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.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) that carries the Advanced Topographic Laser Altimeter System (ATLAS) photon-counting LiDAR (PCL) provides an astonishing opportunity for Earth observations. The precise identification of PCL signal photons is the prerequisite for ICESat-2 data applications in Earth observations. However, due to the influence of variable topography and vegetation structures, denoising PCL photons remains a significant challenge, particularly in mountainous forest areas. The overall goal of this study is to develop an adaptive photon denoising method tailored for mountainous forest areas. Focusing on study areas with Tahoe National Forest and two simulated datasets, the specific goals are to: 1) setting the neighborhood size based on elevation frequency histogram (EFH) and wavelet transform criterion, which considers the effects of noise and vegetation structure on signal photons; 2) setting the neighborhood direction using detrend operation, which could fit the terrain curve more accurately; and 3) setting the discrimination threshold according to the constraints of the signal and noise rates, which could identify noise photons with complex vegetation structures. The results from ICESat-2 data demonstrate that the terrain elevation and relative height of 95% (RH95) derived using the proposed method exhibit superior performance, with the coefficient of determinations (CODs) of 1.00 and 0.62, respectively.

The extraction of canopy heights from spaceborne lidar received waveforms over mountainous areas is a challenging task, as the waveform signal broadening and overlapping effects make the vegetation and ground returns difficult to identify. This study aims to obtain the deconvoluted waveform from the received waveform using a Richardson–Lucy deconvolution algorithm for better separation of the vegetation and ground returns from overlapped waveform components. Specifically, the canopy top elevation is extracted from the start bin of the deconvoluted waveform and the ground elevation is calculated as the elevation centroid of the ground return, which is determined by shuttle radar topography mission (SRTM) data and deconvoluted waveform. The canopy height is the difference between the canopy top and the ground elevations. Eight track data of the Global Ecosystem Dynamics Investigation (GEDI) lidar over the rugged Larkspur Mountain of Colorado are employed to validate the proposed method. The results demonstrate that the mean error (ME), mean absolute error (MAE), and root-mean-square (rms) error of derived canopy heights over rugged terrain with surface slopes more than 20° are greatly reduced by 86.7%, 23.2%, and 19.5% relative to GEDI canopy height products, respectively. The results prove that the proposed method is more applicable to derive canopy heights over rugged mountainous areas.

The canopy cover is a fundamental parameter in forest inventory. The launch of Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) that carries the Advanced Topographic Laser Altimeter System (ATLAS) photon-counting lidar provides an astonishing opportunity to assess canopy covers at a large scale. Currently, the canopy covers were calculated as the proportion of vegetation photons to total signal photons using ICESat-2/ATLAS data without considering the radiometric distortion caused by photon-counting detectors and the surface reflectance of vegetation and ground. The overall goal of this study is to investigate a method to derive more accurate canopy covers considering the radiometric correction and surface reflectance correction with ICESat-2 photon data. With focusing on two study areas, Slaughter (SLAU) and Lenoir Landing (LENO) in USA, the specific purposes are to: 1) propose a radiometric correction model based on the lidar equation and response mechanism of photon-counting detectors to recover accurate vegetation and ground photons; 2) estimate the reflectance ratio between vegetation and ground (RVG) according to the vegetation radiative transfer model and the density of spatial cluster method; 3) derive original and compensated canopy covers with ICESat-2 classified photons; 4) evaluate the accuracy of derived canopy covers relative to local airborne reference canopy covers; and 5) explore the effects of undergrowth vegetation and land cover types on the canopy covers. The coefficients of correlation (${R}$) and root-mean-square errors (RMSEs) of the compensated canopy covers are 0.86 and 0.15 at SLAU and 0.59 and 0.16 at LENO, compared with those for original canopy covers with 0.71 and 0.18, and 0.45 and 0.21, respectively. As the undergrowth vegetation and diverse land cover types have an impact on the retrieval accuracy of canopy covers, we can employ the photons of different species to obtain their specific reflectance ratios to achieve a higher precision.

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.

The signal strength is a fundamental parameter in radiometric applications for satellite lidars. Different from full-waveform lidars, photon-counting lidars cannot record the returned signal strength but only respond to the presence of the photon event and may miss some returned photons due to the dead time effect, i.e., introduce radiometric distortion. Based on the lidar equation and the response mechanism of photon-counting detectors, we propose a radiometric correction model to remove the impact of the nonlinear response and dead time of detectors for the photon-counting lidar borne on ICESat-2. The returned signal photon number is corrected by the proposed model with respect to the photon event number per shot (PNPS) and surface slope derived from ATL03/ATL08 products. Then, the optical throughput calibration factor of ICESat-2 is obtained from ATL06 products over high Antarctic plateau where has given reflectance and clear atmosphere, which is generally equal to 0.52. The atmospheric attenuation induced by the molecular, cloud, and aerosol is calculated from ATL09 products. In addition, the corrected radiometric parameters including the calculated surface reflectance and apparent surface reflectance (ASR) are applied to classify land cover types along laser tracks over snow-covered terrains. The results indicate that the signal strength and calibration constant are reliable after corrections, but the atmospheric attenuation is sometimes inaccurate, which further influences the derived surface reflectance. In classifications, the overall accuracy and Kappa coefficient based on the corrected ASR can achieve the best classification results with 88.80% and 0.69. The proposed radiometric correction model is very essential to radiometric applications for photon-counting lidars such as ICESat-2, especially for data captured on ice and bare land with relatively high reflectance.

Detailed information on lake bathymetry is essential for both hydrology-related studies and water resource management. Conventionally, lake bathymetry was mapped using high-cost approaches (e.g., ship/boat-based multibeam echosounders or airborne bathymetric lidars). With only satellite remotely sensed data sets, a method for deriving high-resolution bathymetry for dynamic areas was proposed by combining the new Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) lidar data and the Landsat-based Global Surface Water Data Set (GSWD). First, ICESat-2 can provide accurate along-track topographic points after the point cloud processing and bathymetric error correction, and the GSWD can supply water occurrence information within the lake dynamic area between 1984 and 2018. Second, using the derived relationship between the elevation and water occurrence, the bathymetric map of Lake Mead, USA, was produced with the dynamic area exceeding 235 km2, elevation ranging nearly 37 m, and a resolution of 30 m. The local reference data (i.e., the airborne topographic lidar data and ship/boat-based bathymetric data) in six areas around Lake Mead were used for the validations. In general, the produced lake bathymetry achieved an accuracy of approximately 2 m in elevation with$R^{2}$of 0.97. The proposed method is promising to obtain global bathymetry for inland water bodies (e.g., the lake and reservoir) and coastal areas (e.g., the tidal zone) where water level fluctuations are strong and the water clarity is sufficient.

Full-waveform satellite LiDAR can be used to retrieve the terrestrial surface information by decomposing its received waveforms. However, it is challenging to accurately extract the parameters of each component from a non-Gaussian overlapped waveform, which happens in steep mountain or urban areas. Therefore, a synthetic algorithm with the boosted Richardson–Lucy (RL) deconvolution, layered extraction, and gradient descent is proposed to implement the skew-normal decomposition for the received waveforms. To validate the performance of the proposed algorithm, we developed waveform decomposition experiments for three types of data, including known-parameter waveforms, simulated waveforms, and Global Ecosystem Dynamics Investigation (GEDI) satellite LiDAR waveforms. Meanwhile, we figured out the evaluation metrics involving correlation coefficients (CCs); root mean square errors (RMSEs); extracted parameter errors; and successful, missing, and unwanted rates for the decomposed waveforms. Through comparing the decomposed results of the proposed algorithm and the classical direct Gaussian decomposition (DGD) algorithms, we discovered that 1) the average CC has a growth of 4% and the average RMSE has a reduction of 60%; 2) the average errors of extracted amplitude, peak position, and pulsewidth have mitigated with 3.9%, 2.2%, and 5.1%, respectively; and 3) the successful detection rate increases by 40% and the unwanted and the missing rate decrease by 5% and 35% for the 2000 groups of known-parameter waveforms. In addition, the average CCs have slight growth of 3% and 1.2%, and the average RMSEs have significant reductions of 43% and 49% for the simulated and GEDI LiDAR waveforms, respectively. This research provides a preferable waveform decomposing approach conductive to characterizing the terrestrial information from the overlapping skew-normal full waveforms.

Hyperspectral LiDAR (HSL) possesses the advantages of the LiDAR and the hyperspectral detection, and detects ranging and spectrum information synchronously, by one HSL system. The data fusion is also avoided. At present, the spectrum range of reported HSLs usually covers only 500 nm-1000 nm (from visual (VIS) to near infrared (NIR) band). However, there is requirement to extend the spectrum range to short wave infrared (SWIR) band, which often contains more useful spectral information. In this paper, a HSL covering the spectrum from VIS to SWIR is reported. In the HSL, the echoes are divided into two sections and are detected by the different optoelectronic devices, of which the spectral response ranges are respectively compatible to the corresponding echoes. The HSL detection experiment in the laboratory was carried out. The waveforms of the echoes were analyzed, and the spectra of different targets were measured by the HSL. The experiment results demonstrate the capability of the prototyped HSL that obtaining the ranging information and the spectrum information of the targets in VIS-SWIR bands synchronously.

Recently, a major effort has been made to develop methods or tools for rock characterization and mineral content mapping. Light detection and ranging (LiDAR) is an efficient active remote sensing technique for collecting geometry information about rock surfaces. However, traditional LiDAR sensors work with a single-wavelength laser source, and it is unfeasible to obtain spectral information using one LiDAR sensor. The combination of hyperspectral imaging and LiDAR techniques is an emerging method for acquiring spatial and spectral information simultaneously that allows remote mapping of high-resolution mineral content and distributions and identifies subtle chemical variations. Unfortunately, spatial and spectral data registration, which introduces additional complicated data processing, is an inevitable and essential issue for this method. In this letter, first, we investigate the feasibility of ore classification applications with hyperspectral LiDAR (HSL). HSL consists of 17 spectral channels covering the visible–shortwave infrared (SWIR) spectral range. Spatial and spectral information about seven different ore samples is obtained under a controlled laboratory environment using HSL. The standard deviation of the distance measurements is less than 1.1 cm for different spectral channels, and the classification accuracy can reach 100% if all 17 spectral measurements are used. To optimize the system design with lower cost and system complexity, a spectral band selection criterion is built based on the feature contribution degree (FCD), which is calculated using the normalized variance of the reflectance values for different ore samples at each wavelength. Two different strategies of FCD selection are tested to generate vectors: ascending sequences and descending sequences. Feature vectors with descending sequences have better classification accuracy. In addition, the results show that the classification accuracy can reach 100% with the feature vector of the seven largest FCD values compared to 59.57% for the feature vector with the seven smallest FCD values. Moreover, we find that the channels with high FCD values are primarily centered in SWIR bands. This result could be a reference for optimizing the hardware design of HSL for ore classification or mineral identification.

This paper presents the estimation of the ground elevation and canopy top elevation from the data collected by an airborne frequency-modulated continuous waveform profiling radar, Tomoradar. The estimated ground and canopy top elevations are critical for the derivation of reference information for the satellite-borne microwave radar data and the modeling of interaction between microwave radar signal and foliage. The methods of estimating the ground elevation and canopy top elevation from profiling radar are introduced, and the accuracy was evaluated via digital terrain model and Velodyne VLP-16 LiDAR integrated with the Tomoradar. To our knowledge, the ranging radar and the LiDAR data were simultaneously collected for the first time. The evaluation proved that the root-mean-square error (RMSE) of ground level estimation of the developed profiling radar can reach up to 0.33 m. When comparing the estimated canopy top peak elevation between the profile radar data and the LiDAR data, it was found that the side lobes of Tomoradar antenna system may produce undesired canopy backscatters when the size of the canopy gap is comparable to the footprint size of the main lobe, resulting in a higher canopy top elevation measurement from Tomoradar than that from LiDAR. The RMSE of the estimated canopy top peak elevation between two data sets was 0.32 m in the best case and 0.852 m on average. Moreover, the RMSE of point-to-point comparing the entire canopy tops elevation estimated from the data of two active remote sensing systems is 0.799 m after excluding the outliers.