATLAS.
Figure 2 shows the CALIOP measured depolarization ratio (a) and color ratio (b) defined in Eqs. (2) and (3) in almost clear sky conditions averaged for the 2006-2020 period to 0.2 latitude by 0.2 longitude pixels. The water absorption is ∼12.20 m−1 at 1064 nm and ∼0.044 m−1 at 532 nm [63,64]. The water penetration depth is only several centimeters at 1064 nm due to the strong absorption at 1064 nm, while the water penetration depth at 532 nm is on the order of tens of meters. Therefore, the CALIOP measured IAB1064 has only ocean surface contribution, but IAB532 has both ocean surface and seafloor contributions for shallow waters. The higher values of depolarization ratio and color ratio are due to the shallow seafloor depths and the fact that the CALIOP measured signals from the 532 nm perpendicular channel have seafloor contributions. The histograms in Fig. 3(a) and (b) show the CALIOP depolarization ratio distributions over the global open ocean and shallow water regions shown in Fig. 1(a). The depolarization ratios of the deep ocean are seen to be less than 0.06, while for shallow waters, when the perpendicular signal IAB532s has seafloor contributions, the depolarization ratios are notably larger and can be up to 0.5.
Fig. 2. (a) CALIOP depolarization ratio; (b) CALIOP color ratio. The color bar shows the values of depolarization and color ratios. Data have been averaged for the 2006-2020 period to 0.2 latitude by 0.2 longitude pixels.
Fig. 3. (a) CALIOP depolarization ratio histogram over global deep ocean; (b) CALIOP depolarization ratio histogram over coastal area shown in Fig. 1(a); (c) 2-D histogram of depolarization ratio and color ratio for the coastal water, the color bar shows the number of CALIOP single shot observations.
Color-coded 2-D distributions of depolarization ratio and color ratio for GBB waters are given in Fig. 3(c), while Table 1 lists the typical values of depolarization ($\delta $) and color ratios ($\chi $) for global deep water, GBB shallow water, and GBB nearby land surfaces obtained from CALIOP measurements. The land surfaces, such as bare soil, grass, trees, concrete or asphalt without snow/ice cover, generally show a steady rise in reflectance as wavelength increases from the visible to the near infrared (e.g., land surface reflectance is <0.2 in the visible and higher than 0.3 in the near infrared) [50], which result in the attenuated backscatter color ratio (IAB532/IAB1064) of these land surfaces being lower than 1. For example, the color ratio is ∼0.46 ± 0.17 for land surfaces within the GBB studied areas shown Fig. 1(a). As indicated in Fig. 3(c), the color ratios in studied shallow waters increase from 1 to ∼2.5 with the depolarization ratio varying from 0.06 to 0.5. This quasi-linearly relationship between depolarization ratio and color ratio shown in Fig. 3(c) is due to the large seafloor contribution to the CALIOP measured IAB532s from 532 nm perpendicular channel. The results shown in Figs. 2 and 3 demonstrate that the relationship between integrated depolarization ratio and integrated attenuated backscatter color ratio can efficiently separate shallow water from deep water and land surfaces.
Table 1. Typical values of depolarization ratio (${\delta }$) and color ratio (${\chi }$) for global deep water, GBB optically shallow water and GBB nearby land surfaces obtained from CALIOP measurements.
3.2 Bathymetry results
The super resolution altimetry (SRA) method [65] is applied to CALIOP measured attenuated backscatter coefficients for ocean surface and seafloor height retrievals. More details on the SRA retrieval method can be found in [61]. Because the water absorption is strong at 1064 nm and Fresnel reflection does not change the polarization state of the laser light, the CALIOP measured signals from 1064 nm channel and 532 nm parallel channels are dominated by the ocean surface, and the measured signals from 532 nm perpendicular channel are almost entirely from the seafloor in optically shallow water. As a result, the ocean surface height is obtained from CALIOP 532 nm parallel and 1064 nm channels, while the seafloor height is obtained from CALIOP 532 nm perpendicular channel by SRA method. The water depth is defined as the height differences between ocean surface and seafloor, that is, the height differences between 532 nm parallel and perpendicular channels (532p – 532s), and 1064 nm and 532 nm perpendicular channels (1064 – 532s). The CALIOP obtained water depths are corrected for tidal effects by using OSU tide models [61] at CALIOP measurement times and locations.
The method by Parrish [28] is used to retrieve the water depth from ATLAS measured photons. In version 5 of ATL03 data, the geoid is provided in the EGM2008 tide-free system, so the water depths obtained directly from the seafloor photon height (e.g., Fig. 1(c)) do not need additional tidal corrections.
As an example, Fig. 4 shows one granule of bathymetry results along ICESat-2 and CALIPSO ground tracks as shown in purple and red lines in Fig. 1(a). Figure 4(a) shows the bottom depth comparisons from ATLAS (red), Landsat-8 (purple), GEBCO 1 arc-minute (blue) and 15 arc-second (green) bathymetry products along ICESat-2 footprints. The ATLAS retrieved water depths agree very well with Landsat-8 results, except the areas of reef edges where the depths change very fast. As shown in Fig. 1(c) and Fig. 4(a), ATLAS can capture the reef edges due to its high vertical and horizontal resolutions and small footprints, while Landsat-8 provides the mean water depth within the 30 m pixels. Comparisons between the CALIOP water depths (1064 – 532s) and the other bathymetry products along the CALIPSO ground tracks are given in Fig. 4(b), with the single shot results shown as black dots and 5 shots running means in gray. The CALIOP water depths are generally in good agreement with ATLAS and Landsat-8 results. Because the CALIPSO and ICESat-2 satellites do not share any identical orbit tracks, the results in Fig. 4(b) are the water depths for the ICESat-2, Landsat-8 and GEBCO pixels that are nearest to the CALIPSO ground track locations. The results in Fig. 4 indicate that the ATLAS, Landsat-8 and CALIOP estimates are more consistent with one another than they are with the GEBCO gridded bathymetric data. However, in the region between 24°N and 25.5°N in Fig. 4(b), CALIOP water depths (gray) appear to agree better with the GEBCO bathymetric results (green dots). Note that the measurements time and date of ATLAS, CALIOP and Landsat-8 instruments in Fig. 4 are different, therefore the water depths can be different with time due to erosion and sediment transport.
Fig. 4. Comparison of water bottom depths from different sources: (a) ATLAS/ICESat-2 (red), GEBCO 1 arc-minute (blue) and 15 arc-second (green) gridded bathymetric data products, and Landsat-8 (purple); (b) Water depths from CALIOP single shot observations (1064-532s, black dots), 5 shots running mean (gray). The corresponding ICESat-2 and CALIPSO ground tracks are given as purple and red lines in Fig. 1(a). Please see text for details.
Figure 5 shows bathymetric maps projected onto a pixel grid of 0.02°×0.02° latitude and longitude bins over the studied region from CALIOP 532p-532s (a) and 1064-532s (b), ATLAS (c), and Landsat-8 (d). Note the 2-D CALIOP (Figs. 5(a) and (b)) and ATLAS (Fig. 5(c)) bathymetry maps are averaged seafloor depths within each pixel from available CALIOP measurements from June 2006 to November 2021, and ATLAS measurements from October 2018 to November 2021, respectively. The 2-D bathymetric map shown in Fig. 5(d) are from Landsat-8 images made on Sep. 1, Dec. 6, 13, 31, 2020 with a spatial resolution of 30 m. Figure 5(e) shows the absolute water depth differences between ATLAS and Landsat-8 with a 0.02°×0.02° grid resolution. The standard deviation (std) of the ATLAS water depths from October 2018 to November 2021 within each pixel is shown in Fig. 5(f), which can be used to study the seafloor dynamics in relation to spatial and temporal changes in water depths. Histograms of the water depth distributions and the water depth differences between different instruments are given in Fig. 6. Satellite bathymetries from CALIOP, ATLAS and Landsat-8 in Figs. 5 and 6 show significant changes over the entire GBB, with the water depths ranging ∼0 to ∼25 m, and the changes in GBB seafloor depths (Fig. 5(f)) during October 2018 ∼ November 2021 are mostly from −1 m to 1 m with a mean of ∼9 cm.
Fig. 5. Bathymetry maps obtained from (a) CALIOP’s two 532 nm channels; (b) CALIOP’s 532 nm perpendicular and 1064 nm channels; (c) ATLAS/ICESat-2 ATL03 photon heights; and (d) Landsat-8. The color bars show the water depths in meters. Panel (e) shows absolute water depth differences between Landsat-8 and ICESat-2 in meters. Panel (f) shows the standard deviation of the ICESat-2 water depths within each grid from Oct. 2018 to Nov. 2021.
Fig. 6. (a) Water depths distributions (associated to Fig. 5) over the GBB from different sources: CALIOP’s two 532 nm channels (blue), CALIOP’s 532 nm perpendicular and 1064 nm channels (red), ATLAS photon measurements (black), and Landsat-8 (green); (b) Water depths differences: ATLAS/ICESat-2 minus CALIOP (blue and red), and Landsat-8 minus ATLAS/ICESat-2 (green).
While ATLAS can deliver accurate and highly precise measurements of the heights of the ocean surface and the underlying seafloor in shallow waters, the spatial extent of the ATLAS measurements is extremely narrow compared with Landsat-8. Although ATLAS has high horizontal resolution (∼0.7 m) in its along-track direction it has no cross-track swath, so measurements are limited to its footprint diameter of ∼11 m. On the other hand, Landsat-8 has continuous 30 m pixel resolution spanning a 185 km wide swath. When coupled to the neural network in described in [7], Landsat-8 data can provide extensive, high resolution retrievals of seafloor topography over the entire area examined in this study within a few orbits, as shown in Fig. 5(d). Acquiring the same detailed imagery with ATLAS or CALIOP requires compositing data over several months or years of on-orbit measurements, and the bottom depths and habitats could vary during a long time period.
To evaluate the bathymetry results from different sources, Table 2 lists the statistical results, which indicate that the mean water depth differences between CALIOP and ATLAS are less than 0.5 m with a root mean square error (RMSE) < 2 m. Validation results of the CALIOP water depths show a mean bias less than 1.5 m and a RMSE < 2.3 m when compared to Landsat-8 retrieved water depths. The bias between CALIOP 532p-532s and 1064-532s is about −0.36 m with a RMSE of ∼1.14 m and the determination coefficient (R2) of ∼0.81. Please note that the ocean surface backscattered signal is ∼30 times larger than the subsurface backscattered signal with surface wind speed of ∼ 6 m/s [66]. This small subsurface signal could cause the ocean surface height obtained from CALIOP 532 nm parallel channel being a little bit lower (e.g., ∼0.36 m lower) than that from 1064 nm channel.
Table 2. Statistics of water depth differences from different sources: CALIOP/CALIPSO, ATLAS/ICESat-2, and Landsat-8. RMSE: Root Mean Square Error, R2: Coefficient of determination.
ATLAS water depths show a mean bias of 0.49 m and a RMSE of 1.84 m with the determination coefficient of ∼0.75 when compared to Landsat-8 water depths over studied regions. The water depth bias (∼0.49 m) between ATLAS and Landsat-8 results can be due to the tidal effects. In some places, shallow water bathymetry can change with time due to erosion and sediment transport (e.g., Fig. 5(f)), which results in the water depth differences between lidar retrieved results and Landsat-8 results. These results (Figs. 2–6) demonstrate that the space lidars: CALIOP/CALIPSO and ATLAS/ICESat-2 can effectively separate optically shallow water from deep water and provide accurate nearshore bathymetry along their orbit tracks as well.
4. Nearshore water and seafloor properties
Previous studies [10–17] indicate that open ocean optical properties can be obtained from CALIOP and ATLAS space-based lidars, and ATLAS measurements can also be used to quantify the vertical structures of phytoplankton optical properties below the ocean surface [15,16]. This section demonstrates that it is possible to derive the nearshore water and seafloor properties from the CALIOP and ATLAS measurements.
As shown by the photon clouds in Fig. 1(c), the accurately separation of seafloor photons from ocean surface photons provides a way to estimate the seafloor attenuated backscatter, as indicated in Eqs. (4) and (5). Figure 7(a) shows the photon ratios between seafloor and water surface backscattered photons, where the atmospheric contributions (two-way transmittance) are easily and accurately removed by these photon ratios because the laser light passed through the same atmospheric column when backscattered from the ocean surface and then the seafloor. The ATLAS retrieved seafloor attenuated backscatter (Rrsb, sr−1) given by Eq. (4) is shown in Fig. 7(b), where Rtheory in Eq. (5) is determined using MERRA-2 10-m wind speeds. For comparison, CALIOP layer-integrated attenuated backscatter (IABs, sr−1) from the 532 nm perpendicular channel and MODIS remote sensing reflectance (Rrs, sr−1) at 531 nm are shown in Fig. 7(c) and (d), respectively. Note that ATLAS Rrsb is a function of the seafloor reflectance and the two-way transmittance of the water column between the water surface and the ocean bottom (Eq. (6)), while the CALIOP IABs and MODIS Rrs are integrated values that also include contributions from water column absorption and scattering along the path from ocean surface to seafloor. The CALIOP IABs has no atmospheric effects because ocean cases with clear sky conditions are selected.
Fig. 7. (a) ATLAS measured photon counts ratios between seafloor and water surface to remove atmospheric two-way transmittance; (b) ICESat-2 retrieved seafloor attenuated backscatter at 532 nm, Rrsb (sr−1); (c) CALIOP integrated attenuated backscatter (IABs, sr−1) from 532 nm perpendicular channel; (d) MODIS Remote sensing reflectance at 531 nm, Rrs (sr−1); (e) ATLAS retrieved seafloor reflectance, Rb; (f) MODIS diffuse attenuation coefficient scaled at 532 nm.
Although the CALIOP IABs and MODIS Rrs have contributions from the water column that cannot be separated from seafloor contributions, the water column contribution can be estimated from ATLAS measured photons. For example, in the ATLAS measurements shown in Fig. 1(c), the ratio between ocean surface photon counts and the photon counts backscattered from within the water column is on the order of 4% or less. As shown in Fig. 7(a), the ratios between seafloor and water surface photon counts are typically greater than 20% and can exceed 100% for some shallow waters (e.g., red color regions in Fig. 7(a)) depending on water depths and seafloor types (e.g., sand or vegetation). In addition, Fig. 7(c) and 7(d) show the CALIOP IABs and MODIS Rrs are ∼2.7 × 10−4 sr−1 and ∼2.5 × 10−3 sr−1 over deep oceans and more than 8 × 10−3 sr−1 and 2 × 10−2 sr−1 for the shallow waters. These large values of IABs and Rrs are due to the seafloor contributions. Figure 7(e) shows the ATLAS retrieved bottom reflectance (Rb) by Eq. (6) with annual mean of MODIS diffuse attenuation coefficient shown in Fig. 7(f). The ATLAS retrieved bottom reflectance at 532 nm after removing the distorting influence of the water column is useful for benthic substrates classification, such as sand and seagrass which are the two dominated substrates in GBB [67]. The bottom reflectance spectra can be obtained by combining space lidar measurements with passive multiband images, which is useful for quantitative mapping of shallow water habitats.
Unlike passive ocean color records, which are limited to daytime observations only, the lidar retrieved results provide both daytime and nighttime information about the nearshore water and seafloor properties. For example, the day and night distributions of CALIOP depolarization ratio (a, b), IABs (c, d) and ICESat-2 seafloor attenuated backscatter Rrsb (e, f) are shown in Fig. 8. The lidar retrieved day-night differences can be used to study the diurnal cycles of the coastal ecosystems, which cannot be assessed using the existing ocean color record generated from passive remote sensing measurements. The combinations of bathymetry (e.g., Fig. 5), water and seafloor properties (e.g., Figs. 7 and 8) are useful to map and monitor bottom types such as sand, coral reefs, sea grass beds, kelp forests, which have different reflectance.
Fig. 8. Day-Night distributions of CALIOP depolarization ratio (a, b) and IABs (c, d) at 532 nm and ICESat-2 retrieved sea floor attenuated backscatter at 532 nm, Rrsb (sr−1) (e, f).
5. Summary
The main objective of this study is to further demonstrate the potential of space-borne lidars for shallow water remote sensing. The bottom depths of optically shallow water obtained from CALIOP are compared to those obtained using ATLAS and Landsat-8 bathymetry products. The ATLAS and CALIOP retrieved bathymetry performs excellently in estimating the depth of optically shallow waters. In particular, ATLAS provides accurate measurements of reef edges due to its high vertical and horizontal resolutions along its ground tracks. Although the space lidars effectively have a zero swath width when compared with passive remote sensing sensors (e.g., Landsat-8), the lidar retrieved bathymetry results can be used as input into the SDB algorithms. Our results demonstrate that the ATLAS/ICESat-2 and CALIOP/CALIPSO can provide important information about nearshore water bathymetry and seafloor properties during both day and night which can be used to complement and/or validate the passive ocean color records such as MODIS and the upcoming Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.
Funding
NASA Headquarters (80NSSC20K0129, 80NSSC21K0910).
Acknowledgments
The authors would like to thank Tom Neumann for his help on ICESat-2/ATLAS after pulses issues, and Prof. Zhongping Lee’s team from University of Massachusetts Boston for the help on Landsat-8 bathymetry results. The authors also extend heartfelt thanks to the NASA CALIPSO, ICESat-2, MODIS ocean color and Landsat-8 teams for providing the data used in this study.
Disclosures
The authors declare no conflicts of interest.
Data availability
Data underlying the results presented in this paper are available in Ref. [44–47,53,58,62].
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