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Impacts of small‐scale surface irregularities, or surface roughness, of atmospheric ice crystals on lidar backscattering properties are quantified. Geometric ice crystal models with various degrees of surface roughness and state‐of‐the‐science light‐scattering computational capabilities are utilized to simulate the single‐scattering properties across the entire practical size parameter range. The simulated bulk lidar and depolarization ratios of polydisperse ice crystals at wavelength 532 nm are strongly sensitive to the degree of surface roughness. Comparisons of these quantities between the theoretical simulations and counterparts inferred from spaceborne lidar observations for cold cirrus clouds suggest a typical surface‐roughness‐degree range of 0.03–0.15 in the cases of compact hexagonal ice crystals, which is most consistent with direct measurements of scanning electron microscopic images. To properly interpret lidar backscattering observations of ice clouds, it is necessary to account for the degree of surface roughness in light‐scattering computations involving ice crystals. Plain Language Summary Lidar (Light Detection and Ranging) instruments on satellites use reflected, or backscattered, laser beams to investigate ice clouds in the atmosphere. However, it has long been a challenge to interpret lidar signals, called backscattering properties, to infer ice cloud characteristics accurately. This study uses theoretical simulations to investigate how small‐scale surface irregularities of ice crystals affect the lidar signals associated with ice clouds. These simulations demonstrate the significant impacts of small‐scale surface irregularities of ice crystals on backscattering. Based on comparisons between the theoretical simulations and satellite lidar observations, it is necessary to assume a moderate degree of small‐scale surface irregularities to explain lidar observations of typical ice clouds. Key Points The sensitivity of the backscattering properties to the surface roughness of atmospheric ice crystals is theoretically investigated The depolarization ratio is substantially sensitive to the degree of surface roughness of ice crystals Compact hexagonal ice models with degrees of surface roughness ranging 0.03–0.15 reasonably explain the Cloud‐Aerosol Lidar with Orthogonal Polarization backscattering signals

Lidar remote sensing has proven to be a powerful tool for estimating ground elevation, canopy height, and additional vegetation parameters, which in turn are valuable information for the investigation of ecosystems. Spaceborne lidar systems, like the Global Ecosystem Dynamics Investigation (GEDI), can deliver these height estimates on a near global scale. This paper analyzes the accuracy of the first version of GEDI ground elevation and canopy height estimates in two study areas with temperate forests in the Free State of Thuringia, central Germany. Digital terrain and canopy height models derived from airborne laser scanning data are used as reference heights. The influence of various environmental and acquisition parameters (e.g., canopy cover, terrain slope, beam type) on GEDI height metrics is assessed. The results show a consistently high accuracy of GEDI ground elevation estimates under most conditions, except for areas with steep slopes. GEDI canopy height estimates are less accurate and show a bigger influence of some of the included parameters, specifically slope, vegetation height, and beam sensitivity. A number of relatively high outliers (around 9–13% of the measurements) is present in both ground elevation and canopy height estimates, reducing the estimation precision. Still, it can be concluded that GEDI height metrics show promising results and have potential to be used as a basis for further investigations.

Plant biodiversity supports life on Earth and provides a range of important ecosystem services, but is under severe pressure by global change. Structural diversity plays a crucial role for carbon, water and energy cycles and animal habitats. However, it is very difficult to map and monitor over large areas, limiting our ability to assess the status of biodiversity and predict change. NASA's Global Ecosystem Dynamics Investigation (GEDI) provides a new opportunity to measure 3D plant canopy structure of the world's temperate, Mediterranean and tropical ecosystems, but its potential to map structural diversity is not yet tested. Here, we use wall-to-wall airborne laser scanning (ALS) to simulate GEDI data (GEDIsim) over 7380 km2 in the southern Sierra Nevada mountains in California and evaluate how well GEDI's sampling scheme captures patterns of structural diversity. We evaluate functional richness and functional beta diversity in a biodiversity hot spot. GEDIsim performed well for trait retrievals (r2 = 0.68) and functional richness mapping (r2 = 0.75) compared to ALS retrievals, despite lower correlations in complex terrain with steep slopes. Functional richness patterns were strongly associated with soil organic carbon stocks and density as well as variables related to water availability and could be appropriately mapped by GEDIsim with and without cloud cover. Functional beta diversity was more strongly related to local changes in topography and more challenging to map, especially with decreasing sampling density. The reduced number of GEDIsim shots when simulating cloud cover lead to a strong overestimation of beta diversity and a reduction of r2 from 0.64 to 0.40 compared to ALS. The ability to map functional richness has been demonstrated with potential application at continental scales that could be transformative for our understanding of large-scale patterns of plant canopy structure, diversity and potential links to animal diversity, movement and habitats.

Detecting submerged targets in shallow waters from satellite platforms remains a challenge, as the optical spectral information of targets is significantly distorted by the absorption and scattering effects of the water column. In this study, we propose a new framework as the bathymetry‐informed target extraction, which integrates the spaceborne lidar data and multispectral imagery. By using lidar assisted Satellite‐Derived Bathymetry model, we convert the complex multispectral information into relative depth data. Through this transformation, the challenging issue of distorted color domain image segmentation is converted into the task of depth anomaly detection. The method is validated on submerged artificial stone weirs and breakwaters in typical open ocean and coastal waters, which indicates significant improvements in target detection rate and reliability compared to direct color‐based methods. This approach promises large‐scale surveys of submerged targets in shallow waters, offering an alternative solution to on‐site surveys such as shipborne sonars.

A more accurate representation of ice‐phase processes in numerical models necessitates an enhanced understanding of ice‐particle microphysics and their respective formation conditions. Prior in situ measurements have noted distinctive ice‐particle shape characteristics associated with different cloud systems and geographical locations. The recent advancement in ice‐particle backscattering theories enables a more comprehensive exploration of the geographical distribution and seasonal dependence of ice‐particle shape categories than ever before. This exploration is being undertaken for the first time using space‐borne lidar measurements. Distinct geographical preferences were observed for five different ice‐particle categories. Bullets/rosettes were the most common, followed by Voronois, which were especially prevalent in high‐level tropical clouds, and 2D columns, which were commonly found in mid‐ and low‐level clouds. Droxtals were primarily observed in high‐level subtropical regions. The global distribution of ice‐particle types provides valuable insights into the physical processes related to ice cloud particle shape formation, cloud duration, and radiative impacts. Plain Language Summary An enhanced understanding of ice‐particle microphysics associated with different cloud systems and geographical locations is expected to improve the representation of ice clouds in numerical models for better future climate predictions. For such purpose, space‐borne lidar observations have been intensively used to characterize the global distribution of cloud phases, as well as ice particle types. Latest theoretical studies have indicated the possibility of further decomposing these ice particle types into more specific ice‐particle category information, but had not been applied to actual global observation data. In this work, the geographical distribution of five ice‐particle categories was derived based on the theoretical studies using spaceborne lidar data for the first time. It was found that different ice‐particle categories had a unique geographical preference for occurrence, along with seasonal and height‐dependent characteristics. The global distribution of ice‐particle categories obtained in the present study is expected to be useful for understanding the physical processes related to ice‐particle shape formation and the ice‐particle terminal velocity characteristics relevant to cloud duration. Key Points For the first time, spaceborne lidar was used to study meticulously the height‐resolved global distribution of five ice‐particle categories Each cloud‐particle type displayed a unique geographical preference for occurrence, along with seasonal and height‐dependent characteristics The global data on ice‐particle types and particle size from radar‐lidar synergies promises to be valuable for future cloud modeling studies

Global Ecosystem Dynamics Investigation (GEDI) satellite mission is expanding the spatial bounds and temporal resolution of large-scale mapping applications. Integrating the recent GEDI data into Airborne Laser Scanning (ALS)-derived estimations represents a global opportunity to update and extend forest models based on area based approaches (ABA) considering temporal and spatial dynamics. This study evaluates the effect of combining ALS-based aboveground biomass (AGB) estimates with GEDI-derived models by using temporally coincident datasets. A gradient of forest ecosystems, distributed through 21,766 km2 in the province of Badajoz (Spain), with different species and structural complexity, was used to: (i) assess the accuracy of GEDI canopy height in five Mediterranean Ecosystems and (ii) develop GEDI-based AGB models when using ALS-derived AGB estimates at GEDI footprint level. In terms of Pearson’s correlation (r) and rRMSE, the agreement between ALS and GEDI statistics on canopy height was stronger in the denser and homogeneous coniferous forest of P. pinaster and P. pinea than in sparse Quercus-dominated forests. The GEDI-derived AGB models using relative height and vertical canopy metrics yielded a model efficiency (Mef) ranging from 0.31 to 0.46, with a RMSE ranging from 14.13 to 32.16 Mg/ha and rRMSE from 38.17 to 84.74%, at GEDI footprint level by forest type. The impact of forest structure confirmed previous studies achievements, since GEDI data showed higher uncertainty in highly multilayered forests. In general, GEDI-derived models (GEDI-like Level4A) underestimated AGB over lower and higher ALS-derived AGB intervals. The proposed models could also be used to monitor biomass stocks at large-scale by using GEDI footprint level in Mediterranean areas, especially in remote and hard-to-reach areas for forest inventory. The findings from this study serve to provide an initial evaluation of GEDI data for estimating AGB in Mediterranean forest.

Continuous large-scale mapping of forest canopy height is crucial for estimating and reporting forest carbon content, analyzing forest degradation and restoration, or to model ecosystem variables such as aboveground biomass. Over the last years, the spaceborne Light Detection and Ranging (LiDAR) sensor specifically designed to acquire forest structure information, Global Ecosystem Dynamics Investigation (GEDI), has been used to extract forest canopy height information over large areas. Yet, GEDI has no spatial coverage for most forested areas in Canada and other high latitude regions. On the other hand, the spaceborne LiDAR called Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides a global coverage but was not specially developed to study forested ecosystems. Nonetheless, both spaceborne LiDAR sensors obtain point-based information, making spatially continuous forest canopy height estimation very challenging. This study compared the performance of both spaceborne LiDAR, GEDI and ICESat-2, combined with ALOS-2/PALSAR-2 and Sentinel-1 and -2 data to produce continuous canopy height maps in Canada for the year 2020. A set-aside dataset and airborne LiDAR (ALS) from a national LiDAR campaign were used for accuracy assessment. Both maps overestimated canopy height in relation to ALS data, but GEDI had a better performance than ICESat-2 with a mean difference (MD) of 0.9 m and 2.9 m, and a root mean square error (RMSE) of 4.2 m and 5.2 m, respectively. However, as both GEDI and ALS have no coverage in most of the hemi-boreal forests, ICESat-2 captures the tall canopy heights expected for these forests better than GEDI. PALSAR-2 HV polarization was the most important covariate to predict canopy height, showing the great potential of L-band in comparison to C-band from Sentinel-1 or optical data from Sentinel-2. The approach proposed here can be used operationally to produce annual canopy height maps for areas that lack GEDI and ICESat-2 coverage.

In the realm of earth observation and space exploration, LiDAR technology offers humanity insights into the dynamics of our planet and beyond. This paper reviews spaceborne LiDAR instruments with attention to their evolution, capabilities, and achievements. We focus on the high-level LiDAR instrument design, their components, and their operational parameters in contribution to the study of Earth. Through examining selected space missions, this work illustrates the role of LiDAR technology in our understanding of environmental and atmospheric phenomena. Furthermore, the paper looks ahead, discussing the ongoing development of advanced LiDAR technologies.

At the end of May 2025, extremely strong wildfires in Canada produced several pyrocumulonimbus clouds lifting the smoke particles up to the lower stratosphere. Stratospheric aerosol optical depths of more than 2.5 were observed by the ATmospheric LiDAR (ATLID) onboard of the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite. ATLID observations showed that the smoke layer top ascended from 13.6 km over Canada to 17.4 km over Europe. The enhanced depolarization ratio of 0.24 ±$\\pm $0.02 stayed constant during transport and indicated non‐spherical smoke particles. ATLID is the first space lidar that is able to measure the extinction‐to‐backscatter ratio (lidar ratio) at 355 nm. A decrease of the lidar ratio with increasing transport time was observed from values of 68 ±$\\pm $5 to 49 ±$\\pm $5 sr. EarthCARE observed the substantial downmixing of the stratospheric smoke into the troposphere at tropopause folds over the Mediterranean revealing an important removal process of stratospheric smoke.

Cirrus clouds often contain numerous hollow ice crystals, which are distinct in scattering properties from solid ice crystals, and will be challenging to microphysical retrieval and radiative forcing assessment. Currently, hollow ice crystals have not been observed by remote sensing methods, and the estimation of their hollowness is a complex task. To address this issue, the Mixed Modal Hollow Columns (MMHC) model for hollow ice crystals is introduced, and its backscattering properties are computed using the physical optics approximation method. Through comparison with spaceborne lidar observations, we identify a specific type of cirrus associated with the MMHC model for the first time. The visible optical depth of this cirrus is less than or equal to 0.1, and the temperature is between −60 and −40°C. The MMHC characteristic cirrus clouds are prevalent in middle and high latitudes but less comm+on in low latitudes. They exhibit distinct patterns in terms of sea and land distribution as well as seasonal variation. Plain Language Summary Cirrus clouds typically form at altitudes exceeding 6 km and consist of non‐spherical ice crystals, each with unique optical properties affecting the earth’s radiation balance through scattering and absorption. Notably, these crystals include hollow formations, which are distinct in scattering properties from their solid counterparts. The coexistence of hollow and solid ice crystals presents challenges in accurately inverting ice crystal morphology in remote sensing and evaluating radiation properties in climate models. To address these complexities, we propose a MMHC model. Through analysis of spaceborne lidar observational data, we identify characteristic cirrus clouds associated with such type of ice crystals and study the global distribution and seasonal distribution of this kind of cirrus clouds. Key Points A Mixed Modal Hollow Columns model is formulated to characterize the presence of hollow ice crystals in cirrus clouds For the first time, hollow characteristic cirrus clouds are observed using spaceborne lidar Hollow characteristic cirrus clouds exhibit distinct global and seasonal distribution variations