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We provide an overview of the laser absorption spectrometer development that has recently been funded under the Instrument Incubator Program.
In the past 20+ years, NASA Goddard Space Flight Center (GSFC) has successfully developed and flown lidars for mapping of Mars, the Earth, Mercury and the Moon. As laser and electro-optics technologies expand and mature, more sophisticated instruments that once were thought to be too complicated for space are being considered and developed. We will present progress on several new, space-based laser instruments that are being developed at GSFC. These include lidars for remote sensing of carbon dioxide and methane on Earth for carbon cycle and global climate change; sodium resonance fluorescence lidar to measure environmental parameters of the middle and upper atmosphere on Earth and Mars and a wind lidar for Mars orbit; in situ laser instruments include remote and in-situ measurements of the magnetic fields; and a time-of-flight mass spectrometer to study the diversity and structure of nonvolatile organics in solid samples on missions to outer planetary satellites and small bodies.
The overarching objective of this ongoing project is to assess the role of vegetation within climate change. Forests capture carbon, a green house gas, from the atmosphere. Thus, any change, whether, natural (e.g. growth, fire, death) or due to anthropogenic activity (e.g. logging, burning, urbanization) may have a significant impact on the Earth's carbon cycle. Through the use of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and NASA's Laser Vegetation Imaging Sensor (LVIS), which are airborne Light Detection and Ranging (LIDAR) remote sensing technologies, we gather data to estimate the amount of carbon contained in forests and how the content changes over time. UAVSAR and LVIS sensors were sent all over the world with the objective of mapping out terrain to gather tree canopy height and biomass data; This data is in turn used to correlate vegetation with the global carbon cycle around the world.
Laser Remote Sensing provides critical climate science observations necessary to better measure, understand, model and predict the Earth's water, carbon and energy cycles. Laser Remote Sensing applications for studying the Earth and other planets include three dimensional mapping of surface topography, canopy height and density, atmospheric measurement of aerosols and trace gases, plume and cloud profiles, and winds measurements. Beyond the science, data from these missions will produce new data products and applications for a multitude of end users including policy makers and urban planners on local, national and global levels. NASA Missions in formulation including Ice, Cloud, and land Elevation Satellite (ICESat 2) and the Deformation, Ecosystem Structure, and Dynamics of Ice (DESDynI), and future missions such as the Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS), will incorporate the next generation of LIght Detection And Ranging (lidar) instruments to measure changes in the surface elevation of the ice, quantify ecosystem carbon storage due to biomass and its change, and provide critical data on CO 2 in the atmosphere. Goddard's plans for these instruments and potential uses for the resulting data are described below. For the ICESat 2 mission, GSFC is developing a micro-pulse multi-beam lidar. This instrument will provide improved ice elevation estimates over high slope and very rough areas and result in improved lead detection for sea ice estimates. Data about the sea ice and predictions related to sea levels will continue to help inform urban planners as the changes in the polar ice accelerate. DESDynI is planned to be launched in 2017 and includes both lidar and radar instruments. GSFC is responsible for the lidar portion of the DESDynI mission and is developing a scanning laser altimeter that will measure the Earth's topography, the structure of tree canopies, biomass, and surface roughness. The DESDynI lidar will also measure and predict the response of ice masses to climate change and impact on sea level. Data from the lidar will ultimately be fused with radar data products with heretofore unseen results and applications. The 3-D structure of forests is critical to understanding the impact of land use and associated landscape changes on the habitat of life forms and consequently on their biodiversity. Lidar instruments are also under development to measure trace gases in the atmospheric such as CO2 and methane. GSFC is developing an active measurement approach to determine the CO2 column density and surface pressure for the proposed ASCENDS mission. The objective of this approach is to produce data on the amounts of anthropogenic and organic CO2 in the atmosphere with sufficient accuracy to meet the needs of target users including state, federal and international users as well as policy-related legislative, regulatory, and voluntary carbon-related management groups local to international interests. In summary, NASA will continue to rely on laser remote sensing for critical climate science observations and is committed to the development of the next generation of lidar instruments for a range of applications.
Mapping of boreal forest's type, biomass, and other structural parameters are critical for understanding of the boreal forest's significance in the carbon cycle, its response to and impact on global climate change. We believe the nature of the forest structure information available from MISR and GLAS can be used to help identify forest type, age class, and estimate above ground biomass levels beyond that now possible with MODIS alone. The ground measurements will be used to develop relationships between remote sensing observables and forest characteristics and provide new information for understanding forest changes with respect to environmental change. Lidar is a laser altimeter that determines the distance from the instrument to the physical surface by measuring the time elapsed between the pulse emission and the reflected return. Other studies have shown that the returned signal may identify multiple returns originating from trees, building and other objects and permits the calculation of their height. Studies using field data have shown that lidar data can provide estimates of structural parameters such as biomass, stand volume and leaf area index and allows remarkable differentiation between primary and secondary forest. NASA's IceSAT Geoscience Laser Altimeter System (GLAS) was launched in January 2003 and collected data during February and September of that year. This study used data acquired over our study sites in central Siberia to examine the GLAS signal as a source of forest height and other structural characteristics. The purpose of our Siberia project is to improve forest cover maps and produce above-ground biomass maps of the boreal forest in Northern Eurasia from MODIS by incorporating structural information inherent in the Terra MISR and ICESAT Geoscience Laser Altimeter System (GLAS) instruments. A number of forest cover classifications exist for the boreal forest. We believe the limiting factor in these products is the lack of structural information, particularly in the vertical dimension. The emphasis of this project is to improve upon satellite maps of boreal forest structure parameters (i.e. height and biomass) using temporal, multi-angle, and vertical profile information of GLAS data. The existing and near future lidar data is useful for demonstrating these techniques and pursuing current estimates. Future lidar missions may be several years in the future, so we will work other new data sets that may aide in biomass estimates such as ALOS PALSAR We will continue this work to produce an accurate map of current above ground forest phytomass/carbon storage possible for the study area. We plan to develop, test, and integrate remote sensing methods for extracting forest canopy structure measures. We are compiling our field measurements and will compare them with the remote sensing methods where possible. We also be able to produce a realistic error bound on the remotely sensed carbon estimates.
The Global Ecosystems Dynamics Investigation (GEDI) Lidar, is an Earth Science remote sensing instrument aboard the International Space Station (ISS) and the Japanese Experiment Module (JEM). Its core mission is to measure the global carbon balance of Earth's forests by using a set of three solid state laser transmitters in a multibeam waveform capture lidar technique. GEDI's laser transmitters and precision optical system transmits over 3.4 million laser pulses to the Earth every hour, each pulse producing an individual 3-D biomass column measurement. To enable a successful two-year mission, the lasers had to be reliable, highly repeatable in performance with each measurement power cycle, and designed with minimal part count for reduced manufacture complexity and cost. These transmitters are in-house products; developed, constructed, qualified, and fully integrated into the GEDI instrument at NASA's Goddard Space Flight Center. We will present the lasers' path from initial design to flight operation, with emphasis on the major milestones, critical issues, and lessons learned. Full credit goes to the excellent team effort that led to the successful commissioning and initiation of full-time science operations in March 2019.
The boreal forest is the Earth's largest terrestrial biome, covering some 12 million km2 and accounting for about one third of this planet's total forest area. Mapping of boreal forest's type, structure parameters and biomass are critical for understanding the boreal forest's significance in the carbon cycle, its response to and impact on global climate change. Ground based forest inventories, have much uncertainty in the inventory data, particularly in remote areas of Siberia where sampling is sparse and/or lacking. In addition, many of the forest inventories that do exist for Siberia are now a decade or more old. Thus, available forest inventories fail to capture the current conditions. Changes in forest structure in a particular forest-type and region can change significantly due to changing environment conditions, and natural and anthropogenic disturbance. Remote sensing methods can potentially overcome these problems. Multispectral sensors can be used to provide vegetation cover maps that show a timely and accurate geographic distribution of vegetation types rather than decade old ground based maps. Lidar sensors can be used to directly obtain measurements that can be used to derive critical forest structure information (e.g., height, density, and volume). These in turn can used to estimate biomass components using allometric equations without having to use out dated forest inventory. Finally, remote sensing data is ideally suited to provide a sampling basis for a rigorous statistical estimate of the variance and error bound on forest structure measures. In this study, new remote sensing methods were applied to develop estimates timber volume using NASA's MODerate resolution Imaging Spectroradiometer (MODIS) and unique waveform data of the geoscience laser altimeter system (GLAS) for a 10 deg x 10 deg area in central Siberia. Using MODIS and GLAS data, maps were produced for cover type and timber volume for 2003, and a realistic variance (error bound) for timber volume was calculated for the study area. In this 'study we used only GLAS footprints that had a slope value of less than 10 deg. This was done to avoid large errors due to the effect of slope on the GLAS models. The method requires the integration of new remote sensing methods with available ground studies of forest timber volume conducted in Russian forests. The results were compared to traditional ground forest inventory methods reported in the literature and to ground truth collected in the study area.
Satellite instruments have proven especially capable at monitoring the quantity of airborne particles in columns of atmosphere, globally. This chapter describes the principles of satellite measurements and retrieval algorithms, and surveys current instruments and their capabilities. We outline the issues associated with retrieval algorithms, such as surface characterization and aerosol proximity to clouds, and the challenges with interpretation of the results. The relationship between measured aerosol properties and climate-relevant aerosol properties simulated in models is outlined, as well as how measurements are used to evaluate models. Most space-based aerosol instruments are passive sensors that measure reflected sunlight at multiple wavelengths, some at multiple viewing angles. A few are active sensors that send out their own laser light and measure the returned signal. Except when clouds are present, the excess amount of light scattered back to space, beyond that expected from the surface and atmospheric gas, is attributed to aerosol. Satellite measurements are used in many ways in aerosol research. They often provide the only method for monitoring hazardous phenomena such as major wildfire and volcanic eruption plumes, especially in remote areas. Stable, long-term, near-global-scale satellite data records make it possible to identify regional and global aerosol trends. Aerosol radiative effects on climate can be quantified on a near-global scale and used to estimate the strength of aerosol–radiation and aerosol–cloud interactions as well as to evaluate climate model simulations of these interactions. Aerosol-type mapping from satellite imagery is helpful for source attribution, model validation, and to constrain particle light-absorption properties that are essential for radiative forcing calculations. The range of aerosol properties retrieved from satellite observations has grown considerably since the first global estimates of aerosol optical depth (τ a) over ocean were made in the late 1970s. Methods for retrieving particle size and light-absorption properties were explored in the 1990s using multispectral, multi-angle observations, and polarization in visible and near-infrared wavelengths. Sensitivity to particle light absorption, primarily from black or brown carbon content, improved with the inclusion of UV channels, and sensitivity to very thin aerosol layers in the upper troposphere and lower stratosphere was advanced with the use of limb-sounding instruments and active sensors. There are limitations to every measurement technique, including satellite aerosol remote sensing. For wide-swath, passive instruments, aerosol retrievals near clouds can present substantial challenges as far as 15 km away due to cloud-scattered light contaminating the signal. In nearly all cases, retrievals over bright snow and ice surfaces are precluded because surface reflectance uncertainties can overwhelm the aerosol signal. Similarly, meteorological cloud is identified and masked out where possible. Data from passive sensors also lack vertical resolution except those that view toward the limb or where multi-angle imagery is acquired over plumes from wildfires, erupting volcanoes, and wind-blown dust. Yet, passive sensors provide vastly more coverage than the active instruments that mitigate these issues. Particle microphysical information is qualitative from all remote sensing techniques, relying on proxies to infer particle composition, hygroscopicity, and the amount of light-absorbing material. Further, particles smaller than about 200 nm diameter cannot be distinguished from atmospheric gas molecules with remote sensing, which hinders studies of cloud condensation nuclei and their effects on clouds. Most satellite instruments dedicated to aerosol observations are in low-Earth, near-polar, sun-synchronous orbits, which means they cross the equator at the same local time each day. Most are set on cycles that repeat approximately every 16 days, which makes it difficult to monitor aerosol evolution locally. Geostationary satellites make it possible to observe changes occurring from minutes to hours over regions up to 8000 km in size, but lack coverage of high latitudes, and often provide more limited constraints on aerosol properties. Ground-truth data are vital for satellite aerosol-retrieval validation. The AErosol RObotic NETwork (AERONET) of sun photometers was created in 1993 and has become an established global network of over 350 instruments for validating satellite measurements. The network, as well as global networks of ground-based lidars, solar flux radiometers and other sun photometers, are widely used for evaluating global satellite retrievals and model simulations. NASA's Earth Observing System (EOS) program beginning in 1999 led to improvements in reliability, spatial resolution, and spectral resolution (and hence, to improved particle size discrimination and light absorption properties). Satellite payloads include advanced broad-swath and multi-angle imagers, along with the first space-based active sensor focused largely on long-term aerosol monitoring. Since about 2002, Europe's SENTINEL and operational meteorological satellite fleets are also providing sustained aerosol observations, with planned continuation until at least 2030. Satellite remote sensing instruments offer valuable data for evaluating aerosol representations in global climate models. They have been used to assess aerosol optical and physical properties, trends and distributions, and are applied increasingly as direct model constraints in data assimilation to create global aerosol reanalysis products. Aerosol optical depth is the most common quantity adopted for routine model evaluation, including multiwavelength data to loosely constrain particle-size distributions. These evaluations of multiple models have revealed general biases in their regional aerosol amounts and seasonal patterns of transport and removal. Although satellite measurements have near-global coverage, substantial errors can be introduced into the model observation comparison unless attention is paid to spatial and temporal collocation, cloud screening, subgrid-scale variability, and measurement uncertainties that vary with retrieval conditions.