Search NASA⌕ Search

SEARCH · Search NASA

Results for “remote data collection”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Mapping wall-to-wall fractional cover of Arctic tundra plant functional types in Alaska using 20-m spatial resolution satellite imagery and harmonized plot observations

Estimates of fractional cover (fCover) across given land surfaces are used to assess, and often model, vegetation composition and diversity, which are crucial for understanding the health and functioning of terrestrial ecosystems. Remote sensing provides a useful means for scaling local, plot-measured fCover estimates to regional scales. Leveraging a recently synthesized and harmonized plot database, this study generated wall-to-wall maps of fCover for six Alaskan-Arctic plant functional types (PFT), including non-vascular plants, forbs, graminoids, and deciduous and evergreen shrubs, using 20-m satellite data (Sentinel-1, Sentinel-2, ArcticDEM) using a machine learning regression approach, specifically the random forest (RF) algorithm, which is well-suited for handling nonlinear relationships and high-dimensional satellite datasets. This study additionally addressed the spatio-temporal inconsistencies e.g., sampling scale, plot size, and collection year in plot measured fCover by adopting a multivariate outlier detection approach—Cook’s distance—to identify high-quality plots for model training and validation. Our approach achieves high accuracy (R 2 = 0.59–0.93, root mean squared errors = 0.02–0.10 for all PFTs) between plot-observed and satellite-derived fCover when using high-quality plot samples. The mapped fCover characterizes the spatial patterns of different PFTs across the tundra biome at a 20-m resolution, providing key information needed for improved representation of Arctic tundra vegetation in terrestrial biosphere models to better understand climate-vegetation feedback across the Arctic tundra.

Arctic tundra↗
NASA NTRS1998

The Application of Reflected GPS Signals to Ocean Remote Sensing

The L-band broadcast signal from the Global Positioning System (GPS) which has reflected off of the sea surface is under study for use as a ocean, coastal and wetlands remote sensing tool. The reflected signal from a given GPS satellite is cross-correlated with the pseudorandom noise code uniquely identifying that satellite. The shape of this cross-correlation, ordinarily a very sharp triangle when tracking a direct line of sight signal, becomes wider and smoother as the mean square slope of the reflecting surface increases. It is proposed that the surface wind speed can be determined by matching the recorded shape of this cross-correlation to that predicted by theoretical models as a function of wind speed and direction. The significance of these effects increases with altitude of the receiver. Experimental data have been collected using a specially modified GPS receiver on aircraft and on a balloon at altitudes of up to 25 km. These data compare favorably with predictions of analytical models and demonstrate the dependence of the waveform shape on surface wind speed and receiver altitude. The advantages that this measurement technique has over conventional scatterometers is the small size, low cost and simplicity of the receiver hardware, no requirement for a transmitter, and the ability to simultaneously collect data from usually 10 or more points (from a low earth orbiting satellite). This number could if the Russian GLONASS (Global Navigation Satellite System) satellites are also considered as additional sources of radiation. Furthermore, the bistatic scattering geometry is complementary to the backscatter used by conventional scatterometers.

Garrison, James L.↗
NASA NTRS2017

A View from Above: Earth Observations

Since the 1960s, satellites have been looking down at the Earth to monitor weather patterns and track severe storms, observe how our land surface is changing and responding to hydrometerological extremes, and even to sense how the Earth's crust is deforming from earthquakes and volcanoes. Space and airborne platforms can provide unique views of the disaster lifecycle, informing pre-event mitigation and preparedness, emergency response following an event, and monitoring longer-term recovery. These remotely-sensed data, products and models can provide a global perspective to see beyond administrative boundaries, reach remote places where in situ observations are di cult or non-existent, and provide the necessary context and situational awareness to aid in disaster response. So how do these platforms work? Instruments aboard satellites use different portions of the electromagnetic spectrum to passively or actively observe energy across a range of wavelengths, which can be turned into meaningful data on geophysical, atmospheric, and hydrological variables. e US has had a broad range of Earth observation (EO) platforms delivering open data for scientific research and societal benefits for decades. e Landsat programme, a joint initiative between the US Geological Survey (USGS) and NASA, has the world's longest continuous collection of space-based satellite imagery of the Earth, extending from 1972 to present. e Landsat satellites provide visible, near infrared, and thermal data that are used to support emergency response and disaster relief by mapping changes in water during floods, and dramatic land surface changes, including those resulting from landslides, wild res, severe weather, volcanic plumes, and dust storms.

FEMA↗
NASA NTRS1997

Remote Sensing of Ocean Color in the High Arctic

With four years of NASA SeaWiFS funding I established a completely new capability and expertise for in-water optical measurements nearly from scratch and with very little optical background. My first-year budget included only capital for a profiling spectral radiometer. Over the next 30 months we conducted six cruises and collected almost 300 optical profiles in challenging environments; many were collected from 21' launches. I also changed institutions during this period: it is very disruptive to move, set up a new lab, and hire and train new people, etc. We also did not have access to NASA funds for almost a year during the move because of difficulties in subcontracting and/or transferring funds. Nevertheless, we delivered data sets from six bio-optical cruises from three high latitude regions, although only two or three cruises from two areas were promised for our SeaWiFS research. The three Canadian Arctic field programs comprise the most comprehensive high latitude bio-optical and biogeochemical data sets in existence. Optical and pigment data from all six cruises have been submitted to NASA and are being included in the algorithm development test set. Additional data are still being submitted.

Cota, G. F.↗
DOE OSTI2024

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC -heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2%, recall of 89.7%, precision of 88.5% and F1-score of 89.2%. Finally, these results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Cognition at the Point of Sensing

Over the last 15 years, compressive sensing techniques have been developed which have the potential to greatly reduce the amount of data collected by systems while preserving the amount of information obtained. A cost of this efficiency is that a computationally-intensive optimization routine must be used to put the sensed data into a form that a person can interpret. At the same time, machine learning techniques have experienced tremendous growth as well. Machines have demonstrated the ability learn how to effectively perform tasks such as detection and classification at speeds much faster than humanly possible. Our goal in this project was to study the feasibility of using compressive sensing systems "at the edge." That is, how can compressive sensing sensors be deployed such that information is created at the remote sensor rather than sending raw data to a central processing location? Studies were performed to analyze whether machine learning could be done on the compressively sensed data in its raw form. If a machine is performing the task, is it possible to do so without putting the data into a human interpretable form? We show that this is possible for some systems, in particular a compressive sensing snapshot imaging spectrometer. Machine learning tasks were demonstrated to be more effective and more robust to noise when the machine learning algorithm worked on data in its raw form. This system is shown to outperform a traditional spectrometer. Techniques for reducing the complexity of the reconstruction routine were also analyzed. Techniques for such as data regularization, deep neural networks, and matrix completion were studied and shown to have benefits over traditional reconstruction techniques. In this project we showed that compressive sensing sensors are indeed feasible at the edge. As always, sensors and algorithms must be carefully tuned to work in the constrained environment. In this project we developed tools and techniques to enable those analyses.

47 OTHER INSTRUMENTATION↗
NASA NTRS1992

An intercomparison of measured and modeled canopy parameters during the variable water-stressed period of FIFE 1989 using an hybrid biosphere-remote sensing model

The measured atmospheric parameters and the fluxes of latent and sensible heat across the biosphere-atmosphere interface collected during the water-stressed period of the First ISLSCP Field Experiment (FIFE) were compared with those calculated by an experimental version of the Biosphere-Atmosphere Transfer Scheme (Ex-BATS). It is shown that the brightness temperature (T(B)) values observed near the surface during FIFE 1987 are closely correlated to in-canopy temperatures calculated by Ex-BATS. The 1987 near-surface observations of T(B) are also well correlated to AVHHR channels 4 and 5 measurements. An inverted form of Ex-BATS was applied to determine the associated required in-canopy temperatures, T(icr), and regressions between T(icr) and T(B) found from the 1987 data were applied to the 1989 observed T(B) at a different site. The T(icr) so estimated showed excellent correlation to the 1989 model calculated T(icr).

Cooper, H. J.↗
NASA NTRS1995

Remote sensing of hydrologic variables in boreal areas, phase 2

During Phase 2 airborne Gamma Radiation measurements were conducted over 48 BOREAS (boreal ecosystem atmosphere study) flight lines. Ground measurements of the soil moisture (SM) of the mineral soil and the depth and water content (WC) of the moss/humus layer were collected for calibration of the flight lines. Special attention has been given to the flight lines over, and near, the primary tower sites in the southern study area (SSA); Old Black Spruce (OBS), Old Jack Pine, Old Aspen, and Young Aspen. Multiple ground surveys (September 1993, July, August, and September 1994) show the variation of the water content of the moss/humus layers and changes in the amount of standing water near the SSA OBS tower during the period September 1993 to September 1994. All ground data, airborne estimates and locations of flight lines and ground sampling points have been submitted to BORIS (BOREAS Information System). On 8-10 September 1994 exceptionally high values of cosmic radiation were observed by the airborne gamma radiation system over the SSA. Follow up investigation has not determined the source of, or what caused, the high cosmic count rates.

Peck, Eugene L.↗
NASA NTRS2018

Polarization Decomposition and Temperature Bias Resolution for SMAP Passive Soil Moisture Retrieval Using Time Series Brightness Temperature Observations

In passive microwave remote sensing of soil moisture, the tau-omega (τ-ω) model has often been used to provide soil moisture estimates at a spatial scale representative of the satellite footprint dimensions. For modeling simplicity, model parameters such as the single scattering albedo (ω) and vegetation opacity (τ) that go into the geophysical inversion process are often assumed to be independent of polarizations. Although this absence of polarization dependence can often be justified in special cases as in low-frequency remote sensing or under dense vegetation conditions, it is not a robust assumption in general. Additional model parameterization errors arising from this assumption are possible, leading to degradation in soil moisture estimation accuracy. In this paper, we propose a time series approach to try to resolve the polarization dependence of several τ-ω model parameters as well as the temperature bias arising from the ancillary temperature data. The Version 4 of the Soil Moisture Active Passive (SMAP) Level 1B brightness temperature time series observations were used to illustrate the mechanics of this approach, with an emphasis on a comparison between resulting satellite soil moisture retrievals and in situ data collected at several core validation sites. It was found that this time series approach resulted in significant reduction of the dry bias exhibited in the current SMAP passive soil moisture data products, while retaining the same performance in other metrics of the current baseline passive soil moisture retrieval algorithm.

time series↗
DOE OSTI2025

Observational ozone datasets over the global oceans and polar regions (version 2024)

Studying tropospheric ozone over the remote areas of the planet, such as the open oceans and the polar regions, is crucial to understand the role of ozone as a global climate forcer and regulator of atmospheric oxidative capacity. A focus on the pristine oceanic and polar regions complements the available land-based datasets and provides insights into key photochemical and depositional loss processes that control the concentrations and spatiotemporal variability in ozone as well as the physicochemical mechanisms driving these patterns. However, an assessment of the role of ozone over the oceanic and polar regions has been hampered by a lack of comprehensive observational datasets. Here, we present the first comprehensive collection of ozone data over the oceans and the polar regions. The overall dataset consists of 77 ship cruises/buoy-based observations and 48 aircraft-based campaigns. The dataset, consisting of more than 630 000 independent ozone measurement data points covering the period from 1977 to 2022 and an altitude range from the surface to 5000 m (with a focus on the lowest 2000 m), allows systematic analyses of the spatiotemporal distribution and long-term trends over the 11 defined ocean/polar regions. The datasets from ships, buoys, and aircraft are complemented by ozonesonde data from 29 launch sites or field campaigns and by 21 non-polar and 17 polar ground-based station datasets. The datasets contain information on how long the observed air masses were isolated from land, as estimated by backward trajectories from the individual observation points. To extract observations representative of oceanic conditions, we recommend using a subset of the data with an isolation time of 72 h or longer, from the analysis with coincident radon observations. These filtered oceanic and polar data showed typically flat diurnal cycles at high latitudes, whereas daytime decreases in ozone (11 %–16 %) were observed at lower latitudes. The ship/buoy- and aircraft-based datasets presented here will supplement the land-based ones in the TOAR-II (Tropospheric Ozone Assessment Report Phase II) database to provide a fully global assessment of tropospheric ozone. The described dataset is available at https://doi.org/10.17596/0004044 (Kanaya et al., 2025).

Kanaya, Yugo [Japan Agency for Marine-Earth Scienc↗
NASA NTRS1971

A survey of multispectral remote sensing techniques for earth resources applications.

Remote sensing for earth resources applications has as its objective the collection and interpretation of information on all environmentally significant features of the atmosphere, oceans, and land surfaces. This information is most effectively gathered by airborne and satellite borne imaging systems. These systems operate at a variety of wavelengths extending from the visual spectrum, through the infrared and into the centimeter wavelengths of the microwave band. A comparison is made of the signal characteristics at these spectrum regions. In particular, the ability of microwave radiometric imaging to augment visual and infrared imaging is discussed. Recent developments in electronically scanned microwave radiometric sensors are described, together with methods of optimizing microwave data display. Results achieved with integrated display systems for both microwave and optical data are presented, together with projections for future applications.-

Pascalar, H. G.↗
NASA NTRS1990

Delivery and application of precise timing for a traveling wave powerline fault locator system

The Bonneville Power Administration (BPA) has successfully operated an in-house developed powerline fault locator system since 1986. The BPA fault locator system consists of remotes installed at cardinal power transmission line system nodes and a central master which polls the remotes for traveling wave time-of-arrival data. A power line fault produces a fast rise-time traveling wave which emanates from the fault point and propagates throughout the power grid. The remotes time-tag the traveling wave leading edge as it passes through the power system cardinal substation nodes. A synchronizing pulse transmitted via the BPA analog microwave system on a wideband channel sychronizes the time-tagging counters in the remote units to a different accuracy of better than one microsecond. The remote units correct the raw time tags for synchronizing pulse propagation delay and return these corrected values to the fault locator master. The master then calculates the power system disturbance source using the collected time tags. The system design objective is a fault location accuracy of 300 meters. BPA's fault locator system operation, error producing phenomena, and method of distributing precise timing are described.

Street, Michael A.↗
DOE OSTI2021

Assimilation of citizen science data in snowpack modeling using a new snow data set: Community Snow Observations

A physically based snowpack evolution and redistribution model was used to test the effectiveness of assimilating crowd-sourced snow depth measurements collected by citizen scientists. The Community Snow Observations project gathers, stores, and distributes measurements of snow depth recorded by recreational users and snow professionals in high mountain environments. These citizen science measurements are valuable since they come from terrain that is relatively undersampled and can offer in situ snow information in locations where snow information is sparse or nonexistent. The present study investigates (1) the improvements to model performance when citizen science measurements are assimilated, and (2) the number of measurements necessary to obtain those improvements. Model performance is assessed by comparing time series of observed (snow pillow) and modeled snow water equivalent values, by comparing spatially distributed maps of observed (remotely sensed) and modeled snow depth, and by comparing fieldwork results from within the study area. The results demonstrate that few citizen science measurements are needed to obtain improvements in model performance, and these improvements are found in 62 % to 78 % of the ensemble simulations, depending on the model year. Model estimations of total water volume from a subregion of the study area also demonstrate improvements in accuracy after CSO measurements have been assimilated. These results suggest that even modest measurement efforts by citizen scientists have the potential to improve efforts to model snowpack processes in high mountain environments, with implications for water resource management and process-based snow modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

In this study, we develop a machine-learning (ML)-enabled strategy for selecting hillslope-scale ecohydrological monitoring sites within snow-dominated mountainous watersheds, with a particular focus on snow-soil–plant interactions. Data layers rely on spatial data layers from both remote sensing and hydrological model simulations. Specifically, a Landsat-based foresummer drought sensitivity index is used to define the dependency of the annual peak plant productivity on the Palmer drought severity index in the early growing season. Hydrological simulations provide the spatiotemporal dynamics of near-surface soil moisture and snow depth. In this framework, a regression analysis identifies the key hydrological variables relevant to the spatial heterogeneity of drought sensitivity. We then apply unsupervised clustering to these key variables, using the Gaussian mixture model, to group hillslopes into several zones that have divergent relationships regarding soil moisture, snow dynamics, and drought sensitivity. Using the datasets collected in the East River Watershed (Crested Butte, Colorado, United States), results show that drought sensitivity is significantly correlated with model-derived soil moisture and snow-free timing over space and time. The relationship is, however, non-linear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpacks, lateral flow, and soil storage limitations. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as the mid-elevation regions where sensitivity is associated with the topographic aspect and net potential radiation. In addition, the algorithm identifies the most representative hillslopes with road/trail access within each zone for installing monitoring sites. Our method also aims to significantly increase the use of ML and model-simulation results to guide critical zone and watershed monitoring activities.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1983

NASA/RAE collaboration on nonlinear control using the F-8C digital fly-by-wire aircraft

A cooperative advanced digital research experiment (CADRE) was established by the National Aeronautics and Space Administration (NASA) and the Royal Aircraft Establishment (RAE), in which nonlinear control algorithms developed by the RAE were tested on the F-8C digital fly-by-wire (DFBW) aircraft based at the Dryden Flight Research Facility. In the initial phase of the collaboration, some variable-gain algorithms, referred to collectively as variable integral control to optimize response (VICTOR) algorithms, were fight tested. With VICTOR, various measures available within the control system are used to vary gains and time-constants within the closed loop and thereby enhance the control capability of the system, while reducing the adverse effects of sensor noise on the control surfaces. A review of design procedures for VICTOR and results of preliminary flight tests are presented. the F-8C aircraft is operated in the remotely augmented vehicle (RAV) mode, with the control laws implemented as FORTRAN programs on a ground-based computer. Pilot commands and sensor information are telemetered to the ground, where the data are processed to form surface commands which are then telemetered to the ground, where the data are processed to form surface commands which are then telemetered back to the aircraft. The RAV mode represents a single-string (simplex) system and is therefore vulnerable to a hardover since comparison monitoring is not possible. Hence, extensive error checking is conducted on both the ground and airborne computers to prevent the development of potentially hazardous situations. Experience with the RAV monitoring and validation procedures is described.

Butler, G. F.↗
NASA NTRS1984

Improving crop classification through attention to the timing of airborne radar acquisitions

Radar remote sensors may provide valuable input to crop classification procedures because of (1) their independence of weather conditions and solar illumination, and (2) their ability to respond to differences in crop type. Manual classification of multidate synthetic aperture radar (SAR) imagery resulted in an overall accuracy of 83 percent for corn, forest, grain, and 'other' cover types. Forests and corn fields were identified with accuracies approaching or exceeding 90 percent. Grain fields and 'other' fields were often confused with each other, resulting in classification accuracies of 51 and 66 percent, respectively. The 83 percent correct classification represents a 10 percent improvement when compared to similar SAR data for the same area collected at alternate time periods in 1978. These results demonstrate that improvements in crop classification accuracy can be achieved with SAR data by synchronizing data collection times with crop growth stages in order to maximize differences in the geometric and dielectric properties of the cover types of interest.

Brisco, B.↗
NASA NTRS2016

The Characterization of a DIRSIG Simulation Environment to Support the Inter-Calibration of Spaceborne Sensors

Climate change studies require long-term, continuous records that extend beyond the lifetime, and the temporal resolution, of a single remote sensing satellite sensor. The inter-calibration of spaceborne sensors is therefore desired to provide spatially, spectrally, and temporally homogeneous datasets. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool is a first principle-based synthetic image generation model that has the potential to characterize the parameters that impact the accuracy of the inter-calibration of spaceborne sensors. To demonstrate the potential utility of the model, we compare the radiance observed in real image data to the radiance observed in simulated image from DIRSIG. In the present work, a synthetic landscape of the Algodones Sand Dunes System is created. The terrain is facetized using a 2-meter digital elevation model generated from NASA Goddard's LiDAR, Hyperspectral, and Thermal (G-LiHT) imager. The material spectra are assigned using hyperspectral measurements of sand collected from the Algodones Sand Dunes System. Lastly, the bidirectional reflectance distribution function (BRDF) properties are assigned to the modeled terrain using the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF product in conjunction with DIRSIG's Ross-Li capability. The results of this work indicate that DIRSIG is in good agreement with real image data. The potential sources of residual error are identified and the possibilities for future work are discussed..

Ambeau, Brittany L.↗
NASA NTRS2014

Connecting Returned Apollo Soils and Remote Sensing: Application to the Diviner Lunar Radiometer

The Diviner Lunar Radiometer, onboard NASA's Lunar Reconnaissance Orbiter, has produced the first global, high resolution, thermal infrared observations of an airless body. The Moon, which is the most accessible member of this most abundant class of solar system objects, is also the only body for which we have extraterrestrial samples with known spatial context, returned Apollo samples. Here we present the results of a comprehensive study to reproduce an accurate simulated lunar environment, evaluate the most appropriate sample and measurement conditions, collect thermal infrared spectra of a representative suite of Apollo soils, and correlate them with Diviner observations of the lunar surface. It has been established previously that thermal infrared spectra measured in simulated lunar environment (SLE) are significantly altered from spectra measured under terrestrial or martian conditions. The data presented here were collected at the University of Oxford Simulated Lunar Environment Chamber (SLEC). In SLEC, we simulate the lunar environment by: (1) pumping the chamber to vacuum pressures (less than 10‐4 mbar) sufficient to simulate lunar heat transport processes within the sample, (2) cooling the chamber with liquid nitrogen to simulate radiation to the cold space environment, and (3) heating the samples with heaters and lamp to set‐up thermal gradients similar to those experienced in the upper hundreds of microns of the lunar surface. We then conducted a comprehensive suite of experiments using different sample preparation and heating conditions on Apollo soils 15071 (maria) and 67701 (highland) and compared the results to Diviner noontime data to select the optimal experimental conditions. This study includes thermal infrared SLE measurements of 10084 (A11 - LM), 12001 (A12 - LM), 14259 (A14 - LM), 15071 (A15 - S1), 15601 (A15 - S9a), 61141 (A16 - S1), 66031 (A16 - S6), 67701 (A16 - S11), and 70181 (A17 - LM). The Diviner dataset includes all six Apollo sites at approximately 200 m spatial resolution We find that analyses of Diviner observations of individual sampling stations and SLE measurements returned Apollo soils show good agreement, while comparisons to thermal infrared reflectance under ambient conditions do not agree well, which underscores the need for SLE measurements and validates the Diviner compositional measurement technique.

Greenhagen, B. T.↗