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NASA NTRS2021

Bhutan Agriculture: Developing a Crop Mask for Rice and Creating a Data Collection Protocol Utilizing Remotely Sensed Data in Bhutan

Rice cultivation in Bhutan has been increasingly threatened by deteriorating soil health and outbreaks of diseases and pests associated with the global change in climate patterns. Field surveys, which the national government of Bhutan has relied on to monitor remote agricultural lands, are becoming increasingly overwhelmed by growing threats to agricultural health. To address these concerns, NASA DEVELOP partnered with the Department of Agriculture of Bhutan, the Bhutan Foundation, and the Ugyen Wangchuck Institute of Conservation and Environmental Research (UWICER) and worked to increase the government of Bhutan’s agricultural monitoring capacity. Utilizing Earth observations including Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), Shuttle Radar Topography Mission (SRTM), and Planet imagery, the DEVELOP team worked with NASA SERVIR and created a sampling protocol to identify rice plantations and supplement field surveys for more efficient agriculture monitoring. The analysis focused on districts Paro, Punakha, Samtse, Sarpang, Trongsa, Zhemgang, Wangdue Phodrang, and Samdrup Jongkhar in the year 2020 during the period of transplantation (June) to harvesting of rice (November). The team provided the partners with a sampling protocol for integrating NASA Earth observations into their crop monitoring methods, as well as a crop mask for rice identification and to aid crop management. The crop mask for rice was developed using the Random Forest (RF) classifier for the eight districts of Bhutan. Visually, the random forest model has proved to be more accurate and precise than the classification and Regression Tree model. Statistically, the Random Forest model was 91.8% accurate in identifying rice in Bhutan.

Yeshey Seldon↗
NASA NTRS2007

Physically-Retrieving Cloud and Thermodynamic Parameters from Ultraspectral IR Measurements

A physical inversion scheme has been developed, dealing with cloudy as well as cloud-free radiance observed with ultraspectral infrared sounders, to simultaneously retrieve surface, atmospheric thermodynamic, and cloud microphysical parameters. A fast radiative transfer model, which applies to the clouded atmosphere, is used for atmospheric profile and cloud parameter retrieval. A one-dimensional (1-d) variational multi-variable inversion solution is used to improve an iterative background state defined by an eigenvector-regression-retrieval. The solution is iterated in order to account for non-linearity in the 1-d variational solution. It is shown that relatively accurate temperature and moisture retrievals can be achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud top level are obtained. For both optically thin and thick cloud situations, the cloud top height can be retrieved with relatively high accuracy (i.e., error < 1 km). NPOESS Airborne Sounder Testbed Interferometer (NAST-I) retrievals from the Atlantic-THORPEX Regional Campaign are compared with coincident observations obtained from dropsondes and the nadir-pointing Cloud Physics Lidar (CPL). This work was motivated by the need to obtain solutions for atmospheric soundings from infrared radiances observed for every individual field of view, regardless of cloud cover, from future ultraspectral geostationary satellite sounding instruments, such as the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) and the Hyperspectral Environmental Suite (HES). However, this retrieval approach can also be applied to the ultraspectral sounding instruments to fly on Polar satellites, such as the Infrared Atmospheric Sounding Interferometer (IASI) on the European MetOp satellite, the Cross-track Infrared Sounder (CrIS) on the NPOESS Preparatory Project and the following NPOESS series of satellites.

Zhou, Daniel K.↗
DOE OSTI2020

Radiation Mapping for an Unmanned Aerial Vehicle: Development and Simulated Testing of Algorithms for Source Mapping and Navigation Path Generation

Image reconstruction algorithms were developed for radiation source mapping and used for generating the search path of a moving radiation detector, such as one onboard an unmanned aerial vehicle. Simulations consisted of first assuming radioactive sources of varying complexity and estimating the radiation fields that would then be produced by that source distribution. Next, the "measurements" that would result from a pair of adjacent spatial locations were computed. A crude estimate of the source distribution likely to have produced such "measurements" was reconstructed based upon the limited measurements. Location of the next "measurement" was then determined as halfway between the location of the estimated source and the current "measurement." With each additional sample, improved source distribution reconstructions were made and used to inform the immediate direction of detector motion. Source reconstruction or mapping was formulated as an inverse problem solved with either maximum a posteriori or least squares (LS) regression deconvolution methods. Different amounts of noise were added to the simulated "measurements," allowing evaluation of the methods' performances as functions of signal-to-noise ratio of the measured map. As expected, methods that promote sparsity were better suited in reconstructing point sources. Reliable prior information of the source distribution also improved the reconstruction results, especially with distributed sources. With a non-negative least square algorithm and the suggested paths it generated, location of sources was successfully estimated to an accuracy of 0.014 m within nine iterations in a single-source scenario and 12 iterations in a two-source scenario, given a 10% error on the integrated counts and a Poisson distribution of the noise associated with the measured counts.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS1993

Streak camera based SLR receiver for two color atmospheric measurements

To realize accurate two-color differential measurements, an image digitizing system with variable spatial resolution was designed, built, and integrated to a photon-counting picosecond streak camera, yielding a temporal scan resolution better than 300 femtosecond/pixel. The streak camera is configured to operate with 3 spatial channels; two of these support green (532 nm) and uv (355 nm) while the third accommodates reference pulses (764 nm) for real-time calibration. Critical parameters affecting differential timing accuracy such as pulse width and shape, number of received photons, streak camera/imaging system nonlinearities, dynamic range, and noise characteristics were investigated to optimize the system for accurate differential delay measurements. The streak camera output image consists of three image fields, each field is 1024 pixels along the time axis and 16 pixels across the spatial axis. Each of the image fields may be independently positioned across the spatial axis. Two of the image fields are used for the two wavelengths used in the experiment; the third window measures the temporal separation of a pair of diode laser pulses which verify the streak camera sweep speed for each data frame. The sum of the 16 pixel intensities across each of the 1024 temporal positions for the three data windows is used to extract the three waveforms. The waveform data is processed using an iterative three-point running average filter (10 to 30 iterations are used) to remove high-frequency structure. The pulse pair separations are determined using the half-max and centroid type analysis. Rigorous experimental verification has demonstrated that this simplified process provides the best measurement accuracy. To calibrate the receiver system sweep, two laser pulses with precisely known temporal separation are scanned along the full length of the sweep axis. The experimental measurements are then modeled using polynomial regression to obtain a best fit to the data. Data aggregation using normal point approach has provided accurate data fitting techniques and is found to be much more convenient than using the full rate single shot data. The systematic errors from this model have been found to be less than 3 ps for normal points.

Varghese, Thomas K.↗
NASA NTRS2016

A Quantum Annealing Computer Team Addresses Climate Change Predictability

The near confluence of the successful launch of the Orbiting Carbon Observatory2 on July 2, 2014 and the acceptance on August 20, 2015 by Google, NASA Ames Research Center and USRA of a 1152 qubit D-Wave 2X Quantum Annealing Computer (QAC), offered an exceptional opportunity to explore the potential of this technology to address the scientific prediction of global annual carbon uptake by land surface processes. At UMBC,we have collected and processed 20 months of global Level 2 light CO2 data as well as fluorescence data. In addition we have collected ARM data at 2sites in the US and Ameriflux data at more than 20 stations. J. Dorband has developed and implemented a multi-hidden layer Boltzmann Machine (BM) algorithm on the QAC. Employing the BM, we are calculating CO2 fluxes by training collocated OCO-2 level 2 CO2 data with ARM ground station tower data to infer to infer measured CO2 flux data. We generate CO2 fluxes with a regression analysis using these BM derived weights on the level 2 CO2 data for three Ameriflux sites distinct from the ARM stations. P. Gentine has negotiated for the access of K34 Ameriflux data in the Amazon and is applying a neural net to infer the CO2 fluxes. N. Talik validated the accuracy of the BM performance on the QAC against a restricted BM implementation on the IBM Softlayer Cloud with the Nvidia co-processors utilizing the same data sets. G. Nearing and K. Harrison have extended the GSFC LIS model with the NCAR Noah photosynthetic parameterization and have run a 10 year global prediction of the net ecosystem exchange. C. Pellisier is preparing a BM implementation of the Kalman filter data assimilation of CO2 fluxes. At UMBC, R. Prouty is conducting OSSE experiments with the LISNoah model on the IBM iDataPlex to simulate the impact of CO2 fluxes to improve the prediction of global annual carbon uptake. J. LeMoigne and D. Simpson have developed a neural net image registration system that will be used for MODIS ENVI and will be converted to a BM algorithm implementation on the QAC. The first integer adder has been implemented on the D-Wave 2X by A. Shehab that will perform HAAR wavelets for image compression of MODIS scenes. Finally, based on the next generations of QACs, we are preparing a 5-year performance road map on the scalability of the current QAC algorithms.

Science Data Processing↗
NASA NTRS2008

Radiometric Modeling and Calibration of the Geostationary Imaging Fourier Transform Spectrometer (GIFTS)Ground Based Measurement Experiment

The ultimate remote sensing benefits of the high resolution Infrared radiance spectrometers will be realized with their geostationary satellite implementation in the form of imaging spectrometers. This will enable dynamic features of the atmosphere s thermodynamic fields and pollutant and greenhouse gas constituents to be observed for revolutionary improvements in weather forecasts and more accurate air quality and climate predictions. As an important step toward realizing this application objective, the Geostationary Imaging Fourier Transform Spectrometer (GIFTS) Engineering Demonstration Unit (EDU) was successfully developed under the NASA New Millennium Program, 2000-2006. The GIFTS-EDU instrument employs three focal plane arrays (FPAs), which gather measurements across the long-wave IR (LWIR), short/mid-wave IR (SMWIR), and visible spectral bands. The GIFTS calibration is achieved using internal blackbody calibration references at ambient (260 K) and hot (286 K) temperatures. In this paper, we introduce a refined calibration technique that utilizes Principle Component (PC) analysis to compensate for instrument distortions and artifacts, therefore, enhancing the absolute calibration accuracy. This method is applied to data collected during the GIFTS Ground Based Measurement (GBM) experiment, together with simultaneous observations by the accurately calibrated AERI (Atmospheric Emitted Radiance Interferometer), both simultaneously zenith viewing the sky through the same external scene mirror at ten-minute intervals throughout a cloudless day at Logan Utah on September 13, 2006. The accurately calibrated GIFTS radiances are produced using the first four PC scores in the GIFTS-AERI regression model. Temperature and moisture profiles retrieved from the PC-calibrated GIFTS radiances are verified against radiosonde measurements collected throughout the GIFTS sky measurement period. Using the GIFTS GBM calibration model, we compute the calibrated radiances from data collected during the moon tracking and viewing experiment events. From which, we derive the lunar surface temperature and emissivity associated with the moon viewing measurements.

Tian, Jialin↗
DOE OSTI2021

Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning

Arsenic, a potent carcinogen and neurotoxin, affects over 200 million people globally. Current detection methods are laborious, expensive, and unscalable, being difficult to implement in developing regions and during crises such as COVID-19. This study attempts to determine if a relationship exists between soil’s hyperspectral data and arsenic concentration using NASA’s Hyperion satellite. It is the first arsenic study to use satellite-based hyperspectral data and apply a classification approach. Four regression machine learning models are tested to determine this correlation in soil with bare land cover. Raw data are converted to reflectance, problematic atmospheric influences are removed, characteristic wavelengths are selected, and four noise reduction algorithms are tested. The combination of data augmentation, Genetic Algorithm, Second Derivative Transformation, and Random Forest regression (R 2 =0.840 and normalized root mean squared error (re-scaled to [0,1]) = 0.122) shows strong correlation, performing better than past models despite using noisier satellite data (versus lab-processed samples). Three binary classification machine learning models are then applied to identify high-risk shrub-covered regions in ten U.S. states, achieving strong accuracy (=0.693) and F1-score (=0.728). Overall, these results suggest that such a methodology is practical and can provide a sustainable alternative to arsenic contamination detection.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
NASA NTRS1995

Low velocity opposed-flow frame spread in a transport-controlled environment DARTFire

The overall objectives of the DARTFire project are to uncover the underlying physics and increase understanding of the mechanisms that cause flames to propagate over solid fuels against a low velocity of oxidizer flow in a low-gravity environment. Specific objectives are (1) to analyze experimentally observed flame shapes, measured gas-phase field variables, spread rates, radiative characteristics, and solid-phase regression rates for comparison with previously developed model prediction capability that will be continually extended, and (2) to investigate the transition from ignition to either flame propagation or extinction in order to determine the characteristics of those environments that lead to flame evolution. To meet the objectives, a series of sounding rocket experiments has been designed to exercise several of the dimensional, controllable variables that affect the flame spread process over PMMA in microgravity, i.e., the opposing flow velocity (1-20 cm/s), the external radiant flux directed to the fuel surface (0-2 W/cm(exp 2)), and the oxygen concentration of the environment (35-70%). Because radiative heat transfer is critical to these microgravity flame spread experiments, radiant heating is imposed, and radiant heat loss will be measured. These are the first attempts at such an experimental control and measurement in microgravity. Other firsts associated with the experiment are (1) the control of the low velocity, opposed flow, which is of the same order as diffusive velocities and Stefan flows; (2) state-of-the-art quantitative flame imaging for species-specific emissions (both infrared and ultraviolet) in addition to novel intensified array imaging to obtain a color image of the very dim, low-gravity flames.

West, Jeff↗
DOE OSTI2023

A review on recent machine learning applications for imaging mass spectrometry studies

Imaging mass spectrometry (IMS) is a powerful analytical technique widely used in biology, chemistry, and materials science fields that continue to expand. IMS provides a qualitative compositional analysis and spatial mapping with high chemical specificity. The spatial mapping information can be 2D or 3D depending on the analysis technique employed. Due to the combination of complex mass spectra coupled with spatial information, large high-dimensional datasets (hyperspectral) are often produced. Therefore, the use of automated computational methods for an exploratory analysis is highly beneficial. The fast-paced development of artificial intelligence (AI) and machine learning (ML) tools has received significant attention in recent years. These tools, in principle, can enable the unification of data collection and analysis into a single pipeline to make sampling and analysis decisions on the go. There are various ML approaches that have been applied to IMS data over the last decade. Here, in this review, we discuss recent examples of the common unsupervised (principal component analysis, non-negative matrix factorization, k-means clustering, uniform manifold approximation and projection), supervised (random forest, logistic regression, XGboost, support vector machine), and other methods applied to various IMS datasets in the past five years. The information from this review will be useful for specialists from both IMS and ML fields since it summarizes current and representative studies of computational ML-based exploratory methods for IMS.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

High-Resolution PM2.5 Concentrations Estimation Based on Stacked Ensemble Learning Model Using Multi-Source Satellite TOA Data

Nepal has experienced severe fine particulate matter (PM2.5) pollution in recent years. However, few studies have focused on the distribution of PM2.5 and its variations in Nepal. Although many researchers have developed PM2.5 estimation models, these models have mainly focused on the kilometer scale, which cannot provide accurate spatial distribution of PM2.5 pollution. Based on Gaofen-1/6 and Landsat-8/9 satellite data, we developed a stacked ensemble learning model (named XGBLL) combined with meteorological data, ground PM2.5 concentrations, ground elevation, and population data. The model includes two layers: a XGBoost and Light GBM model in the first layer, and a linear regression model in the second layer. The accuracy of XGBLL model is better than that of a single model, and the fusion of multi-source satellite remote sensing data effectively improves the spatial coverage of PM2.5 concentrations. Besides, the spatial distribution of the daily mean PM2.5 concentrations in the Kathmandu region under different air conditions was analyzed. The validation results showed that the monthly averaged dataset was accurate (R2 = 0.80 and root mean square error = 7.07). In addition, compared to previous satellite PM2.5 datasets in Nepal, the dataset produced in this study achieved superior accuracy and spatial resolution.

Environmental Sciences & Ecology↗
DOE OSTI2021

Adapting Secure MultiParty Computation to Support Machine Learning in Radio Frequency Sensor Networks

In this project we developed and validated algorithms for privacy-preserving linear regression using a new variant of Secure Multiparty Computation (MPC) we call "Hybrid MPC" (hMPC). Our variant is intended to support low-power, unreliable networks of sensors with low-communication, fault-tolerant algorithms. In hMPC we do not share training data, even via secret sharing. Thus, agents are responsible for protecting their own local data. Only the machine learning (ML) model is protected with information-theoretic security guarantees against honest-but-curious agents. There are three primary advantages to this approach: (1) after setup, hMPC supports a communication-efficient matrix multiplication primitive, (2) organizations prevented by policy or technology from sharing any of their data can participate as agents in hMPC, and (3) large numbers of low-power agents can participate in hMPC. We have also created an open-source software library named "Cicada" to support hMPC applications with fault-tolerance. The fault-tolerance is important in our applications because the agents are vulnerable to failure or capture. We have demonstrated this capability at Sandia's Autonomy New Mexico laboratory through a simple machine-learning exercise with Raspberry Pi devices capturing and classifying images while flying on four drones.

42 ENGINEERING↗
DOE OSTI2022

From Lake to River: Documenting an Environmental Transition Across the Jura/Knockfarril Hill Members Boundary in the Glen Torridon Region of Gale Crater (Mars)

Between January 2019 and January 2021, the Mars Science Laboratory team explored the Glen Torridon (GT) region in Gale crater (Mars), known for its orbital detection of clay minerals. Mastcam, Mars Hand Lens Imager, and ChemCam data are used in an integrated sedimentological and geochemical study to characterize the Jura member of the upper Murray formation and the Knockfarril Hill member of the overlying Carolyn Shoemaker formation in northern GT. The studied strata show a progressive transition represented by interfingering beds of fine–grained, recessive mudstones of the Jura member and coarser–grained, cross–stratified sandstones attributed to the Knockfarril Hill member. Whereas the former are interpreted as lacustrine deposits, the latter are interpreted as predominantly fluvial deposits. The geochemical composition seen by the ChemCam instrument show K 2 O–rich mudstones (~1–2 wt.%) versus MgO–rich sandstones (>6 wt.%), relative to the average composition of the underlying Murray formation. We document consistent sedimentary and geochemical data sets showing that low–energy mudstones of the Jura member are associated with the K–rich endmember, and that high–energy cross–stratified sandstones of the Knockfarril Hill member are associated with the Mg–rich endmember, regardless of stratigraphic position. The Jura to Knockfarril Hill transition therefore marks a significant paleoenvironmental change, where a long–lived and comparatively quiescent lacustrine setting progressively changes into a more energetic fluvial setting, as a consequence of shoreline regression due to either increased sediment supply or lake–level drop.

58 GEOSCIENCES↗
NASA NTRS2024

Parameterizing Spectral Surface Reflectance Relationships for the Dark Target Aerosol Algorithm Applied to A Geostationary Imager

Originally developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) in polar, sun-synchronous low-earth orbit (LEO), the Dark Target (DT) aerosol retrieval algorithm relies on the assumption of a Surface Reflectance Parameterization (SRP) over land surfaces. Specifically for vegetated and dark-soiled surfaces, values of surface reflectance in blue and red visible-wavelength bands are assumed to be nearly linearly related to each other and to the value in a shortwave infrared (SWIR) wavelength band. This SRP also includes dependencies on scattering angle and a normalized difference vegetation index computed from two SWIR bands (NDVISWIR). As the DT retrieval algorithm is being ported to new sensors to continue and expand the aerosol data record, we assess whether the MODIS-assumed SRP can be used for these sensors. Here, we specifically assess SRP for the Advanced Baseline Imager (ABI) aboard, the Geostationary Operational Environmental Satellite (GOES)-16/East (ABIE). First, we find that using MODIS-based SRP leads to higher biases and artificial diurnal signatures in aerosol optical depth (AOD)retrievals from ABIE. The primary reason appears to be that geostationary orbit (GEO) encounters an entirely different set of observation geometry than does LEO, primarily with regards to solar angles coupled with fixed view angles. Therefore, we have developed a new SRP for GEO that draws the angular shape of the surface bidirectional reflectance. We also introduce modifications to the parametrization of both red-SWIR and blue-red spectral relationships to include additional information.The revised Red-SWIR SRP includes solar zenith angle, NDVISWIR, and land-type percentage from an ancillary database. The blue-red SRP adds dependencies on the scattering angle and NDVISWIR. The new SRPs improve the AOD retrieval of ABIE in terms of overall less bias and mitigation of the overestimation around local noon. The average bias of DT AOD compared to AERONET AOD shows a reduction from 0.08 to 0.03, while the bias of local solar noon decreases from 0.12 to 0.03.The agreement between DT and AERONET AOD is established through regression slope of 1.06 and y-intercept of 0.01with correlation coefficient is0.74. By using the new SRP, the percentage of data falling within the expected error range (±0.05 + 15%) is notably risen from 54% to 78%.

Mijin Kim↗
NASA NTRSDate not supplied

Shielding Considerations for CubeSat Structures During Solar Maximum

The purpose of this lessons learned is to communicate the utility of shielding in small spacecraft planning for the support of mission assurance and reliability. Numerous SmallSats have been flying in polar low earth orbit for scientific, communications, technology demonstrations, and imaging with academic, commercial, and government interests. Shielding has been part of mission assurance and reliability from the advent of long duration spacecraft missions. The Shields-1 CubeSat has been operating in polar low earth orbit since 16 December 2018 with atomic number (Z)-grade radiation shielding and demonstrates shielding effectiveness. Shields-1 has collected a representative example of solar minimum data in 2019 with 8 Teledyne dosimeters over varying shielding effectiveness. It serves as current experimental data and has been compared with NOVICE Shielding estimates using the AP8 –AE8 trapped radiation model with the Shields-1 CAD and generic CubeSat 3 unit (U) models. Using NOVICE model radiation analysis coding, the shielding effectiveness's, based on a generic CubeSat 3U structure with 4 electronic boards, were estimated for aluminum wall thicknesses ranging from 0.204 cmto4.44 cm (0.550 g/cm2–12.0 g/cm2) thick aluminum. For modeled polar orbiting spacecraft, solar maximum total ionizing dose (TID)increases by nearly a magnitude for thin-walled aluminum 0.550g/cm2-0.686 g/cm2(0.204 cm –0.254 cm) typical CubeSat structures. The shielding effectiveness by NOVICE Sigma estimates, which is a shielding sphere approximation around a detector, showed a linear relationship with wall thickness, which increased over the wall thickness by a ratio of 1.43 determined by linear regression analysis. Using NOVICE Adjoint Monte-Carlo Modeling of solar minimum and solar maximum with the inclusion of a worst-case solar particle event over a one year mission without geomagnetic shielding, the TID for minimum and maximum conditions for a generic 3U with a wall thickness of 0.254 cm is 158 RAD and 1540 RAD, respectively. The modeled total solar maximum TID is over estimated, because at low orbital latitudes a spacecraft will have shielding from Earth's magnetic field. However, TID will still be significant at high latitudes over the poles, where a spacecraft is exposed in a solar particle event. In contrast, to a thin walled generic 3U CubeSat, Shields-1 electronics enclosure has a shielding effectiveness of 21.3 g/cm2from NOVICE Sigma modeling and is expected to show reduced total ionizing dose increases during the present active Solar Cycle 25 period. Because solar particle events during solar maximum increase TID on electronic parts with thin-walled shielding in short periods of time, it is a mission assurance and reliability consideration on the spacecraft’s mission value versus adding shielding for risk reduction of premature spacecraft or instrument payload loss. Since the volumes of many instruments and system electronics have reduced with small spacecraft, shielding material costs and weight penalties have diminished. A small spacecraft project budget and schedule may limit traditional radiation-hardened part use and radiation testing requirements, where shielding can contribute to mission assurance and reliability with reduced costs.

Shields-1↗
NASA NTRSDate not supplied

Shielding Considerations for CubeSat Structures During Solar Maximum

The purpose of this lessons learned paper is to communicate the utility of shielding in small spacecraft planning for the support of mission assurance and reliability. Numerous SmallSats have been flying in polar low Earth orbit for science, communications, technology demonstrations, and imaging with academic, commercial, and government interests. Shielding has been part of mission assurance and reliability from the advent of long duration spacecraft missions. The Shields-1 CubeSat has been operating in polar low Earth orbit since 16 December 2018 with atomic number (Z)-grade radiation shielding and demonstrates shielding effectiveness. Shields-1 has collected a representative example of solar minimum data in 2019 with eight Teledyne µDosimeters over varying shielding effectivenesses. It serves as current experimental data and has been compared with NOVICE Shielding estimates using the AP8 – AE8 trapped radiation model with the Shields-1 CAD and generic CubeSat three unit (3U) models. Using NOVICE model radiation analysis coding, the shielding effectivenesses, based on a generic CubeSat 3U structure with four electronic boards, were estimated for aluminum wall thicknesses ranging from 0.204 cm to 4.44 cm (0.550 g/cm 2 to 12.0 g/cm 2 ). For modeled polar orbiting spacecraft, solar maximum total ionizing dose (TID) increases by nearly a magnitude for thin-walled aluminum 0.550-g/cm 2 - 0.686-g/cm 2 (0.204-cm – 0.254-cm) typical CubeSat structures. The shielding effectiveness estimated by NOVICE Sigma, which is a shielding sphere approximation around a detector, showed a linear relationship with wall thickness, which increased over the wall thickness by a ratio of 1.43 determined by linear regression analysis. Using NOVICE Adjoint Monte-Carlo Modeling of solar minimum and solar maximum with the inclusion of a worst-case solar particle event over a one-year mission without geomagnetic shielding, the TID for minimum and maximum conditions for a generic 3U with a wall thickness of 0.254 cm is 158 RAD and 1540 RAD, respectively. The modeled total solar maximum TID is over estimated, because at low orbital latitudes a spacecraft will have shielding from the Earth’s magnetic field. However, TID will still be significant at high latitudes over the poles, where a spacecraft is exposed in a solar particle event. In contrast to a thin-walled generic 3U CubeSat, the Shields-1 electronics enclosure has a shielding effectiveness of 21.3 g/cm 2 from NOVICE Sigma modeling and is expected to show reduced total ionizing dose increases during the present active Solar Cycle 25 period. Because solar particle events during solar maximum increase TID on electronic parts with thin-walled shielding in short periods of time, it is a mission assurance and reliability consideration on the mission value of the spacecraft versus adding shielding for risk reduction of premature spacecraft or instrument payload loss. Since the volumes of many instruments and system electronics have reduced with small spacecraft, shielding material costs and weight penalties have diminished. A small spacecraft project budget and schedule may limit traditional radiation-hardened part use and radiation testing requirements, where shielding can contribute to mission assurance and reliability with reduced costs.

Shields-1↗
NASA NTRS1993

Cross-calibration of liquid and solid QCT calibration standards: corrections to the UCSF normative data

Quantitative computed tomography (QCT) has been shown to be a precise and sensitive method for evaluating spinal bone mineral density (BMD) and skeletal response to aging and therapy. Precise and accurate determination of BMD using QCT requires a calibration standard to compensate for and reduce the effects of beam-hardening artifacts and scanner drift. The first standards were based on dipotassium hydrogen phosphate (K2HPO4) solutions. Recently, several manufacturers have developed stable solid calibration standards based on calcium hydroxyapatite (CHA) in water-equivalent plastic. Due to differences in attenuating properties of the liquid and solid standards, the calibrated BMD values obtained with each system do not agree. In order to compare and interpret the results obtained on both systems, cross-calibration measurements were performed in phantoms and patients using the University of California San Francisco (UCSF) liquid standard and the Image Analysis (IA) solid standard on the UCSF GE 9800 CT scanner. From the phantom measurements, a highly linear relationship was found between the liquid- and solid-calibrated BMD values. No influence on the cross-calibration due to simulated variations in body size or vertebral fat content was seen, though a significant difference in the cross-calibration was observed between scans acquired at 80 and 140 kVp. From the patient measurements, a linear relationship between the liquid (UCSF) and solid (IA) calibrated values was derived for GE 9800 CT scanners at 80 kVp (IA = [1.15 x UCSF] - 7.32).(ABSTRACT TRUNCATED AT 250 WORDS).

NASA Program Space Physiology and Countermeasures↗
NASA NTRS2018

A Global Spatially Contiguous Solar-Induced Fluorescence (CSIF) Dataset Using Neural Networks

Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inconsistency in measurement footprints also hinders the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially contiguous SIF (CSIF) datasets at moderate spatiotemporal (0.05° 4-day) resolutions during the MODIS era, one for clear-sky conditions (2000–2017) and the other one in all-sky conditions (2000–2016). The clear-sky instantaneous CSIF (CSIF(sub clear-inst)) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIF(sub all-daily)) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39 %) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite-observed SIF and CSIF is mostly caused by the environmental down-regulation on SIF(sub yield), the ratio between OCO-2 SIF and CSIF(sub clear-inst) can be an effective indicator of drought stress that is more sensitive than the normalized difference vegetation index and enhanced vegetation index. By comparing CSIF(sub all-daily) with GPP estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of the GPP–SIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two contiguous SIF datasets and the derived GPP–SIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate.

Zhang, Yao↗
NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗