Lidar Research Articles

Intraocular Pressure Lowering Associated With Worsening Optic Pit-Like Retinoschisis.

In this article, we present a case of optic pit-like macular retinoschisis in the absence of advanced glaucomatous cupping. Intraocular pressure (IOP)-lowering therapy, which was started due to an early concern for glaucoma, caused a worsening of the retinoschisis, which subsequently resolved on discontinuation of the IOP-lowering therapy. Lower IOP likely triggered intraretinal fluid accumulation by facilitating a translaminar gradient from the subarachnoid to intraretinal space. [Ophthalmic Surg Lasers Imaging Retina 2024;55:100-102.].

Retinal Oxygenation With Conventional 100-ms Versus Short-Pulse Pan-Retinal Laser Photocoagulation.

Conventional (100 ms) pan-retinal photocoagulation (PRP) laser burns are larger than short-pulse (10 ms to 20 ms) PRP burns. This study investigates the effect of PRP burns of different sizes on retinal oxygenation. A mathematical model using COMSOL Multiphysics 6 was used to create a three-dimensional abstraction of the coupled biology of the choroid, photoreceptor, and retinal tissues. Laser burn sizes were varied in the model, specifically considering burn diameters of 500 μm, 250 μm, and 125 μm, while keeping the total burn area constant. Total increase in retinal oxygenation was the same for different burn sizes, but the oxygen distribution differed. Smaller burns resulted in a more even lateral oxygen distribution but with reduced penetration into the inner retina. Conventional and short-pulse PRP may affect retinal oxygenation differently, even when total burn area is the same. Further investigation into optimum burn size and pattern is required. [Ophthalmic Surg Lasers Imaging Retina 2024;55:40-45.].

Racial Differences in Choroidal Vascularity Index in Healthy Patients: Novel Insights.

Choroidal vascularity index (CVI) measures the ratio of blood vessels in the choroid to the total choroidal area. We aimed to compare CVI between young Black and White patients without a history of ocular or systemic disease. We used a previously validated algorithm for shadow compensation and choroidal vessel binarization to measure CVI across the Early Treatment of Diabetic Retinopathy Study grid. Black patients had a lower CVI (ß = -0.05, P < 0.001) compared to White patients. Choroidal volume or luminal volume did not significantly differ with respect to race, whereas there was a trend for Black patients to have a greater stromal volume (ß = 3.08, P = 0.01). Black patients have a lower CVI than do White patients, likely due to a greater proportion of stromal volume. Further study of this parameter is warranted to validate the findings of this exploratory study. [Ophthalmic Surg Lasers Imaging Retina 2024;55:30-38.].

Advanced 3D Navigation System for AGV in Complex Smart Factory Environments

The advancement of Industry 4.0 has significantly propelled the widespread application of automated guided vehicle (AGV) systems within smart factories. As the structural diversity and complexity of smart factories escalate, the conventional two-dimensional plan-based navigation systems with fixed routes have become inadequate. Addressing this challenge, we devised a novel mobile robot navigation system encompassing foundational control, map construction positioning, and autonomous navigation functionalities. Initially, employing point cloud matching algorithms facilitated the construction of a three-dimensional point cloud map within indoor environments, subsequently converted into a navigational two-dimensional grid map. Simultaneously, the utilization of a multi-threaded normal distribution transform (NDT) algorithm enabled precise robot localization in three-dimensional settings. Leveraging grid maps and the robot’s inherent localization data, the A* algorithm was utilized for global path planning. Moreover, building upon the global path, the timed elastic band (TEB) algorithm was employed to establish a kinematic model, crucial for local obstacle avoidance planning. This research substantiated its findings through simulated experiments and real vehicle deployments: Mobile robots scanned environmental data via laser radar and constructing point clouds and grid maps. This facilitated centimeter-level localization and successful circumvention of static obstacles, while simultaneously charting optimal paths to bypass dynamic hindrances. The devised navigation system demonstrated commendable autonomous navigation capabilities. Experimental evidence showcased satisfactory accuracy in practical applications, with positioning errors of 3.6 cm along the x-axis, 3.3 cm along the y-axis, and 4.3° in orientation. This innovation stands to substantially alleviate the low navigation precision and sluggishness encountered by AGV vehicles within intricate smart factory environments, promising a favorable prospect for practical applications.

Optical vortex coronagraphy for high-contrast imaging of femtosecond laser filaments

A high-contrast imaging technique for phase objects based on the optical vortex coronagraph (OVC) is proposed. This method offers the unique advantage of background-free imaging due to the introduction of azimuthal phase in the Fourier plane. We employed the OVC method to detect femtosecond laser-induced air plasma and compared it with the classic diffractometry and fluorescent imaging methods. We achieved a phase sensitivity of ∼0.035 waves that surpassed the capabilities of the other two methods. The combination of this highly sensitive imaging technique with the pump–probe method holds promise for applications in ultrafast imaging of laser–material interactions.

Leveraging national forestry data repositories to advocate wildfire modeling towards simulation-driven risk assessment

Modeling wildfire dynamics is complex and challenging due to the multiple scales involved in fire propagation, from physical–chemical processes to the interaction with topography and meteorological conditions. To provide reliable indicators of the risk of an ongoing wildfire, models aimed at informing policy-making should quantify the primary sources of uncertainty in their predictions. In this paper, we introduce a novel methodology built on top of Cellular Automata to assess the impact of uncertainty by implementing wildfire ensemble modeling using data from the Spanish National Forestry Data Repositories. Uncertainty is embedded in the model considering the ±2σ deviations from the medians of linear regressions of the canopy stratum with LiDAR metrics as explainable variables. The relevance of dynamic meteorological conditions in contrast to static environment conditions is analyzed. Our results suggest that an accurate account of the fuel model, including time-dependent wind and moisture maps, is mandatory to provide reliable predictions. Using a real case study (Concentaina’s extreme wildfire), we also illustrate the importance of assessing the impact of the firefighters’ mitigation efforts.

Photometric correction of images obtained from Chandrayaan-2 Imaging Infra-Red Spectrometer (IIRS) data

India’s second mission to the Moon, Chandrayaan-2, carried one of the most advanced imaging spectrometers ever sent to image a planetary surface – the IIRS (Imaging Infra-Red Spectrometer) – a 250 band spectrometer developed at the Space Applications Centre (SAC), ISRO, India. In this study, IIRS data between 800 and 2000 nm have been used to derive photometrically corrected reflectance from calibrated radiance data supplied by ISRO’s online archive (Pradan) of Chandrayaan-2 data. Additionally, the photometric properties of three IIRS bands (950, 1500, and 1700 nm) have been studied in detail. Since the images obtained by IIRS are at different viewing geometry orientations, the purpose of performing a photometric correction on IIRS images is to re-project each pixel to a standard viewing geometry of incidence angle, i = 30°, emission angle, e = 0° and, phase angle, α = 30°. In order to correct the lunar limb darkening effect, a combination of the Lommel-Seeliger function and McEwen’s model was used. In addition, topographic data from the merged Digital Elevation Model of Lunar Reconnaissance Orbiter – Lunar Orbiter Laser Altimeter (LRO-LOLA) and SELENE Terrain Camera (TC) were utilized to remove effects from local topography. A photometric function was then applied on four images captured by IIRS which were included in the first public release of data in August 2021. Radiance and reflectance (obtained after implementing photometric correction on IIRS images) values were compared with L1 and L2 M3 (Moon Mineralogy Mapper) data from Chandrayaan-1. It was found that although IIRS images are much darker than M3, corrected IIRS reflectance images had a much smaller variation in terms of reflectance values with M3. Moreover, the associated spectral reddening and reflectance values are within range when compared with data from other lunar missions. In addition, the reflectance spectra derived from the corrected IIRS images have been plotted for each image. However, the spectra exhibit significantly deeper absorptions than their corresponding M3 data for the same pixel. Phase functions derived from three bands have a good correlation with the reflectance values and phase angles. Consequently, this function can be applied to all IIRS images as and when more data are released to the public.

Systematic Evaluation of Multi-Resolution ICESat-2 Canopy Height Data: A Case Study of the Taranaki Region

Forest canopy height data are essential to assessing forest biomass and carbon storage, and they can be efficiently retrieved using the Advanced Terrain Laser Altimetry System (ATLAS) onboard ICESat-2. However, due to the sparse and discrete distribution of ATLAS footprints, the wall-to-wall mapping of forest canopy heights requires a combination of other ancillary data. In order to match the ATLAS data with ancillary data, or estimate higher-resolution canopy heights, the standard ATLAS canopy height products (ATL08), reported at a fixed step size of 100 m (12 m × 100 m), are typically divided into 30 m step sizes. There is little concern about the accuracy of the generated 30 m segment (12 m × 30 m) dataset. Furthermore, previous studies have primarily evaluated the along-track performance of the canopy height product, rather than the ability of ATLAS to provide an accurate representation of canopy heights at the pixel-wise level. In this study, we use airborne LiDAR data as references with which to evaluate the along-track accuracy of 30 m segment canopy height products and their accuracy in representing canopy height at the pixel-wise level. A framework method for spatially matching ATLAS canopy height estimate data with ancillary data accurately is also established in this study. This enables the use of ATLAS data to characterize pixel-wise canopy heights more precisely. The results show that the accuracy of the 30 m segment version dataset in representing the pixel-wise canopy heights (R2 = 0.38, RMSE = 8.37 m) is lower than its along-track accuracy (R2 = 0.44, RMSE = 7.63 m). Using only the nighttime data for the Taranaki region, the proposed method improves the estimation of pixel-wise forest canopy heights, with the R2 increasing from 0.49 to 0.59, the RMSE decreasing from 7.48 m to 5.51 m, and the %RMSE decreasing from 36.7% to 27.6%. This study contributes to understanding the accuracy of the ATLAS in reflecting pixel-wise canopy height and provides a new way of spatially matching ATLAS canopy height data with other remote sensing data.

A laser-ultrasonic based sensor fusion framework for height measurement during the stone crushing process

It is a challenging work to monitor the height of the material pile via laser measurement due to the influence of high-concentration dust in a moving crushing line. At present, although the error of using only ultrasonic radar is small, the monitoring range is limited, while using only laser radar to monitor materials is highly affected by dust environments and may fail in high dust environments. To overcome this limitation, a height measurement based on the laser-ultrasonic radar fusion method was proposed to monitor material height in mobile crushing line. Firstly, the proposed method used laser scanning to obtain the three-dimensional point cloud of the material surface through point cloud preprocessing and calculated the material height. Secondly, considering that the concentration of dust has a great impact on the monitoring of the material level height, the experimental data on the change of the material level height with the concentration of dust were obtained, and the monitoring error was fitted to improve the accuracy of the material level height detection. Finally, in order to further reduce the error, the monitoring data of the ultrasonic radar was integrated to reduce the influence of dust on the detection of the material level height. The results showed that the average error was reduced to 22.65 mm when the dust concentration was below 200 mg m−3, and the average error was reduced to 32.14 mm when the dust concentration was higher than 200 mg m−3. The experimental results in different dust concentration environments demonstrated that the proposed method can effectively detect the point cloud shape and material level height of the material in real applications, which effectively improves the accuracy of material height detection by combining the three-dimensional laser points of the laser radar and the penetrability of the ultrasonic radar in high-concentration dust.

Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements

Abstract. Cloud droplet number concentration (Nd) is crucial for understanding aerosol–cloud interactions (ACI) and associated radiative effects. We present evaluations of four ground-based Nd retrievals based on comprehensive datasets from the Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The Nd retrieval methods use ARM ENA observatory ground-based remote sensing observations from a micropulse lidar, Raman lidar, cloud radar, and the ARM NDROP (Droplet Number Concentration) value-added product (VAP), all of which also retrieve cloud effective radius (re). The retrievals are compared against aircraft measurements from the fast cloud droplet probe (FCDP) and the cloud and aerosol spectrometer (CAS) obtained from low-level marine boundary layer clouds on 12 flight days during summer and winter seasons. Additionally, the in situ measurements are used to validate the assumptions and characterizations used in the retrieval algorithms. Statistical comparisons of the probability distribution function (PDF) of the Nd and cloud re retrievals with aircraft measurements demonstrate that these retrievals align well with in situ measurements for overcast clouds, but they may substantially differ for broken clouds or clouds with low liquid water path (LWP). The retrievals are applied to 4 years of ground-based remote sensing measurements of overcast marine boundary layer clouds at the ARM ENA observatory to find that Nd (re) values exhibit seasonal variations, with higher (lower) values during the summer season and lower (higher) values during the winter season. The ensemble of various retrievals using different measurements and retrieval algorithms such as those in this paper can help to quantify Nd retrieval uncertainties and identify reliable Nd retrieval scenarios. Of the retrieval methods, we recommend using the micropulse lidar-based method. This method has good agreement with in situ measurements, less sensitivity to issues arising from precipitation and low cloud LWP and/or optical depth, and broad applicability by functioning for both daytime and nighttime conditions.

Building damage assessment using improved combined adjustment and automatic building corners extraction method

ABSTRACTAccurately estimating the collapse height of buildings during earthquakes holds great significance. Acquiring precise height information of buildings using dual-phase high-resolution stereo satellite images (HRSSIs) requires high-precision ground control points, which are difficult to obtain in earthquake-stricken areas. Spaceborne laser data has been validated to significantly improve the vertical accuracy of HRSSIs. Therefore, this paper first proposes the application of a combined stereo adjustment model in building collapse estimation and thoroughly verifies its feasibility. Firstly, the laser altimetry points (LAPs) acquired by GF-7 and ICESat-1 serve as elevation control points (ECPs), and the tie points from HRSSIs were used as constraint to conduct combined adjustment to improve the consistency of stereoscopic positioning accuracy between HRSSIs before and after the earthquake. Subsequently, a footprints-to-conners, coarse-to-fine, automatic building corners extraction method was performed. Avoiding the time-consuming generation of large-scale DSM while also improving the accuracy of building collapse assessment. Finally, according to the height change of building corners calculated from HRSSIs, building damage assessment was completed. According to the above methods, independent experiments were carried out respectively for the Oaxaca earthquake in Mexico in 2020 and the Kahramanmaras earthquake in Türkiye in 2023. GF-7 HRSSIs was used to verify the accuracy and stability of the proposed method for 3D assessment of building damage. The results indicate that the RMSE of elevation positioning accuracy has been greatly reduced in both regions, ranging from 10.29 m to 0.95 m and from 6.67 m to 0.99 m, respectively. This accuracy guarantees the feasibility of detecting the building floor-level collapse. And 2541 building corner points were extracted from two disaster areas, with correct corner points accounting for 85.18%. A total of 520 buildings were correctly extracted corner points, with an accuracy rate of 94.12% for assessing the degree of damage.

Uncertainties on the combined use of ICESat and ICESat-2 observations to monitor lake levels

Lake water level is an important variable to indicate lake hydrological balances and climate change impacts. Benefiting from the launch of the laser altimeters ICESat and ICESat-2, higher spatial-resolution elevation measurements have opened new possibilities for monitoring lake levels globally over the past two decades. However, uncertainties on the combined use of two-generation satellite laser measurements have not yet been investigated specifically. This study aimed to summarize the important technique notes on water level data processing by integrating the ICESat and ICESat-2 altimetry measurements. We mainly focused on the effect of geoid height, water masks for extracting altimetry footprints, and the 9-year data gap between the two generations of satellites on water level change estimates. We compared the influences of the above three factors in different situations by selecting typical lakes worldwide as study cases. The results showed that: (1) In the combination of ICESat and ICESat-2 products, geoid heights need to be recalculated for each footprint based on its longitude and latitude in order to replace the geoid values of the original products when calculating orthometric heights. It is necessary because the default geoids in both generations of products (ICESat and ICESat-2) exhibit a systematic deviation; (2) To balance the accuracy and efficiency, the small water mask in the low-level year is recommended to extract the potential footprints in comparison with the laborious processing of time-varying water masks; (3) The 9-year data gap between ICESat and ICESat-2 observations may cause inevitable overestimations or underestimations of the long-term change rate of lake levels with a non-linear trajectory, yet it has few effects on lakes with (near) linear trending or fluctuating changes.

Modeling Uncertainty of GEDI Clear-Sky Terrain Height Retrievals Using a Mixture Density Network

Early spaceborne laser altimetry mission development starts in pre-phase A design, where diverse ideas are evaluated against mission science requirements. A key challenge is predicting realistic instrument performance through forward modeling at an arbitrary spatial scale. Analytical evaluations compromise accuracy for speed, while radiative transfer modeling is not applicable at the global scale due to computational expense. Instead of predicting the arbitrary properties of a lidar measurement, we develop a baseline theory to predict only the distribution of uncertainty, specifically for the terrain elevation retrieval based on terrain slope and fractional canopy cover features through a deep neural network Gaussian mixture model, also known as a mixture density network (MDN). Training data were created from differencing geocorrected Global Ecosystem Dynamics Investigation (GEDI) L2B elevation measurements with 32 independent reference lidar datasets in the contiguous U.S. from the National Ecological Observatory Network. We trained the MDN and selected hyperparameters based on the regional distribution predictive capability. On average, the relative error of the equivalent standard deviation of the predicted regional distributions was 15.9%, with some anomalies in accuracy due to generalization and insufficient feature diversity and correlation. As an application, we predict the percent of elevation residuals of a GEDI-like lidar within a given mission threshold from 60°S to 78.25°N, which correlates to a qualitative understanding of prediction accuracy and instrument performance.

Application of Airborne Radar Variable Altitude Aerial Survey in High Precision Mapping of Mountainous Areas

This paper studies how to use UAV aerial survey equipment to carry laser radar and high-definition real-scene lenses to achieve high-precision mountain mapping. The study aims to solve the problems of complex terrain, difficulties in surveying, and controlling the accuracy of surveying and mapping in mountainous, canyon, and hilly areas. Firstly, the LK tea production area development project is selected as an example for example analysis. The aerial survey equipment and data acquisition equipment are selected according to the characteristics of the project. Secondly, aiming at the problem that the mountainous terrain undulates greatly and that the accuracy of ground object mapping is difficult to control, this paper puts forward the “variable height aerial survey” method. It collects aerial survey data according to the terrain characteristics. Finally, this paper proposes a “point cloud overlay” modeling method. It combines laser point cloud data with real 3D data to form a high-precision real 3D model point cloud reorganization and establish a high-precision real 3D model.

Bacillary Layer Detachment as a Presenting Feature of Ocular Sarcoidosis.

Bacillary layer detachment (BALAD) is a recently described finding on optical coherence tomography (OCT) that has been reported in a variety of uveitic and retinal diseases. To add to the growing literature on conditions associated with this finding, we report on the first cases of BALADs in ocular sarcoidosis. Observational, retrospective chart review of patients with BALADs as a feature of sarcoid-associated uveitis. Three patients presented with blurry vision and bacillary layer detachments on OCT who were either known to have or found to have sarcoid-associated uveitis. All three patients had resolution of the BALAD and improvement in visual acuity with a combination of high-dose oral steroids and/or systemic immunosuppression. Patients presenting with BALAD should be assessed for sarcoidosis, if appropriate, given the systemic implications of this diagnosis. [Ophthalmic Surg Lasers Imaging Retina 2023;54:686-690.].

A Literary Pediatric Retina Fellowship With Michael T. Trese, MD.

Michael T. Trese, MD (1946-2022), a vitreoretinal surgeon, made significant contributions to the field of retina. Although most known for his work in pediatric retina surgery, he was a pioneer in areas such as medical retina, translational research, and telemedicine. This article reviews his major contributions to spread his knowledge more widely to vitreoretinal trainees and specialists. We discuss six areas where Trese made a lasting impact: lens-sparing vitrectomy, familial exudative vitreoretinopathy, congenital X-linked retinoschisis, autologous plasmin enzyme, regenerative medicine, and telemedicine. [Ophthalmic Surg Lasers Imaging Retina 2023;54:701-712.].

Clinical Utility of Spectral-Domain Optical Coherence Tomography Marker Disorganization of Retinal Inner Layers in Diabetic Retinopathy.

Disorganization of retinal inner layers (DRIL) is a potential spectral-domain optical coherence tomography (SD-OCT) imaging biomarker with clinical utility in diabetic retinopathy (DR). A cross-sectional study was conducted at a large academic center. The cohort was composed of 1,175 patients with type 2 diabetes with and without retinopathy on initial examination between September 2009 and January 2019 (n = 2,083 eyes). DR risk and progression factors were obtained from the medical record. Trained graders masked to patients' clinical histories evaluated SD-OCT scans for DRIL. Of 2,083 eyes, 28.1% (n = 585) demonstrated presence of DRIL with high interrater reliability (K = 0.88, 95% CI 0.86-0.90). DRIL was associated with worse visual acuity (VA) (P < 0.001) and DR severity (P < 0.0001). Insulin users had more severe DR (P < 0.0001). DR-related factors, race (Black, White) and sex (male) were significantly associated with DRIL (P < 0.05). DRIL was strongly associated with DR severity and worse VA, supporting its utility as an unfavorable prognostic indicator. [Ophthalmic Surg Lasers Imaging Retina 2023;54:692-700.].

Research And Design of A 3D Reconstruction Algorithm with Lio Algorithm

Based on existing research results, this article uses the WheelTev Ackermann-type robot to implement the Fast-Lio and related improved algorithms and initializes system parameters under the condition of tight coupling of laser radar and inertial navigation unit data. At the same time, the obtained point cloud map is subjected to backend loop detection to remove graphic distortion generated during mapping and achieve 3D structure reproduction in multiple semi-open environments. Selected scenes with structural features in the campus of Southeast University were used to test the algorithm's effectiveness, and the robot was able to successfully complete mapping requirements and achieve automatic cruising and return in industrial scenes, demonstrating the algorithm's reliability in industrial scenes. The algorithm was also tested on some open-source datasets, and the experimental results showed improvements in some performance parameters, especially in computational load and robustness compared to existing algorithms.

Transboundary transport of non-east and East Asian dust observed at Dunhuang, northwest China

Long-range transport of North African and Middle Eastern dust has been frequently observed in East Asia. However, understanding of their characteristics and effects in this region is insufficient. In this study, the transboundary transport of multi-source dust was captured by a polarized micro-pulse lidar from April 5 to 10, 2012, during a field campaign in Dunhuang, northwest China. Six dust plumes (DN1-DN6) were selected based on the spatiotemporal evolution of the depolarization ratio. Furthermore, the source, vertical characteristics of the coarse and fine components and their contributions were investigated by combining lidar observation with the WRF-Chem model simulation. The results indicated that DN1 was from Central Asia and it took approximately 4–5 days. DN2 originated from sources in North Africa and the Middle East and took approximately 6–7 days to reach Dunhuang. Samples DN3-DN6 originated from the Taklimakan and Gobi deserts in East Asia. A noticeable contrast was observed in the vertical distribution of the coarse and fine components between the long-range transported (DN1∼DN2) and fresh (DN3∼DN6) dust plumes. DN1 and DN2 presented comparable dust components (DD = Dc + Df, where Dc and Df are coarse- and fine-mode dust, respectively) and non-dust (ND) component contributions and a high predominance of Dc particles was present with at least 40% of total components in DN3-DN4. The contribution of ND particles was nearly negligible in DN5 and DN6. Lidar observations and model simulation both indicated that the maximum contribution of North African and Middle Eastern dust to the local aerosol loading exceeded 58%. However, the model simulation underestimated the contribution of DN1 by an average of 18% compared to lidar observations because DN1 was not reproduced well. This implies that the vertical distribution of dust should be carefully considered when dust impacts (e.g., radiation effects and air quality) are finely assessed using a model on a regional scale.

Mountain snow depth retrievals from customized processing of ICESat-2 satellite laser altimetry

Snow depth is highly variable across basins, yet most snow depth data in the western U.S. come from sparse in situ point measurements. The water resources community needs accurate snow depth data for improved basin-wide snow depth estimates. The NASA ICESat-2 mission has provided over four years of global satellite laser altimetry measurements since October 2018. Previous studies have shown that standard ICESat-2 data products, when combined with snow-off digital terrain models (DTMs) from airborne laser scanning, have the potential to provide snow depth measurements with varying accuracy depending on factors such as surface slope and canopy cover. In this study we show that ICESat-2 snow depth measurements can be improved with customized data products generated using the SlideRule Earth service. Here we investigate the accuracy of our ICESat-2 SlideRule snow depth method using four years (2019–2022) of reference in situ snow depth measurements and airborne lidar snow depth observations for two watersheds with varying terrain characteristics: the Tuolumne River basin above Hetch Hetchy, CA and the Methow Valley, WA. We observe median differences of −0.14 m (RMSE of 0.18 m) and −0.20 m (RMSE of 0.33 m) between our ICESat-2 snow depth measurements and reference snow depth measurements for the Tuolumne Basin and Methow Valley sites, respectively. While individual ICESat-2 elevation measurements can contain noise, basin-scale aggregation offers robust statistics for snow depth. Differences in accuracy between sites are attributed to terrain characteristics and their spatial distributions. The customized ICESat-2 SlideRule data products used in this study resulted in more accurate median snow depth values, including under canopy, than those found by previous studies using standard ICESat-2 data products in mid-latitude mountainous regions. When combined with snow-off DTMs, the aggregated snow-on ICESat-2 SlideRule observations could provide a new snow depth dataset across the western U.S. and potentially global land surfaces.