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There are currently three operational lidar systems orbiting the Earth, the Moon and the planet Mercury gathering scientific data and images to form a better understanding of our Earth and solar system. In this paper we will present an overview of the spacebome laser programs and offer insights into future spacebome lasers for remote sensing applications.
The CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) mission is a comprehensive suite of active and passive sensors including a 20Hz 230mj Nd:YAG lidar, a visible wavelength Earth-looking camera and an imaging infrared radiometer. CALIPSO flies in formation with the Earth Observing System Post-Meridian (EOS PM) train, provides continuous, near-simultaneous measurements and is a planned 3 year mission. CALIPSO was launched into a 98 degree sun synchronous Earth orbit in April of 2006 to study clouds and aerosols and acquires over 5 gigabytes of data every 24 hours. The ground track of one CALIPSO orbit as well as high and low intensity South Atlantic Anomaly outlines is shown. CALIPSO passes through the SAA several times each day. Spaced based remote sensing systems that include multiple instruments and/or instruments such as lidar generate large volumes of data and require robust real-time hardware and software mechanisms and high throughput processors. Due to onboard storage restrictions and telemetry downlink limitations these systems must pre-process and reduce the data before sending it to the ground. This onboard processing and realtime requirement load may mean that newer more powerful processors are needed even though acceptable radiation-hardened versions have not yet been released. CALIPSO's single board computer payload controller processor is actually a set of four (4) voting non-radiation hardened COTS Power PC 603r's built on a single width VME card by General Dynamics Advanced Information Systems (GDAIS). Significant radiation concerns for CALIPSO and other Low Earth Orbit (LEO) satellites include the South Atlantic Anomaly (SAA), the north and south poles and strong solar events. Over much of South America and extending into the South Atlantic Ocean the Van Allen radiation belts dip to just 200-800km and spacecraft entering this area are subjected to high energy protons and experience higher than normal Single Event Upset (SEU) and Single Event Latch-up (SEL) rates. Although less significant, spacecraft flying in the area around the poles experience similar upsets. Finally, powerful solar proton events in the range of 10MeV/10pfu to 100MeV/1pfu as are forecasted and tracked by NOAA's Space Environment Center in Colorado can result in Single Event Upset (SEU), Single Event Latch-up (SEL) and permanent failures such as Single Event Gate Rupture (SEGR) in some technologies. (Galactic Cosmic Rays (GCRs) are another source, especially for gate rupture) CALIPSO mitigates common radiation concerns in its data handling through the use of redundant processors, radiation-hardened Application Specific Integrated Circuits (ASIC), hardware-based Error Detection and Correction (EDAC), processor and memory scrubbing, redundant boot code and mirrored files. After presenting a system overview this paper will expand on each of these strategies. Where applicable, related on-orbit data collected since the CALIPSO initial boot on May 4, 2006 will be noted.
NASA atmospheric scientists are currently planning active remote sensing missions that will enable global monitoring of atmospheric ozone, water vapor, aerosols and clouds as well as global wind velocity. The measurements of these elements and parameters are important because of the effects they have on climate change, atmospheric chemistry and dynamics, atmospheric transport and, in general, the health of the planet. NASA will make use of Differential Absorption Lidar (DIAL) and backscatter lidar techniques for active remote sensing of molecular constituents and atmospheric phenomena from advanced high-altitude aircraft and space platforms. This paper provides an overview of NASA Langley Research Center's (LaRC's) development of advanced solid state lasers, harmonic generators, and wave mixing techniques aimed at providing the broad range of wavelengths necessary to meet measurement goals of NASA's Earth Science Enterprise.
Mapping vegetation species is critical to facilitate related quantitative assessment, and mapping invasive plants is important to enhance monitoring and management activities. Integrating high-resolution multispectral remote-sensing (RS) images and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple sources of high-resolution RS data for vegetation mapping on a large spatial scale can be both computationally and sampling intensive. Here, we designed a two-step classification workflow to potentially decrease computational cost and sampling effort and to increase classification accuracy by integrating multispectral and lidar data in order to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1362 km2) of Tennessee (U.S.). Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive, coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the producer’s accuracy, user’s accuracy, and Kappa for the SVM model on kudzu were 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions as well as map other vegetation species.
Integrated path differential absorption (IPDA) lidar is an active remote sensing technique for monitoring different atmospheric species. The technique relies on wavelength differentiation between strong and weak absorbing features normalized to the transmitted energy. An advanced 2-μm triple-pulse IPDA lidar was developed at NASA Langley Research Center for active sensing of carbon dioxide and water vapor simultaneously. The IPDA transmitter produces three successive laser pulses separated by a short interval (200 μs) with a repetition rate of 50Hz. Measurement of laser pulse energy accurately is a prerequisite for the retrieval of gas mixing ratios from IPDA. The design and calibration of a 2-μm triple-pulse laser energy monitor are presented. Due to the short interval between the three transmitted pulses, conventional thermal energy monitors underestimate the total transmitted energy. The design is based on a high speed, extended range InGaAs pin quantum detector suitable for separating the three pulse events. Pulse integration is applied for converting the detected pulse power into energy. The results obtained from the laser energy monitor were compared to an ultra-fast energy-meter reference for energy scaling and verification. High correlations between the pin energy monitor and the total transmitted energy were obtained. The objective of this development is to reduce measurement biases and errors using the triple-pulse IPDA technique.
Various lidar methods have been developed for measuring the atmospheric temperature, making use of the temperature dependant characteristics of rotational Raman scattering (RRS) from nitrogen and oxygen, and Rayleigh or Rayleigh-Brillowin scattering (RS or RBS). These methods have various advantages and disadvantages as compared to each other but their potential accuracies are principal characteristics of their efficiency. No systematic attempt has been undertaken so far to compare the efficiences, in the above meaning, of different temperature lidar methods. Two RRS techniques have been compared. Here, we do such a comparison using two methods based on the detection and analysis of RS (RBS) spectra. Four methods are considered here for measuring the atmospheric temperature. One of them (Schwiesow and Lading, 1981) is based on an analysis of the RS linewidth with two Michelson interferometers (MI) in parallel. The second method (Shimisu et al., 1986) employs a high-resolution analysis of the RBS line shape. The third method (Cooney, 1972) employs the temperature dependance of the RRS spectrum envelope. The fourth method (Armstrong, 1974) makes use of a scanning Fabry-Perot interferometer (FPI) as a comb filter for processing the periodic RRS spectrum of the nitrogen. Let us denote the corresponding errors in measuring the temperature by sigma(sub MI), sigma(sub HR), sigma(sub ENV), and sigma(sub FPI). Let us also define the ratios chi(sub 1) = sigma(sub MI)/sigma(sub ENV), chi(sub 2) = sigma(sub HR)/sigma(sub ENV), and chi(sub 3) = sigma(sub FPI)/sigma(sub ENV) interpreted as relative errors with respect to sigma(sub ENV).
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This data package contains data from terrestrial laser scanning (TLS) at five urban park sites in Knoxville, Tennessee, USA. All parks include open-grown and/or closed-canopy trees and mixed nearby land use. These study sites were established as part of the Urban Biogeochemistry Pilot Project, which has an overall goal of better understanding how hydrobiogeochemical cycling is altered within the human environment. These five sites represent a gradient of urbanization, and were instrumented to understand hydrological and biogeochemical cycling (e.g., soil moisture, soil physical properties and biogeochemistry, tree transpiration, species type). The TLS data archived here were collected to provide detailed, three-dimensional information about forest structure. Specifically, data were collected to allow tree- and stand-level characterization of woody structure and leaf area. TLS scans were placed to capture the area around trees with sap flow sensors, and as much of a 50 m radius area around the meteorological station as possible given site property limits. Derived products will allow upscaling of water content and transpiration data. This data package contains the following data: - High-level files document further details of the campaign and data package: 1_CampaignSummary.csv provides details about the campaign and study site, 2_ScanAreasDetail.csv provides details about each separate scan area (groups of scans post-processed into a single point cloud), 3_TerrestrialLidarSensor.csv provides further technical details about the Riegl VZ-400i TLS sensor, TLS_CSV_dd.csv is a CSV Data Dictionary providing information about the fields in CSV files following the ESS-DIVE CSV File Formatting Guidelines Reporting Format, TLS_flmd.csv is a File Level Metadata file providing information about each file in the data package following the ESS-DIVE File Level Metadata Reporting Format, and README.txt is a text file describing the overall project and file structure. - Level 0 data are the raw data (.PROJ folders) as recorded by the Riegl VZ-400i TLS instrument before scan co-registration and post-processing with the Riegl's proprietary RiSCAN PRO software, which requires a license. - Level 1 data contain post-processed, co-registered data from each scan area. The "PointClouds" folder for each scan area contains a .las file with 1 cm resolution point cloud data exported from RiSCAN PRO. These are the main files likely to be of interest to most users and can be further processed with any software capable of manipulating .las files (e.g. Python, R CloudCompare). The "Project Information" folder contains log files from post-processing in RiSCAN PRO that may be of interest to users who want to see detailed records of post-processing, including all PDF reports generated by RiSCAN PRO. The "ScanPositions" folder contains information about the final position of all TLS scans, after post-processing, in multiple formats. The file ScanPositions_*.csv provides final geo-referenced scan positions, and the file SOP_backup_*.csv can be used in RiSCAN PRO to restore the co-registered scan positions if users wish to re-process raw data (Level 0 .PROJ folders) with RiSCAN PRO software (e.g., subsample to a different resolution, exclude a certain scan position, or apply different filters on reflectance or deviation values) without redoing time-consuming co-registration steps.
Cloud condensation nuclei (CCN) are mediators of aerosol–cloud interactions (ACIs), contributing to the largest uncertainties in the understandings of global climate change. We present a novel remote-sensing-based algorithm that quantifies the vertically resolved CCN number concentrations (N CCN ) using aerosol optical properties measured by a multiwavelength lidar. The algorithm considers five distinct aerosol subtypes with bimodal size distributions. The inversion used the lookup tables developed in this study, based on the observations from the Aerosol Robotic Network, to efficiently retrieve optimal particle size distributions from lidar measurements. The method derives dry aerosol optical properties by implementing hygroscopic enhancement factors in lidar measurements. The retrieved optically equivalent particle size distributions and aerosol-type-dependent particle composition are utilized to calculate critical diameters using κ-Köhler theory and N CCN at six supersaturations ranging from 0.07 % to 1.0 %. Sensitivity analyses indicate that uncertainties in extinction coefficients and relative humidity greatly influence the retrieval error in N CCN . The potential of this algorithm is further evaluated by retrieving N CCN using airborne lidar from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign and is validated against simultaneous measurements from the CCN counter. The independent validation with robust correlation demonstrates promising results. Furthermore, the N CCN has been retrieved for the first time using a proposed algorithm from spaceborne lidar – Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) – measurements. The application of this new capability demonstrates the potential for constructing a 3D CCN climatology at a global scale, which helps to better quantify ACI effects and thus reduce the uncertainty in aerosol climate forcing.
Implementation of the radar, lidar and passive optical remote sensing concepts for measuring space debris from an Earth orbiting platform was investigated. Each system was compared for their requirements on the host platform, their performance relative to the space debris measurement program and the estimated cost of developing each concept into a flight instrument. It is found that the radar and lidar systems offer the greatest versatility and accuracy since they control the direction, intensity and duration of the energy incident on the target object. The performance of the passive optical system is determined by the capability of the detector and by the baseline separation of the telescopes, which requires the use of an on-orbit deployment mechanism. The passive optical concept provides the largest total event rae, and includes significant detections of particle sizes greater than 10 cm to allow correlation with ground based observations of the larger particles. The event rate for the radar system is relatively constant with particle while the lidar system is slightly biased toward the smaller sizes. These event rate differences result from a combination of the debris flux size distribution and the variation of sensitive detection area with particle size for the three concepts.
Clouds and aerosols are fundamental regulators of Earth’s radiation budget and climate system, influencing both solar and terrestrial radiation through scattering, absorption, and emission processes. Accurate characterization of their physical and radiative properties from space requires a rigorous understanding of particle single-scattering, gaseous absorption, and radiative transfer in the atmosphere, as well as reliable inversion methods. This review synthesizes the physical foundations and algorithmic implementations of satellite-based passive optical remote sensing of clouds and aerosols, spanning the ultraviolet to thermal infrared spectral range. Beginning with electromagnetic scattering theory and state-of-the-art methods for computing single-scattering by nonspherical particles and computationally efficient methods for accounting for atmospheric absorption, we discuss the radiative transfer framework underpinning cloud and aerosol retrievals. The connection between single-scattering and multiple-scattering is rigorously formulated. We then summarize operational and research-grade retrieval techniques, including cloud masking and thermodynamic phase determination, CO₂ slicing for cloud-top pressure, the Nakajima-King shortwave bi-spectral, and infrared split-window approaches for cloud optical thickness and effective particle size, inversion algorithms for determining aerosol properties from multi-spectral and/or multi-angle radiometric and polarimetric measurements, and active-passive sensing synergy. Examples of the global cloud and aerosol climatologies are illustrated using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging SpectroRadiometer (MISR). Furthermore, the unique strengths of active remote sensing techniques based on spaceborne lidar observations are briefly elaborated in the context of studying ice clouds composed of randomly and horizontally oriented ice crystals, which is a significant challenge for conventional passive remote sensing techniques. By connecting physical theory to practical retrievals, this review highlights both the maturity of current methodologies and the remaining challenges in reducing uncertainties in particle morphology, vertical structure, absorption, and aerosol-cloud interactions. Furthermore, the impact of artificial intelligence (AI) on atmospheric remote sensing is briefly addressed.
The role of lidar in atmospheric studies concerned with radiative energy transfer and remote sensing is summarized. The application of lidar to climatic change investigations is proposed.
Abstract. Wind lidars are widespread and important tools in atmospheric observations. An intrinsic part of lidar measurement error is due to atmospheric variability in the remote-sensing scan volume. This study describes and quantifies the distribution of measurement error due to turbulence in varying atmospheric stability. While the lidar error model is general, we demonstrate the approach using large ensembles of virtual WindCube V2 lidar performing a profiling Doppler-beam-swinging scan in quasi-stationary large-eddy simulations (LESs) of convective and stable boundary layers. Error trends vary with the stability regime, time averaging of results, and observation height. A systematic analysis of the observation error explains dominant mechanisms and supports the findings of the empirical results. Treating the error under a random variable framework allows for informed predictions about the effect of different configurations or conditions on lidar performance. Convective conditions are most prone to large errors (up to 1.5 m s−1 in 1 Hz wind speed in strong convection), driven by the large vertical velocity variances in convective conditions and the high elevation angle of the scanning beams (62∘). Range-gate weighting induces a negative bias into the horizontal wind speeds near the surface shear layer (−0.2 m s−1 in the stable test case). Errors in the horizontal wind speed and direction computed from the wind components are sensitive to the background wind speed but have negligible dependence on the relative orientation of the instrument. Especially during low winds and in the presence of large errors in the horizontal velocity estimates, the reported wind speed is subject to a systematic positive bias (up to 0.4 m s−1 in 1 Hz measurements in strong convection). Vector time-averaged measurements can improve the behavior of the error distributions (reducing the 10 min wind speed error standard deviation to <0.3 m s−1 and the bias to <0.1 m s−1 in strong convection) with a predictable effectiveness related to the number of decorrelated samples in the time window. Hybrid schemes weighting the 10 min scalar- and vector-averaged lidar measurements are shown to be effective at reducing the wind speed biases compared to cup measurements in most of the simulated conditions, with time averages longer than 10 min recommended for best use in some unstable conditions. The approach in decomposing the error mechanisms with the help of the LES flow field could be extended to more complex measurement scenarios and scans.
Quantum sensing promises to revolutionize sensing applications by employing quantum states of light or matter as sensing probes. Photons are the clear choice as quantum probes for remote sensing because they can travel to and interact with a distant target. Existing schemes are mainly based on the quantum illumination framework, which requires quantum memory to store a single photon of an initially entangled pair until its twin reflects off a target and returns for final correlation measurements. Existing demonstrations are limited to tabletop experiments, and expanding the sensing range faces various roadblocks, including long-time quantum storage and photon loss and noise when transmitting quantum signals over long distances. We propose a novel quantum sensing framework that addresses these challenges using quantum frequency combs with path identity for remote sensing of signatures (“qCOMBPASS”). The combination of one key quantum phenomenon and two quantum resources—namely, quantum-induced coherence by path identity, quantum frequency combs, and two-mode squeezed light—allows for quantum remote sensing without requiring quantum memory. The proposed scheme is akin to a quantum radar based on entangled frequency-comb pairs that uses path identity to detect, range, or sense a remote target of interest by measuring pulses of one comb in the pair that never traveled to the target but that contains target information “teleported” by quantum-induced coherence by path identity from the other comb in the pair that traveled to the target but is not detected. We develop the basic qCOMBPASS theory, analyze the properties of the qCOMBPASS transceiver, and introduce the qCOMBPASS equation—a quantum analog of the well-known LIDAR equation in classical remote sensing. We also describe an experimental scheme to demonstrate the concept using two-mode squeezed quantum combs. qCOMBPASS can strongly impact various applications in remote quantum sensing, imaging, metrology, and communications. These applications include detection and ranging of low-reflectivity objects, measurement of small displacements of a remote target with precision beyond the standard quantum limit (SQL), standoff hyperspectral quantum imaging, discreet surveillance from space with low detection probability (detect without being detected), very-long-baseline interferometry, quantum Doppler sensing, quantum clock synchronization, and networks of distributed quantum sensors. Published by the American Physical Society 2024
The convenient availability and simplicity of the Lorenz-Mie theory has resulted in a widespread practice of treating nonspherical particles (especially those in random orientation) as if they were spheres to which Lorenz-Mie results are applicable. However, the assumption of sphericity is rarely made after first having studied the effects of nonsphericity and having concluded that they are negligible. On the contrary, overwhelming evidence suggests that scattering properties of nonspherical particles, including those in random orientation, can significantly differ from those of volume- or surface-equivalent spheres. Hence, the last few decades have demonstrated major research efforts aimed at a significantly better understanding of the effects of particle shape and morphology on electromagnetic scattering. The goal of this presentation is to provide a concise summary of these efforts. The recent availability of theoretical techniques for computing single and multiple scattering of light by realistic polydispersions of spherical and nonspherical particles and the strong dependence of the Stokes scattering matrix on particle size, shape, and refractive index make polarization and depolarization measurements a powerful particle characterization tool. This presentation will focus on recent applications of photopolarimetric and lidar depolarization measurements to remote sensing characterization of tropospheric aerosols, polar stratospheric clouds (PSCs) and contrails. The talk will include (1) a short theoretical overview of the effects of particle microphysics on particle single-scattering characteristics; (2) the use of multi-angle multi-spectral photopolarimetry to retrieve the optical thickness, size distribution, refractive index, and number concentration of tropospheric aerosols over the ocean surface; and (3) the application of the T-matrix method to constraining the PSC and contrail particle microphysics using multi-spectral measurements of lidar backscatter and depolarization
The recent availability of theoretical techniques for computing single and multiple scattering of light by realistic polydispersions of spherical and nonspherical particles and the strong dependence of the Stokes scattering matrix on particle size, shape, and refractive index make polarization and depolarization measurements a powerful particle characterization tool. In this presentation I will describe recent applications of photopolarimetric and lidar depolarization measurements to remote sensing characterization of tropospheric aerosols, polar stratospheric clouds (PSCs), and contrails. The talk will include (1) a short theoretical overview of the effects of particle microphysics on particle single-scattering characteristics; (2) the use of multi-angle multi-spectral photopolarimetry to retrieve the optical thickness, size distribution, refractive index, and number concentration of tropospheric aerosols over the ocean surface; and (3) the application of the T-matrix method to constraining the PSC and contrail particle microphysics using multi-spectral measurements of lidar backscatter and depolarization.
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.
Abstract. Given the importance of constraining cloud droplet number concentrations (Nd) in low-level clouds, we explore two methods for retrieving Nd from surface-based remote sensing that emphasize the information content in lidar measurements. Because Nd is the zeroth moment of the droplet size distribution (DSD), and all remote sensing approaches respond to DSD moments that are at least 2 orders of magnitude greater than the zeroth moment, deriving Nd from remote sensing measurements has significant uncertainty. At minimum, such algorithms require the extrapolation of information from two other measurements that respond to different moments of the DSD. Lidar, for instance, is sensitive to the second moment (cross-sectional area) of the DSD, while other measures from microwave sensors respond to higher-order moments. We develop methods using a simple lidar forward model that demonstrates that the depth to the maximum in lidar-attenuated backscatter (Rmax) is strongly sensitive to Nd when some measure of the liquid water content vertical profile is given or assumed. Knowledge of Rmax to within 5 m can constrain Nd to within several tens of percent. However, operational lidar networks provide vertical resolutions of > 15 m, making a direct calculation of Nd from Rmax very uncertain. Therefore, we develop a Bayesian optimal estimation algorithm that brings additional information to the inversion such as lidar-derived extinction and radar reflectivity near the cloud top. This statistical approach provides reasonable characterizations of Nd and effective radius (re) to within approximately a factor of 2 and 30 %, respectively. By comparing surface-derived cloud properties with MODIS satellite and aircraft data collected during the MARCUS and CAPRICORN II campaigns, we demonstrate the utility of the methodology.