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 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.