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NASA NTRS2017

Development and Assessment of the SMAP Enhanced Passive Soil Moisture Product

Launched in January 2015, the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) observatory was designed to provide frequent global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using a radar and a radiometer operating at L-band frequencies. Despite a hardware mishap that rendered the radar inoperable shortly after launch, the radiometer continues to operate nominally, returning more than two years of science data that have helped to improve existing hydrological applications and foster new ones.Beginning in late 2016 the SMAP project launched a suite of new data products with the objective of recovering some high-resolution observation capability loss resulting from the radar malfunction. Among these new data products are the SMAP Enhanced Passive Soil Moisture Product that was released in December 2016, followed by the SMAPSentinel-1 Active-Passive Soil Moisture Product in April 2017.This article covers the development and assessment of the SMAP Level 2 Enhanced Passive Soil Moisture Product (L2_SM_P_E). The product distinguishes itself from the current SMAP Level 2 Passive Soil Moisture Product (L2_SM_P) in that the soil moisture retrieval is posted on a 9 km grid instead of a 36 km grid. This is made possible by first applying the Backus-Gilbert optimal interpolation technique to the antenna temperature (TA) data in the original SMAP Level 1B Brightness Temperature Product to take advantage of the overlapped radiometer footprints on orbit. The resulting interpolated TA data then go through various correctioncalibration procedures to become the SMAP Level 1C Enhanced Brightness Temperature Product (L1C_TB_E). The L1C_TB_E product, posted on a 9 km grid, is then used as the primary input to the current operational SMAP baseline soil moisture retrieval algorithm to produce L2_SM_P_E as the final output. Images of the new product reveal enhanced visual features that are not apparent in the standard product. Based on in situ data from core validation sites and sparse networks representing different seasons and biomes all over the world, comparisons between L2_SM_P_E and in situ data were performed for the duration of April 1, 2015 October 30, 2016. It was found that the performance of the enhanced 9 km L2_SM_P_E is equivalent to that of the standard 36 km L2_SM_P, attaining a retrieval uncertainty below 0.040 m(exp 3)/m(exp 3) unbiased root-mean-square error (ubRMSE) and a correlation coefficient above 0.800. This assessment also affirmed that the Single Channel Algorithm using the V-polarized TB channel (SCA-V) delivered the best retrieval performance among the various algorithms implemented for L2_SM_P_E, a result similar to a previous assessment for L2_SM_P.

Enhanced↗

NASA NTRS2021

Using SMAP Level-4 Soil Moisture to Constrain MOD16 Evapotranspiration over the Contiguous USA

Evapotranspiration (ET) is a key hydrologic variable linking the Earth’s water, carbon and energy cycles. At large spatial scales, remote sensing-based (RS) models are often used to quantify ET. Despite the large number of RS ET models available, few include soil moisture as a key environmental input, which can degrade model accuracy and utility. Here, we use model assimilation enhanced soil moisture estimates from the NASA SMAP (Soil Moisture Active Passive) mission as a water supply control in the MOD16 ET algorithm framework. SMAP-derived daily surface (0-5cm depth) and root zone (0-1m depth) soil moisture are used with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation observations, and 4km gridded regional surface meteorology (Gridmet) as primary inputs for estimating daily ET and underlying model soil and stomatal conductance terms. We calibrated the model environmental response parameters using tower eddy covariance ET observations representing major North American biomes. The model ET results were validated using a holdout set of tower observations spanning a large regional climate gradient. The updated ET estimates outperform the baseline MOD16 product across all tower validation sites (RMSE = 0.758 vs 1.108 mm day-1; R2 = 0.68 vs0.45, respectively). Smaller relative improvements were obtained using a recalibrated model with 4km Gridmet meteorology, but no soil moisture control (RMSE = 0.813 mm day-1; R2 = 0.66), indicating that these changes are essential for the improved model performance. The soil moisture-constrained model improvements and relative benefits from the SMAP observations are greater in arid climates, consistent with stronger soil moisture control on ET in water-limited regions. The use of SMAP soil moisture as an additional model constraint improves MOD16 regional performance and provides a new framework for investigating both soil and atmosphere controls on ET.

SMAP↗

NASA NTRS1994

Oceanographic results from analysis of ERS-1 altimetry

Large scale dynamic ocean topography and its variations were observed using ERS-1 radar altimeter measurements. The altimeter measurements analyzed are primarily from the ESA ocean product (OPR02) and from the Interim Geophysical Data Records (IGDR) generated by NOAA from the fast delivery (FD) data during the ERS-1 35 day repeat orbit phase. The precise orbits used for the dynamic topography solution are computed using dual satellite crossover measurements from ERS-1 and TOPEX (Topology Ocean Experiment)/Poseidon (T/P) as additional tracking data, and using improved models and constants which are consistent with T/P. Analysis of the ERS-1 dynamic topography solution indicates agreement with the T/P solution at the 5 cm root mean square level, with regional differences as large as 15 cm tide gauges at the 8 to 9 cm level. There are differences between the ERS-1 OPR02 and IGDR determined dynamic topography solutions on the order of 5 cm root mean square. Mesoscale oceanic variability time series obtained using collinear analysis of the ERS-1 altimeter data show good qualitative agreement when compared with the T/P results.

Tapley, B. D.↗

NASA NTRS2019

Omni-gravity Hydroponics for Space Exploration

As part of the NASA Plant Water Management technology demonstration experiments, a capillary fluidics hydroponic system that can function in a variety of gravity environments has been developed and tested for crop production in space. A passive liquid delivery method is employed that drastically reduces the number of contaminable moving parts providing a high reliability solution requiring minimal resources for operation. The terrestrial, lunar, and Martian environments are managed in a ‘gravity-dominated mode,’ while the low-gravity transit and orbit environments are managed in a ‘capillary fluidics mode,’ where the role of gravity is replaced by the equally passive effects of surface tension, conduit shape, and wettability. The unique considerations for priming, germination, aeration, nutrient supply, root accommodation, layout, crew interaction, etc. are highlighted. Design guides for system function are provided along with high Technology Readiness Level demonstrations of the system during terrestrial and drop tower tests. Long duration tests are planned on short schedule aboard the International Space Station in 2019.

gravity↗

NASA NTRS1962

Jet Interference Effects on a Model of a Single-Engine Four Jet V/STOL Airplane at Mach Numbers from 0.60 to 1.00

An investigation was conducted in the Langley 16-foot transonic tunnel to determine the interference from four exhaust jets on the aerodynamic characteristics of a model of a V/STOL airplane. The single- engine four-jet turbofan power plant of the airplane was simulated by inducing tunnel airflow through two large side inlets and injecting the decomposition products of hydrogen peroxide into the internal flow. The heated gas mixture was exhausted through four nozzles located on the sides of the fuselage under the wing, two near the wing leading edge and two forward of the trailing edge; the nozzles were deflected downward 1.5 deg and outward 5.0 deg to simulate cruise conditions. The wing of the model was a clipped delta with leading-edge sweep of 40 deg, aspect ratio of 3.06, taper ratio of 0.218, thickness-chord ratio of 0.09 at the root and 0.07 at the tip, and 10 deg negative dihedral. Aerodynamic and longitudinal stability coefficients were obtained for the model with the tail removed, and for horizontal-tail incidences of 0 deg and -5 deg. Data were obtained at Mach numbers from 0.60 to 1.00, angles of attack from 0 deg to 12 deg, and with jet total-pressure ratios up to 3.1. Jet operation generally caused a decrease in lift, an increase in pitching-moment coefficient, and a decrease in longitudinal stability at subsonic speeds. The jet interference effects on drag were detrimental at a Mach number of 0.60 and favorable at higher speeds for cruising-flight attitudes.

Schmeer, James W.↗

NASA NTRS2017

Estimating Evaporative Fraction From Readily Obtainable Variables in Mangrove Forests of the Everglades, U.S.A.

A remote-sensing-based model to estimate evaporative fraction (EF) the ratio of latent heat (LE; energy equivalent of evapotranspiration -ET-) to total available energy from easily obtainable remotely-sensed and meteorological parameters is presented. This research specifically addresses the shortcomings of existing ET retrieval methods such as calibration requirements of extensive accurate in situ micro-meteorological and flux tower observations, or of a large set of coarse-resolution or model-derived input datasets. The trapezoid model is capable of generating spatially varying EF maps from standard products such as land surface temperature [T(sub s)] normalized difference vegetation index (NDVI)and daily maximum air temperature [T(sub a)]. The 2009 model results were validated at an eddy-covariance tower (Fluxnet ID: US-Skr) in the Everglades using T(sub s) and NDVI products from Landsat as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results indicate that the model accuracy is within the range of instrument uncertainty, and is dependent on the spatial resolution and selection of end-members (i.e. wet/dry edge). The most accurate results were achieved with the T(sub s) from Landsat relative to the T(sub s) from the MODIS flown on the Terra and Aqua platforms due to the fine spatial resolution of Landsat (30 m). The bias, mean absolute percentage error and root mean square percentage error were as low as 2.9% (3.0%), 9.8% (13.3%), and 12.1% (16.1%) for Landsat-based (MODIS-based) EF estimates, respectively. Overall, this methodology shows promise for bridging the gap between temporally limited ET estimates at Landsat scales and more complex and difficult to constrain global ET remote-sensing models.

trapezoid↗

NASA NTRS2022

Instantaneous Photosynthetically Available Radiation (IPAR) prediction models based on Neural Network for ocean waters.

Instantaneous photosynthetically available radiation (IPAR) at the ocean surface and its vertical profile below the surface play a critical role in models to calculate net primary productivity of marine phytoplankton. In this work, we report two IPAR prediction models based on neural network (NN) approach, one for open ocean and the other for coastal waters. These models are trained, validated, and tested using a large volume of synthetic datasets for open ocean and coastal waters simulated by a radiative transfer model. Our NN models are designed to predict IPAR under a large range of atmospheric and oceanic conditions. The NN models can compute subsurface IPAR profile very accurately up to euphotic zone depth. The root mean square errors associated with the diffuse attenuation coefficient of IPAR are less than 0.011 𝑚−1 and 0.036 𝑚−1 for open ocean and coastal waters respectively. The performance of the NN models is better than presently available semi analytical models, with significant superiority in coastal waters.

PACE↗

NASA NTRS2006

Development of Standardized Lunar Regolith Simulant Materials

Lunar exploration requires studies using standardized testing procedures. Previous lunar simulants focused on physical aspects of the lunar regolith. Renewed lunar exploration requires standardized lunar regolith simulant (SLRS) materials that simulate the physical, chemical, and mineralogical properties of the regolith [l]. A root simulant models an end-member, and a derivative is formed by addition of material [2]. The 2005 Workshop recommended a low-Ti mare basalt and a high-Ca highland anorthosite set of roots. Derivatives involve addition of ilmenite and glassy agglutinates [3]. An ideal SLRS material is homogeneous. Geochemical standards are finely-ground to reduce chemical variability, but lunar simulants have a grain size variation and mineralogy that must match the lunar regolith. The simulant homogeneity is monitored using major, minor, and trace element data of progressively smaller samples compared to bulk material. Both chemical and geotechnical properties depend on the contrast from grain to grain. The variability of simulant material is an inherent property that must be taken into account. Simulant production requires monitoring of adherence to simulant requirements and homogeneity during production. Quality control establishes a traceability to a master set of reference standards.

Carpenter, P.↗

NASA NTRS2008

Potential Use of a Bayesian Network for Discriminating Flash Type from Future GOES-R Geostationary Lightning Mapper (GLM) data

Continuous monitoring of the ratio of cloud flashes to ground flashes may provide a better understanding of thunderstorm dynamics, intensification, and evolution, and it may be useful in severe weather warning. The National Lighting Detection Network TM (NLDN) senses ground flashes with exceptional detection efficiency and accuracy over most of the continental United States. A proposed Geostationary Lightning Mapper (GLM) aboard the Geostationary Operational Environmental Satellite (GOES-R) will look at the western hemisphere, and among the lightning data products to be made available will be the fundamental optical flash parameters for both cloud and ground flashes: radiance, area, duration, number of optical groups, and number of optical events. Previous studies have demonstrated that the optical flash parameter statistics of ground and cloud lightning, which are observable from space, are significantly different. This study investigates a Bayesian network methodology for discriminating lightning flash type (ground or cloud) using the lightning optical data and ancillary GOES-R data. A Directed Acyclic Graph (DAG) is set up with lightning as a "root" and data observed by GLM as the "leaves." This allows for a direct calculation of the joint probability distribution function for the lighting type and radiance, area, etc. Initially, the conditional probabilities that will be required can be estimated from the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) together with NLDN data. Directly manipulating the joint distribution will yield the conditional probability that a lightning flash is a ground flash given the evidence, which consists of the observed lightning optical data [and possibly cloud data retrieved from the GOES-R Advanced Baseline Imager (ABI) in a more mature Bayesian network configuration]. Later, actual GLM and NLDN data can be used to refine the estimates of the conditional probabilities used in the model; i.e., the Bayesian network is a learning network. Methods for efficient calculation of the conditional probabilities (e.g., an algorithm using junction trees), finding data conflicts, goodness of fit, and dealing with missing data will also be addressed.

Solakiewiz, Richard↗

NASA NTRS2023

Fine particulate concentrations over East Asia derived from aerosols measured by the Advanced Himawari Imager using machine learning

Fine particulate matter with a diameter below 2.5 μm (PM 2.5 ) is deleterious to the cardiovascular and respiratory systems. It is often difficult to assess the effects of PM 2.5 on human health over regions with limited ground monitoring sites, especially in East Asia. As an alternative, we estimated near-surface PM 2.5 concentrations by analyzing Advanced Himawari Imager (AHI) Yonsei Aerosol Retrieval (YAER) products. This study incorporates daytime data for East Asia covering the Korean Peninsula, China, Japan, Southeast Asia, and southern Mongolia. We collocated AHI YAER product pixels with meteorological, land-cover, and other ancillary data for the period from March 2018 to February 2019. To estimate PM 2.5 concentrations over wide areas spanning many countries displaying various relationships between aerosol optical depth and PM 2.5 , monthly models were developed by considering both the spatial and temporal characteristics of ground-based PM 2.5 measurements. Random forest machine learning model estimated ground-level mass concentrations of PM 2.5 ; subsequent 10-fold cross validation (CV) yielded a CV R 2 value of 0.81 and a CV root mean squared error (RMSE) of 12.3 μg m -3 . We investigated the spatial pattern of PM 2.5 concentrations over multiple countries and seasonal variation in PM 2.5 concentrations. Diurnal variation of a severe PM 2.5 event in the Korean Peninsula was investigated as a case study. The model captured the extremely heterogeneous spatial distribution of PM 2.5 concentrations peaked around local noon. To measure the capability of the developed model to estimate PM 2.5 concentrations in areas with few in-situ data, its predictive performance was evaluated using a dataset independent of the training process with an R 2 of 0.60 and RMSE of 8.18 μg m −3 . This study demonstrates the potential for satellite-based PM 2.5 estimation for areas with insufficient measuring stations.

Pm2.5↗

NASA NTRS2016

Assessment of the SMAP Passive Soil Moisture Product

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational Level 2 soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 kilometer Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 cubic meter per cubic meter unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 cubic meter per cubic meter.

passive↗

NASA NTRS2000

Controlling Microbial Byproducts using Model-Based Substrate Monitoring and Control Strategies

We have developed a computer-controlled bioreactor system to study various aspects of microbially-mediated nitrogen cycling. The system has been used to investigate methods for controlling microbial denitrification (the dissimilatory reduction of nitrate to N2O and N2) in hydroponic plant growth chambers. Such chambers are key elements of advanced life support systems being designed for use on long duration space missions, but nitrogen use efficiency in them is reduced by denitrification. Control software architecture was designed which permits the heterogeneous control of system hardware using traditional feedback control, and quantitative and qualitative models of various system features. Model-based feed forward control entails prediction of future systems in states and automated regulation of system parameters to achieve desired and avoid undesirable system states. A bacterial growth rate model based on the classic Monod model of saturation kinetics was used to evaluate the response of several individual denitrifying species to varying environmental conditions. The system and models are now being applied to mixed microbial communities harvested from the root zone of a hydroponic growth chamber. The use of a modified Monod organism interaction model was evaluated as a means of achieving more accurate description of the dynamic behavior of the communities. A minimum variance parameter estimation routine was also' used to calibrate the constant parameters in the model by iterative evaluation of substrate (nitrate) uptake and growth kinetics. This representation of processes and interactions aids in the formulation of control laws. The feed forward control strategy being developed will increase system autonomy, reduce crew intervention and limit the accumulation of undesirable waste products (NOx).

Smernoff, David T.↗

NASA NTRS2019

An L-Band Brightness Temperature Disaggregation Method Using S-Band Radiometer Data for the Water Cycle Observation Mission (WCOM)

The Water Cycle Observation Mission (WCOM) will build upon previous L and C band passive microwave soil moisture satellite missions. WCOM will consist of a passive microwave synthetic aperture radiometer operating at L, S, and C bands. The WCOM requirements for passive soil moisture are to estimate soil moisture in the top 5 cm of soil layer with an error less than 0.04 cu m/cu m, at 15 km resolution and with a 3-day revisit. A new set of algorithms for these multi-frequency platforms will need to be developed for estimating the data products at the desired resolution. To accomplish this, a brightness temperature (TB) downscaling methodology is developed that uses passive S-band TB (30 km) to downscale L-band TB (50 km) and to estimate soil moisture at a 30 km resolution, based on the linear relationships between the passive signals of L-band and S-band. To test this downscaling method, analysis was performed using PALS data from the Soil Moisture Experiments in 2002 (SMEX02). For this study, 4 km L-band observations were downscaled to 800 m. The root mean square errors (RMSE) between the downscaled TBL at 800m with the observed TBL at 800m are 2.63 K and 1.60 K for H and V polarizations respectively. The results also showed that it was possible to use these disaggregated TB to estimate soil moisture to meet the mission requirement of 0.04 cu m/cu m. These results showed that we can obtain higher resolution soil moisture from L band passive TB with a high accuracy (<0.04 cu m/cu m) by using S-band information.

Disaggregation↗

NASA NTRS1996

Effect of CO2 levels on nutrient content of lettuce and radish

Atmospheric carbon-dioxide enrichment is known to affect the yield of lettuce and radish grown in controlled environments, but little is known about CO2 enrichment effects on the chemical composition of lettuce and radish. These crops are useful model systems for a Controlled Ecological Life-Support System (CELSS), largely because of their relatively short production cycles. Lettuce (Lactuca sativa L.) cultivar 'Waldmann's Green' and radish (Raphanus sativus L.) cultivar 'Giant White Globe' were grown both in the field and in controlled environments, where hydroponic nutrient solution, light, and temperature were regulated, and where CO2 levels were controlled at 400, 1000, 5000, or 10,000 ppm. Plants were harvested at maturity, dried, and analyzed for proximate composition (protein, fat, ash, and carbohydrate), total nitrogen (N), nitrate N, free sugars, starch, total dietary fiber, and minerals. Total N, protein N, nonprotein N (NPN), and nitrate N generally increased for radish roots and lettuce leaves when grown under growth chamber conditions compared to field conditions. The nitrate-N level of lettuce leaves, as a percentage of total NPN, decreased with increasing levels of CO2 enrichment. The ash content of radish roots and of radish and lettuce leaves decreased with increasing levels of CO2 enrichment. The levels of certain minerals differed between field- and chamber-grown materials, including changes in the calcium (Ca) and phosphorus (P) contents of radish and lettuce leaves, resulting in reduced Ca/P ratio for chamber-grown materials. The free-sugar contents were similar between the field and chamber-grown lettuce leaves, but total dietary fiber content was much higher in the field-grown plant material. The starch content of growth-chamber lettuce increased with CO2 level.

NASA Discipline Life Support Systems↗

NASA NTRS2012

Improving the Accuracy of the AFWA-NASA (ANSA) Blended Snow-Cover Product over the Lower Great Lakes Region

The Air Force Weather Agency (AFWA) -- NASA blended snow-cover product, called ANSA, utilizes Earth Observing System standard snow products from the Moderate- Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to map daily snow cover and snow-water equivalent (SWE) globally. We have compared ANSA-derived SWE with SWE values calculated from snow depths reported at ~1500 National Climatic Data Center (NCDC) co-op stations in the Lower Great Lakes Basin. Compared to station data, the ANSA significantly underestimates SWE in densely-forested areas. We use two methods to remove some of the bias observed in forested areas to reduce the root-mean-square error (RMSE) between the ANSA- and station-derived SWE. First, we calculated a 5- year mean ANSA-derived SWE for the winters of 2005-06 through 2009-10, and developed a five-year mean bias-corrected SWE map for each month. For most of the months studied during the five-year period, the 5-year bias correction improved the agreement between the ANSA-derived and station-derived SWE. However, anomalous months such as when there was very little snow on the ground compared to the 5-year mean, or months in which the snow was much greater than the 5-year mean, showed poorer results (as expected). We also used a 7-day running mean (7DRM) bias correction method using days just prior to the day in question to correct the ANSA data. This method was more effective in reducing the RMSE between the ANSA- and co-op-derived SWE values, and in capturing the effects of anomalous snow conditions.

Hall, Dorothy K.↗

NASA NTRSDate not supplied

Plant Water Management in Microgravity

The NASA Plant Water Management (PWM) technology demonstrations aboard ISS apply recent advances in microgravity capillary fluidics research towards the mundane yet problematic challenges of simply watering plants in space. Plant growth in a low-g environment is often hampered by inadequate aeration and oversaturation of the root zone. The present effort aims to exploit the passive capillary forces of poorly wetting liquids (i.e., contaminated water) within unique system geometries that effectively replace the role of gravity in providing sufficient aeration and hydration for simulated plants. Several flight demonstrations have been completed on ISS, including soil and hydroponic models in single and parallel channel networks. The results demonstrate proof-of-concept, system stability, limits of operation, more. The implications are discussed in relation to plant growth facilities for further near-term microgravity plant science research as well as automated food production for long duration human exploration missions.

microgravity↗

NASA NTRSDate not supplied

Thermal Model of Ice Growth in Vacuum for Lunar Water Production

To support NASA’s goal of sustained lunar presence, the use of in-situ resource utilization will reduce launch weight and frequency of resupply missions. With the discovery of water in the permanently shadowed regions of the moon, the prospect of harvesting water has generated particular interest. A more fundamental understanding of water storage and capture in lunar conditions is necessary for effective and efficient water capture and transport. The present study derives a thermal model for ice height, heat flux, chamber pressure, and mass flow rate in sub-atmospheric pressures below the triple point. The thermal model compares ice layer height against the results of the experiment at two pressures (300 Pa and 500 Pa) and temperatures from -18°C to -3°C. The thermal model predicts the trends seen experimentally with an overall root mean square error of 0.1135 cm and coefficient of efficiency of 0.994, indicating the model predicts the experimental results. The thermal model can be utilized to examine the impacts of varying pressure, temperature, and cold plate area to determine the most effective ice growth conditions for efficient capture of water on the lunar surface. The thermal model will provide a useful tool for NASA in the design of the lunar ice tanker and shows the promise of effective capture water on the lunar surface.

Ice growth↗

NASA NTRSDate not supplied

Thermal Model of Ice Growth in Vacuum for Lunar Water Production

Ice growth↗