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Lidar Simulations of Backscatter and Extinction Uncertainty Estimates for ACCP Assessments

As part of the NASA Aerosols and Clouds, Convection, and Precipitation (ACCP) pre-formulation study, several lidar instruments are being assessed for their capabilities in advancing the science objectives enumerated in the most recent Earth Sciences Decadal Survey. These assessments are conducted using the NASA Langley Research Center(LaRC) lidar simulator, which produces profiles of backscatter and extinction uncertainty estimates for a given set of input parameters. Specifically, attention is focused onevaluating the science benefits and improvements of a candidate two-wavelength space-based High Spectral Resolution Lidar (HSRL) compared to an elastic backscatter lidar with similar capabilities to the Cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP), flying aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. In this talk, simulations of satellite-based HSRL and those of a CALIOP-like system are compared for different atmospheric scenes that capture varying total aerosol loadings, aerosol vertical distributions, and aerosol types.The results of these analyses are presented as functions of averaging resolution(horizontal/vertical), lighting conditions (daytime/nighttime), surface type (land/ocean),and wavelength (532 nm/1064 nm). Also, simulations of multiple aerosol layers and of aerosols beneath transparent cirrus clouds are shown to demonstrate the consequent impacts on underlying backscatter and extinction uncertainties, and to better differentiate the performance of HSRL compared to that of CALIOP for real-world atmospheric scenarios.

Travis D. Toth↗
NASA NTRS2019

Estimation of coarse dead wood stocks in intact and degraded forests in the Brazilian Amazon using airborne lidar

Coarse dead wood is an important component of forest carbon stocks, but it is rarely measured in Amazon forests and is typically excluded from regional forest carbon budgets. Our study is based on line intercept sampling for fallen coarse dead wood conducted along 103 transects with a total length of 48 km matched with forest inventory plots where standing coarse dead wood was measured in the footprints of larger areas of airborne lidar acquisitions. We developed models to relate lidar metrics and Landsat time series variables to coarse dead wood stocks for intact, logged, burned, or logged and burned forests. Canopy characteristics such as gap area produced significant individual relations for logged forests. For total fallen plus standing coarse dead wood (hereafter defined as total coarse dead wood), the relative root mean square error for models with only lidar metrics ranged from 33 % in logged forest to up to 36 % in burned forests. The addition of historical information improved model performance slightly for intact forests (31 % against 35 % relative root mean square error), not justifying the use of a number of disturbance events from historical satellite images (Landsat) with airborne lidar data. Lidar-derived estimates of total coarse dead wood compared favorably with independent ground-based sampling for areas up to several hundred hectares. The relations found between total coarse dead wood and variables quantifying forest structure derived from airborne lidar highlight the opportunity to quantify this important but rarely measured component of forest carbon over large areas in tropical forests.

Brazilian Amazon↗
NASA NTRS2021

The NASA AfriSAR Campaign: Airborne SAR and Lidar Measurements of Tropical Forest Structure and Biomass in Support of Current and Future Space Missions

In 2015 and 2016, the AfriSAR campaign was carried out as a collaborative effort among international space and National Park agencies (ESA, NASA, ONERA, DLR, ANPN and AGEOS) in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The NASA contribution to the campaign was conducted in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). A central motivation for the AfriSAR deployment was the common AGBD estimation requirement for the three future spaceborne missions, the lack of sufficient airborne and ground calibration data covering the full range of ABGD in tropical forest systems, and the intercomparison and fusion of the technologies. During the campaign, over 7000 km2 of waveform Lidar data from LVIS and 30000 km2 of UAVSAR data were collected over 10 key sites and transects. In addition, field measurements of forest structure and biomass were collected in sixteen 1 hectare sized plots. The campaign produced gridded Lidar canopy structure products, gridded aboveground biomass and associated uncertainties, Lidar based vegetation canopy cover profile products, Polarimetric Interferometric SAR and Tomographic SAR products and field measurements. Our results showcase the types of data products and scientific results expected from the spaceborne Lidar and SAR missions; we also expect that the AfriSAR campaign data will facilitate further analysis and use of waveform Lidar and multiple baseline polarimetric SAR datasets for carbon cycle, biodiversity, water resources and more applications by the greater scientific community.

AfriSAR↗
NASA NTRS2021

Quantifying the Observational Requirements of a Space-borne LiDAR Snow Mission

This study quantifies the level of observational accuracy required from a spaceborne light detection and ranging (LiDAR) snow depth retrieval mission for enabling beneficial impacts for snow estimation. The study is conducted over a region in Western Colorado using a suite of observing system simulation experiments(OSSEs).The Joint UK Land Environment Simulator, version 5.0 (JULES v5.0) is employed to simulate a suite of idealized LiDAR observations, considering a range of LiDAR snow depth retrieval errors, different hypothetical sensor swath widths, and the impact of cloud cover on observability. These simulated observations are then assimilated into the Noah land surface modelwith multi-parameterization options, version 3.6 (Noah-MP v3.6) model. This data assimilation setup is used to systematically evaluate the potential utility of LiDAR observations for improving modeled snow water equivalent (SWE)estimates and water budget variables such as runoff. Results from the OSSE runsshow that, in general, assimilation of synthetic LiDAR observations provide beneficial impacts when theLiDAR snow depth retrieval error standard deviation (σerror) is below 60 cm.Based on comparisons between the realistic (i.e., swath-limited and cloud-attenuated) case and the idealized (i.e., infinite swath width in the absence of cloud cover) case,this study concludes that observations with a conservative error standard deviation threshold of 40 cm (i.e., upper limit of the snow depth retrieval error that adds value to the SWE estimates viaassimilation) are needed for improving modeled snow estimates. More than a 33% reduction in SWEroot mean square errors and more than a 15% increase in correlation coefficientsare achieved when σ error ≤ 40 cmusing a 170-km sensor swath width in the presence of cloud attenuation effects. Further, the integrated hydrologic response, as represented by total (surface and subsurface) runoff estimates during the snow ablation season, are also enhanced when assimilating synthetic LiDAR snow depth retrievals with errors below this level.

Yonghwan Kwon↗
NASA NTRS2020

The Aerosol Characterization from Polarimeter and Lidar (ACEPOL) airborne field campaign

In the fall of 2017, an airborne field campaign was conducted from the NASA Armstrong Flight Research Center in Palmdale, California, to advance the remote sensing of aerosols and clouds with multi-angle polarimeters (MAP) and lidars. The Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign was jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON). Six instruments were deployed on the ER-2 high-altitude aircraft. Four were MAPs: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary EXploration (SPEX airborne), and the Research Scanning Polarimeter (RSP). The remainder were lidars, including the Cloud Physics Lidar (CPL) and the High Spectral Resolution Lidar 2 (HSRL-2). The southern California base of ACEPOL enabled observation of a wide variety of scene types, including urban, desert, forest, coastal ocean, and agricultural areas, with clear, cloudy, polluted, and pristine atmospheric conditions. Flights were performed in coordination with satellite overpasses and ground-based observations, including the Ground-based Multiangle SpectroPolarimetric Imager (GroundMSPI), sun photometers, and a surface reflectance spectrometer.

SPECTRAL-RESOLUTION LIDAR↗
NASA NTRS2019

On Promoting the Use of Lidar Systems in Forest Ecosystem Research

Forest structure is an important driver of ecosystem dynamics, including the exchange of carbon, water and energy between canopies and the atmosphere. Structural descriptors are also used in numerous studies of eco-logical processes and ecosystem services. Over the last 20+ years, lidar technology has fundamentally changed the way we observe and describe forest structure, and it will continue to impact the ways in which we investigate and monitor the relations between forest structure and functions. Here we present the currently available lidar system types (ground, air, and space-based), we highlight opportunities and challenges associated with each system, as well as challenges associated with a wider use of lidar technology and wider availability of lidar derived products. We also suggest pathways for lidar to further contribute to addressing questions in forest ecosystem science and increase benefits to a wider community of researchers.

Lidar systems↗
NASA NTRS2006

New Raman Water Vapor and Temperature Lidar at JPL Table Mountain Facility: Optimization, Validations and Sonde Intercomparison

Jet Propulsion Laboratory currently operates lidar systems at Table Mountain Facility (TMF), California (34.4 deg N, 117.7 deg W at 2300m) and Mauna Loa Observatory (MLO), Hawaii (19.5 deg N, 155.6 deg W at 3400m) under the Network for the Detection of Atmospheric Composition Change (NDACC, formerly NDSC). To complement existing NDACC lidars at TMF, which acts as a primary site for inter-comparisons, a new water vapor and temperature lidar has begun routine operation with typically 3-4 nightly profiles per week. As water vapor is a key greenhouse gas, and is highly variable on annual and seasonal cycles, accurate long term measurements are necessary for predictions of climate change and to increase our understanding of the atmospheric processes it contributes to. The new TMF lidar has demonstrated high spatial and temporal resolution, with a high degree of optimization being achieved over the past year, although the authors believe further improvement may yet be possible. The lidar has been designed for accuracies of 5% up to 12km in the free troposphere with the capability to measure to the tropopause and lower stratosphere with accuracies of 1 ppm. It is anticipated that the data sets produced will be used for Aura validation and for incorporation into NDACC archives. Validation results for the optimized system are presented with intercomparisons using Vaisala RS92-K radiosondes.

water vapor↗
NASA NTRS2012

Linear Estimation of Particle Bulk Parameters from Multi-Wavelength Lidar Measurements

An algorithm for linear estimation of aerosol bulk properties such as particle volume, effective radius and complex refractive index from multiwavelength lidar measurements is presented. The approach uses the fact that the total aerosol concentration can well be approximated as a linear combination of aerosol characteristics measured by multiwavelength lidar. Therefore, the aerosol concentration can be estimated from lidar measurements without the need to derive the size distribution, which entails more sophisticated procedures. The definition of the coefficients required for the linear estimates is based on an expansion of the particle size distribution in terms of the measurement kernels. Once the coefficients are established, the approach permits fast retrieval of aerosol bulk properties when compared with the full regularization technique. In addition, the straightforward estimation of bulk properties stabilizes the inversion making it more resistant to noise in the optical data. Numerical tests demonstrate that for data sets containing three aerosol backscattering and two extinction coefficients (so called 3 + 2 ) the uncertainties in the retrieval of particle volume and surface area are below 45% when input data random uncertainties are below 20 %. Moreover, using linear estimates allows reliable retrievals even when the number of input data is reduced. To evaluate the approach, the results obtained using this technique are compared with those based on the previously developed full inversion scheme that relies on the regularization procedure. Both techniques were applied to the data measured by multiwavelength lidar at NASA/GSFC. The results obtained with both methods using the same observations are in good agreement. At the same time, the high speed of the retrieval using linear estimates makes the method preferable for generating aerosol information from extended lidar observations. To demonstrate the efficiency of the method, an extended time series of observations acquired in Turkey in May 2010 was processed using the linear estimates technique permitting, for what we believe to be the first time, temporal-height distributions of particle parameters.

linear estimation↗
NASA NTRS2014

Performance Simulations for a Spaceborne Methane Lidar Mission

Future spaceborne lidar measurements of key anthropogenic greenhouse gases are expected to close current observational gaps particularly over remote, polar, and aerosol-contaminated regions, where actual in situ and passive remote sensing observation techniques have difficulties. For methane, a "Methane Remote Lidar Mission" was proposed by Deutsches Zentrum fuer Luft- und Raumfahrt and Centre National d'Etudes Spatiales in the frame of a German-French climate monitoring initiative. Simulations assess the performance of this mission with the help of Moderate Resolution Imaging Spectroradiometer and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations of the earth's surface albedo and atmospheric optical depth. These are key environmental parameters for integrated path differential absorption lidar which uses the surface backscatter to measure the total atmospheric methane column. Results showthat a lidar with an average optical power of 0.45W at 1.6 μm wavelength and a telescope diameter of 0.55 m, installed on a low Earth orbit platform(506 km), will measure methane columns at precisions of 1.2%, 1.7%, and 2.1% over land, water, and snow or ice surfaces, respectively, for monthly aggregated measurement samples within areas of 50 × 50 km2. Globally, the mean precision for the simulated year 2007 is 1.6%, with a standard deviation of 0.7%. At high latitudes, a lower reflectance due to snow and ice is compensated by denser measurements, owing to the orbital pattern. Over key methane source regions such as densely populated areas, boreal and tropical wetlands, or permafrost, our simulations show that the measurement precision will be between 1 and 2%.

methane↗
NASA NTRS2013

Assessing the Temperature Dependence of Narrow-Band Raman Water Vapor Lidar Measurements: A Practical Approach

Narrow-band detection of the Raman water vapor spectrum using the lidar technique introduces a concern over the temperature dependence of the Raman spectrum. Various groups have addressed this issue either by trying to minimize the temperature dependence to the point where it can be ignored or by correcting for whatever degree of temperature dependence exists. The traditional technique for performing either of these entails accurately measuring both the laser output wavelength and the water vapor spectral passband with combined uncertainty of approximately 0.01 nm. However, uncertainty in interference filter center wavelengths and laser output wavelengths can be this large or larger. These combined uncertainties translate into uncertainties in the magnitude of the temperature dependence of the Raman lidar water vapor measurement of 3% or more. We present here an alternate approach for accurately determining the temperature dependence of the Raman lidar water vapor measurement. This alternate approach entails acquiring sequential atmospheric profiles using the lidar while scanning the channel passband across portions of the Raman water vapor Q-branch. This scanning is accomplished either by tilt-tuning an interference filter or by scanning the output of a spectrometer. Through this process a peak in the transmitted intensity can be discerned in a manner that defines the spectral location of the channel passband with respect to the laser output wavelength to much higher accuracy than that achieved with standard laboratory techniques. Given the peak of the water vapor signal intensity curve, determined using the techniques described here, and an approximate knowledge of atmospheric temperature, the temperature dependence of a given Raman lidar profile can be determined with accuracy of 0.5% or better. A Mathematica notebook that demonstrates the calculations used here is available from the lead author.

Raman lidar↗
NASA NTRS2014

A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation

Acquiring current, accurate land-use information is critical for monitoring and understanding the impact of anthropogenic activities on natural environments.Remote sensing technologies are of increasing importance because of their capability to acquire information for large areas in a timely manner, enabling decision makers to be more effective in complex environments. Although optical imagery has demonstrated to be successful for land cover classification, active sensors, such as light detection and ranging (LiDAR), have distinct capabilities that can be exploited to improve classification results. However, utilization of LiDAR data for land cover classification has not been fully exploited. Moreover, spatial-spectral classification has recently gained significant attention since classification accuracy can be improved by extracting additional information from the neighboring pixels. Although spatial information has been widely used for spectral data, less attention has been given to LiDARdata. In this work, a new framework for land cover classification using discrete return LiDAR data is proposed. Pseudo-waveforms are generated from the LiDAR data and processed by hierarchical segmentation. Spatial featuresare extracted in a region-based way using a new unsupervised strategy for multiple pruning of the segmentation hierarchy. The proposed framework is validated experimentally on a real dataset acquired in an urban area. Better classification results are exhibited by the proposed framework compared to the cases in which basic LiDAR products such as digital surface model and intensity image are used. Moreover, the proposed region-based feature extraction strategy results in improved classification accuracies in comparison with a more traditional window-based approach.

Light Detection & Ranging (LIDAR)↗
NASA NTRS2013

Airborne Measurements of CO2 Column Concentration and Range Using a Pulsed Direct-Detection IPDA Lidar

We have previously demonstrated a pulsed direct detection IPDA lidar to measure range and the column concentration of atmospheric CO2. The lidar measures the atmospheric backscatter profiles and samples the shape of the 1,572.33 nm CO2 absorption line. We participated in the ASCENDS science flights on the NASA DC-8 aircraft during August 2011 and report here lidar measurements made on four flights over a variety of surface and cloud conditions near the US. These included over a stratus cloud deck over the Pacific Ocean, to a dry lake bed surrounded by mountains in Nevada, to a desert area with a coal-fired power plant, and from the Rocky Mountains to Iowa, with segments with both cumulus and cirrus clouds. Most flights were to altitudes >12 km and had 5-6 altitude steps. Analyses show the retrievals of lidar range, CO2 column absorption, and CO2 mixing ratio worked well when measuring over topography with rapidly changing height and reflectivity, through thin clouds, between cumulus clouds, and to stratus cloud tops. The retrievals shows the decrease in column CO2 due to growing vegetation when flying over Iowa cropland as well as a sudden increase in CO2 concentration near a coal-fired power plant. For regions where the CO2 concentration was relatively constant, the measured CO2 absorption lineshape (averaged for 50 s) matched the predicted shapes to better than 1% RMS error. For 10 s averaging, the scatter in the retrievals was typically 2-3 ppm and was limited by the received signal photon count. Retrievals were made using atmospheric parameters from both an atmospheric model and from in situ temperature and pressure from the aircraft. The retrievals had no free parameters and did not use empirical adjustments, and >70% of the measurements passed screening and were used in analysis. The differences between the lidar-measured retrievals and in situ measured average CO2 column concentrations were <1.4 ppm for flight measurement altitudes >6 km.

Lidar↗
NASA NTRS2015

Development of a Sodium LIDAR for Spaceborne Missions

We are currently developing laser and electro-optic technologies to remotely measure Sodium (Na) by adapting existing lidar technology with space flight heritage. The developed instrumentation will serve as the core for the planning of a Heliophysics mission targeted to study the composition and dynamics of Earths mesosphere based on a spaceborne lidar that will measure the mesospheric Na layer. There is a pressing need in the Ionosphere Thermosphere - Mesosphere (ITM) community for high-resolution measurements that can characterize small-scale dynamics (i.e. Gravity Waves with wavelengths smaller than a few hundred km) and their effects in the Mesosphere-Lower-Termosphere (MLT) on a global basis. This is compelling because they are believed to be the dominant contributors to momentum transport and deposition in the MLT, which largely drive the global circulation and thermal structure and interactions with the tides and planetary waves in this region. We are developing a spaceborne remote sensing technique that will enable acquisition of global Na density, temperature and wind measurements in the MLT with the spatial and temporal resolution required to resolve issues associated with the structure, chemistry, dynamics, and energetics of this regionA nadir-pointing spaceborne Na Doppler resonance fluorescence LIDAR on board of the ISS will essentially make high-resolution, in time and space, Na density, temperature and vertical wind measurements, from 75-115 km (MLT region). Our instrument concept consisted of a high-energy laser transmitter at 589 nm and highly sensitive photon counting detector that allows for range-resolved atmospheric-sodium-temperature profiles. The atmospheric temperature is deduced from the linewidth of the resonant fluorescence from the atomic sodium vapor D2 line as measured by our tunable laser. We are currently developing a high power energy laser that allows for some day time sodium lidar observations with the help of a narrow bandpass filter based on etalon or atomic sodium Faraday filter with 5 to 10 pm optical bandwidth. The current baseline detector for the lidar instrument is a 16-channel Photomultiplier Tube with receiver electronics that has been space-qualified for the ICESat-2ATLAS mission. Our technique uses the 16-channels as a photon-number-resolving single detector to provide the required full-spectroscopic sodium lineshape waveform for recovering Mesospheric temperature profiles. In this paper, we will describe our instrument concept for a future Heliophysics space mission based on board of the International Space Station (ISS).

lidar↗
NASA NTRS2014

The Uncertainty of Biomass Estimates from LiDAR and SAR Across a Boreal Forest Structure Gradient

In this study, weexamined the uncertainty of aboveground live biomass (AGB) estimates based on light detection and ranging (LiDAR) and synthetic aperture radar (SAR) measurements distributed across a low-biomass vegetation structure gradient fromforest to non-forest in boreal-like ecosystems. The conifer-dominant structure gradient was compiled from ground data amassed from multiple field expeditions in central Maine (USA), Aurskog (Norway), and across central Siberia (Russia). Single variable empirical models were built tomodel AGB fromremote sensingmetrics. Using thesemodels, we calculated a rootmean square error (RMSE) and a 95% confidence interval (CI) of the RMSE fromthe difference between the remote sensing AGB predictions and the ground reference AGB estimates within AGB intervals across a 0-100 Mg ha(exp.−1) boreal forest structure gradient. The results show that the error in AGB predictions (RMSE) and the error uncertainty (the CI) from LiDAR and SAR change across a forest gradient. The errors of airborne LiDAR and SAR metrics and spaceborne LiDAR platforms show a general trend of reduced relative errors as AGBmagnitudes increase, particularly from0 to 60 Mg ha (exp.−1). Empirical models relating spaceborne metrics to AGB and estimates of spaceborne LiDAR error uncertainty demonstrate the difficulty of characterizing differences in AGB at the site-level with current spaceborne sensors, particularly below 80 Mg ha (exp.−1) with less than 50-100% error.

Taiga↗
NASA NTRS2015

Remote Sensing Measurements of the CO2 Mixing Ratio in the Planetary Boundary Layer Using Cloud Slicing with Airborne Lidar

We have measured the CO2 volume mixing ratio (VMR) within the planetary boundary layer (PBL) using cloud slicing with an airborne pulsed integrated path differential absorption (IPDA) lidar from flight altitudes of up to 13 km. During a flight over Iowa in summer 2011, simultaneous measurement of the optical range and CO2 absorption to clouds and the ground were made using time-resolved detection of pulse echoes from each scattering surface.We determined the CO2 absorption in the PBL by differencing the two lidar-measured absorption line shapes, one to a broken shallow cumulus cloud layer located at the top of the PBL and the other to the ground. Solving for the CO2 VMR in the PBL and that of the free troposphere, we measured a ≈15 ppm (4%) drawdown in the PBL. Both CO2 VMRs were within ≈3 ppm of in situ CO2 profile measurements. We have also demonstrated cloud slicing using scatter from thin, diffuse cirrus clouds and cumulus clouds, which allowed solving for the CO2 VMR for three vertical layers. The technique and retrieval algorithm are applicable to a space-based lidar instrument as well as to lidar IPDA measurements of other trace gases. Thus, lidar cloud slicing also offers promise toward space-based remote sensing of vertical trace gas profiles in the atmosphere using a variety of clouds.

VMR↗
NASA NTRS2020

Instrument Offset Optimization Technique for Airborne Doppler Wind Lidar Profiling Algorithm

This paper presents the instrument offset optimization technique for the Doppler Aerosol Wind Lidar (DAWN) profiling algorithm at NASA Langley Research Center (LaRC). The 2 um Doppler wind Lidar system at LaRC has a long history of research development and airborne campaigns. The core of the wind Lidar data processing is Airborne Wind Profiling Algorithm for Doppler Wind Lidar (APOLO), which was developed at LaRC. The extraction of wind parameters from the airborne Doppler wind Lidar data is challenging due to many factors such as the speed of aircraft, and the errors in instrument installation and calibration. It is unavoidable to prevent the adverse impact of calibration error and instrument installation offset on the performance of wind parameter estimation in the algorithm. Due to the unsteady environment where the data are collected, even a small offset will result in nonsensical results in the parameter estimation process. A brief introduction of APOLO and the overview of the optimization techniques are presented in this paper.

Wind profile↗
NASA NTRS2020

Variability of Lidar-Derived Particle Properties Over West Africa Due to Changes in Absorption: Towards an Understanding

Measurements performed in western Africa (Senegal) during the SHADOW field campaign are analyzed to show that spectral dependence of the imaginary part of the complex refractive index (CRI) of dust can be revealed by lidar-measured particle parameters. Observations in April 2015 provide good opportunity for such study, because, due to high optical depth of the dust, exceeding 0.5, the extinction coefficient could be derived from lidar measurements with high accuracy and the contribution of other aerosol types, such as biomass burning, was negligible. For instance, in the second half of April 2015, AERONET observations demonstrated a temporal decrease in the imaginary part of the CRI at 440 nm from approximately 0.0045 to 0.0025. This decrease is in line with a change in the relationship between the lidar ratios (the extinction-to-backscattering ratio) at 355 and 532 nm (S(355) and S(532)). For instance in the first half of April, S(355)∕S(532) is as high as 1.5 and the backscatter Angstrom exponent, A(β), is as low as −0.75, while after 15 April S(355)/S(532)=1.0 and A(β) is close to zero. The aerosol depolarization ratio δ(532) for the whole of April exceeded 30 % in the height range considered, implying that no other aerosol, except dust, occurred. The performed modeling confirmed that the observed S(355)∕S(532) and Aβ values match the spectrally dependent imaginary part of the refractive index as can be expected for mineral dust containing iron oxides. The second phase of the SHADOW campaign was focused on evaluation of the lidar ratio of smoke and estimates of its dependence on relative humidity (RH). For five studied smoke episodes the lidar ratio increases from 44±5 to 66±7 sr at 532 nm and from 62±6 to 80±8 sr at 355 nm, when RH varied from 25 % to 85 %. Performed numerical simulations demonstrate that observed ratio S(355)∕S(532), exceeding 1.0 in the smoke plumes, can indicate an increase in the imaginary part of the smoke particles in the ultraviolet (UV) range.

Lidar-Derived Particle Properties↗
NASA NTRS2021

High-speed wide-angle interleaved scanning technique for a 3D imaging lidar

In this paper, we describe a novel interleaved scanner for an eye-safe 3D scanning lidar system to measure aerodynamic phenomena in a wind tunnel using elastic backscatter from seeding particles. The scanner assembly consists of a rotating polygon scanner for line scanning along the fast axis, a galvanometer (galvo) scanner for scanning along the slow axis, angular position sensors and motor controllers. The polygon scanner sweeps the lidar beam at up to 10,000 lines/s across a 27 deg angular field-of-regard in the fast axis, while the galvo scanner covers an angular range of 20 deg in the slow axis. Using this scanner, the lidar can perform non-intrusive flow visualization, velocimetry, and hard target mapping at mmscale spatial resolution to a standoff range of 5 m at an update rate of 50 Hz for the full field-of-regard. An interleaved scanning methodology for the acquisition of two snapshots of 3D lidar intensity data with a 100 μs time offset is discussed. The design of the control and data acquisition electronics is described. The effects of rapid scanning on the response of a narrow field-of-view lidar are addressed. The design of the scanner is scalable and can be tailored to meet the requirements of other applications by increasing or decreasing the standoff range, angular field-of-regard and scan rate.

3D Imaging Lidar↗