The diffuse attenuation coefficient$K_{d}$continues to play a crucial role in oceanographic research works. Recently, Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has shown its great ability to estimate$K_{d}$using the water column decay profiles. However, the weak water column backscattered signals are vulnerable to afterpulses and solar background noise, making this way perform not well in the daytime and in nearshore areas. In this study, a method to estimate$K_{d}$is proposed which innovatively uses ICESat-2 bathymetric signal intensities. The main principle is to calculate the attenuation in water column transmission by bathymetric lidar equations. Since the seafloor signal level is much stronger than that of the water column, a significant advantage is the greater noise immunity, i.e., the ability to operate under strong background noise and afterpulses interference. The performance is validated against the moderate-resolution imaging spectroradiometer (MODIS) ocean color measurements with mean relative differences (MRDs) of <32% using both daytime and nighttime ICESat-2 data in six sea and large lake nearshore areas. Based on the new generation of spaceborne lidar data, this study explores a new path to monitor water qualities in nearshore areas. This method is applicable where seafloor photons exist in both daytime and nighttime.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) carries the new generation spaceborne photon-counting lidar, Advanced Topographic Laser Altimeter System (ATLAS). ICESat-2/ATLAS has an excellent performance for obtaining precise geometric surface profiles of land and oceans, by which the surface parameters such as the significant wave height (SWH) over oceans can be further obtained. As the strong beams have better data quality, they are currently used to obtain the sea surface parameters. The weak beams could double the spatial coverage area if they can also be successfully used. However, this potential is constrained by the lower signal-to-noise ratio (SNR) of weak beams. To exploit the performance of weak beams, this study proposes a method to extract sea surface signal photons, which are further accumulated to calculate the SWHs. This study explores the effect of the data processing window length on the result of the denoising algorithm and how many sea surface signal photons should be accumulated to estimate the reliable SWHs with ICESat-2 weak beams. The calculated SWH shows good agreement with ECMWF reanalysis 5 (ERA5) data, with the root mean square error (RMSE) under 0.3 m. The method proposed in this study enables the acquisition of SWH values in certain regions where no ATL12 official data are available.

The recent capabilities of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) photon counting lidar in monitoring water levels have been demonstrated through its precise elevation measurement and small footprint. The accuracy in water level measurements is, however, significantly impacted by the first photon bias, especially when photon-counting detectors are fully saturated due to particular reflections from calm water surfaces. In this study, we propose an analytical model to correct the first photon bias in scenarios where the ICESat-2/Advanced Topographic Altimeter System (ATLAS) is fully saturated. Notably, the model innovatively recovers and estimates the required signal level using after-pulses, which are typically considered as noise. These after-pulses can be used to effectively estimate the signal level when the detector is fully saturated. The experiment analysis, conducted on eight ICESat-2 ground tracks over calm water surfaces near the Great Lakes and the Tibetan Plateau, indicates that the actual received signal photons can surpass 200 and in some cases, reach up to 600 counts for strong beams, introducing a first photon bias exceeding 15 cm. The findings prove that 1) after-pulses can be used to retrieve water surface elevation and reflectance when the primary surface return is distorted by detector saturation and 2) calm waters reflect 5–40 times more than ice and snow surfaces, where first photon bias is a predominant error in water level measurements. The method holds great significance for the accurate monitoring of water levels in small inland water bodies using ICESat-2 and may also inform the design of lidar systems for inland water observations.

Photon-counting laser altimeter is an advanced remote sensing observation equipment, which provides detailed surface profile information, exemplified by the advanced topographic laser altimeter system (ATLAS) on Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). However, the high sensitivity of a photon-counting laser altimeter introduces noisy geolocated photons, posing a tremendous challenge in signal extraction from noise photons with low signal-to-noise ratios (SNRs). An efficient signal extraction algorithm is critical for further applications of ICESat-2 data, and the spatial density-based algorithms perform well and have been verified in various scenarios, e.g., canopy and ground detection, sea-ice freeboard detection, and bathymetry. Currently, the geometric parameters in density-based algorithms are usually empirically determined, and the main challenge is to adaptively set the optimal geometric parameters in variable scenarios. In this study, a theoretical mapping model that correlates the performance metrics (e.g., number of true positive, false positive, and false negative photons) with the algorithm parameters and the lidar system parameters is derived. The performance of this model is verified using Monte Carlo simulated data of bare lands and vegetated areas with$R^{2}$exceeding 0.99, and also verified using ICESat-2 data over land, ocean, vegetation, and ice areas with$R^{2}$exceeding 0.94. Based on the model, the signal extraction capacity in different SNRs, channel numbers, and signal durations are discussed, offering a theoretical foundation for determining the optimal parameters to extract ICESat-2 signal photons and also for better designing hardware parameters of photon-counting laser altimeters.

Atmospheric turbulence is a significant factor that affects the radiometry of spaceborne laser pulses. Depending on the meteorological data from the National Centers for Environmental Prediction (NCEP) dataset and phase screens simulated from fractal interpolation method, the optical fields of a transmitted laser pulse propagating through the turbulence are modeled in this study. By calculating the ratio of the received energy with and without turbulence, an energy index is introduced to quantitatively evaluate the turbulence impact. Taking the ICESat-2 Lidar as an example, the distributions of the energy index at three simulated areas with weak, moderate, and strong turbulence are investigated. The results indicate that the means of energy index reaches 0.90 for moderate and weak turbulences, which corresponds to 10% laser energy loss, but would decrease to 0.70 for strong turbulence corresponding to 30% laser energy loss. It implies that the strong turbulence impact should be compensated for the radiometric correction of laser pulses. In addition, the proposed method is validated by comparing the energy index and the atmospheric transmittance derived from the ATL09 data over three areas with different surface types. The mean absolute percentage errors (MAPEs) are below 5% and the root mean square errors (RMSEs) are less than 0.05, which proves that our proposed method is effective for simulating the influence of atmospheric turbulence on the radiometry of spaceborne laser pulses.

Recently, an increasing number of studies have progressively explored the radiometric properties of ICE, Cloud, and land Elevation Satellite-2 (ICESat-2), enabling this satellite and its payload to provide support for not only geometric but also radiometric applications. However, due to potential changes in the optical throughput and electronics of the instrument during flight, the essential radiometric corrections are needed for quantitative radiometric applications. In this study, using ICESat-2 signal and noise data on icesheet and desert surfaces, which have a relatively stable reflectance and clear sky, a radiometric correction method is proposed to describe the changes in instrument parameters, i.e., to estimate how much the signal and noise should be exactly scaled. With instrument and environmental parameters provided by the ICESat-2 ATL04/ATL09 product, signal and noise models are used to calculate the theoretical signals and noise levels. The theoretical predictions are then compared with the actual measured signals and noise results by ICESat-2 to obtain the scale factors (or radiometric correction factors) for signal and noise, respectively. The results indicate that all three photon counting electronics (PCEs) (corresponding to three laser pairs) exhibit very close scale factor values (i.e., ~1.9), i.e., ICESat-2 receives both signal and noise nearly double the expected values. The noise scale factors$F_{\text {noise}}$over icesheet are expected to yield the most accurate scale factors because$F_{\text {noise}}$will change very little with and without layers. We also analyze the annual average radiometric drift of ICESat-2, which indicates the decreases of ~2.5% ($F_{\text {signal}}$) and ~2.2% ($F_{\text {noise}}$) from 2019 to 2022. This study demonstrates the consistency of signal and noise in radiation and also helps to understand the radiation closure and data fusion between the passive background noise and active laser signal for a spaceborne lidar.

Recently, spaceborne active lidars relying on backscattered laser signal can observe thin clouds, but the laser beam cannot penetrate clouds with large optical thickness. The new generation photon-counting lidar on Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), whose noise can be treated as observations of a green band camera, provides an excellent opportunity to fuse active and passive information to retrieve cloud optical thickness (COT). Clouds significantly increase the background noise and sharply attenuate the signal returning from oceans, which makes it feasible to observe thin and thick clouds by combining active and passive information of ICESat-2. In this study, we first derive the passive background noise and active signal models over oceans for spaceborne lidars, which considers medium contributions from clouds, aerosols, air molecules, ocean surface, and subsurface. ICESat-2 measured surface signals and noise rates in open oceans of Western Pacific are used to verify the models with auxiliary environment datasets. The results indicate that the theoretical predictions have the mean absolute error (MAE) of less than 0.31 counts for signal and the mean absolute percentage error (MAPE) of less than 35% for noise. Then, based on these theoretical models, we propose a COT estimation method combining ICESat-2 active signal and passive noise data without extra auxiliary datasets, and the MAEs between ICESat-2 retrieved COTs and Himawari-8 (H8) cloud products are less than 1.2 (COTs range from 0 to exceeding 20) over open oceans. In general, the proposed method not only expands the observation range of retrieved COTs compared to methods solely relying on signal or noise data but also has great significance for assessing the availability of lidar surface signal, i.e., producing cloud mask.

ICESat-2/ATLAS can obtain nearly continuous profiles of ground targets. At present, fusing with other satellite-based data sources such as images is an important usage for ICESat-2 data. Some scenarios such as urban areas and applications such as stereo photogrammetry require registration features with a high geolocation accuracy when fusing. The building boundaries would be very appropriate features, as they not only have obvious geometric features in ICESat-2 data, but also has textural features which are distinct in images. However, in ICESat-2 geolocated photons, the building boundary normally expands than its actual boundary. This non-negligible blurring phenomenon of geometric boundaries is caused by the extended laser spot of ICESat-2. This study theoretically and practically proposes a solution for these blurred building boundaries in ICESat-2 data. We first derive a theoretical model to describe the spatial convolution of laser spots (or called the extended laser spot effect) on building boundaries and then propose a method to estimate the exact building boundary points from for ICESat-2 data. The signal model and boundary point correcting method are validated using typical 23 targets in four tracks of ICESat-2 in Hutt City, New Zealand, where local airborne lidar points with high density and accuracy are available. After correcting the horizontal offset of ICESat-2, the horizontal accuracy of the determined building boundary locations has an RMSE of ~1 m, which is much better than that directly obtained by the signal photons from the ATL03 and ATL08 product (with RMSEs of ~6m). The results indicate this study can provide significant feature points for accurate registration and fusion between ICESat-2 data and other data sources in urban areas.

The new generation photon-counting lidar on Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) can obtain the subsurface optical properties of sea waters. Recent studies highlight the applications of deriving the lidar attenuation coefficient$K_{\mathrm {lidar}}$and then substituting$K_{\mathrm {lidar}}$into the bio-optical model to obtain more information of sea waters. As commonly used bio-optical models are built for the diffuse attenuation coefficient$K_{d}$that are traditionally derived by passive ocean color sensors, whether$K_{\mathrm {lidar}}$derived from ICESat-2 can be directly used as$K_{d}$is a fundamental question. Given that$K_{d}$is an apparent optical property (AOP) in the water column rather than an inherent optical property (IOP),$K_{d}$is closely related to the zenith angle of the incident light. The zenith angle of the incident light of the sunlight is normally tens of degrees for ocean color sensors, while the maximum laser off-nadir angle is ~1.5° for the ICESat-2 lidar. To demonstrate this issue, we select hundreds of ground tracks of ICESat-2 in both open ocean and coastal sea waters and compare the derived$K_{\mathrm {lidar}}$with their corresponding Moderate Resolution Imaging Spectroradiometer (MODIS)-derived$K_{d}$. The results indicate that the corrected results of$1.2\times K_{\mathrm {lidar}}$, instead of the direct results of$K_{\mathrm {lidar}}$, are more consistent with MODIS$K_{d}$. This study is of great significance to the better fusion of active lidar data and passive optical data in ocean observations.

The passive multispectral imaging and active bathymetric lidar make a great achievement for bathymetry in optically shallow waters. However, due to the attenuation of the water column precludes deep penetration of the light, accurately obtaining the underwater topography in turbid waters through remote sensing techniques, both passive and active, is still a challenging task. Airborne lidars can obtain water surface topography with high accuracy and resolution, which can further be used to derive the water depth based on wave theory. In this study, an ‘indirect’ method to determine water depth is proposed using airborne lidar measured water surface points. As the wavelength and wave direction can be accurately tracked from the water surface topography, a 20m×20m underwater topography near Ganquan Island, South China Sea, is generated with an RMSE of 0.91 m and a MAPE of 7.1%. The basic theory of deriving water depths is totally different from airborne lidar bathymetry, i.e., this method is independent of water clarity and can be used in turbid waters or even works with near infrared airborne lidar that can only obtain water surface points.

Over the past few decades, spaceborne passive ocean color sensors that measure the solar radiance have provided an unprecedented source of scientific knowledge on marine biology. Recently, spaceborne active lidars that measure the backscattered laser signal from the ocean subsurface provide new insights in deriving vertical profiles of ocean subsurface and obtaining shallow water bathymetry. The solar radiation is the signal source of passive ocean color sensors but acts as the primary noise source of satellite-based lidars, which may limit the extraction and application of the weak subaqueous signal in the daytime. Based on the perspective of the reciprocity, the background noise of the ICESat-2 spaceborne photon-counting lidar in six channels (or pixels) has potential to be regarded as the signal of an “ocean color camera” with a very narrow band. In this study, the remote sensing reflectance, that is the fundamental data of ocean color sensors, is theoretically linked to and accurately transferred from ICESat-2 noise data with an average Mean Absolute Percentage Error (MAPE) of less than 20% compared to thein-situmeasurements. With this method, not only the remote sensing reflectanceRrscan be retrieved from ICESat-2 under strong background noise, which enhances the capability of ICESat-2 to monitor the diurnal variation, but also numerous quantitative applications by passive remote sensing sensors may be achievable by the noise data of spaceborne photon-counting lidars in the future. In addition, a spaceborne lidar can synchronously detect active laser signal and passive solar radiation, which may bring new insights in the data fusion and verification of active and passive techniques.