Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “BaNd”

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 901 records · Page 50

Definition of a Technology Validation Mission for P-band Reflectometry using Signals of Opportunity

Root-Zone Soil Moisture (RZSM) (moisture profile in the top meter of soil) and Snow Water Equivalent (SWE) (total snow pack water content) are identified as priority target variables in the ESAS 2017 decadal survey [1] with critical roles in hydrology and water management. RZSM estimates are vital for understanding multiple Earth system processes and forecasting (for example, droughts [2]). Simultaneous knowledge of surface and RZSM could enable a breakthrough in estimating key unobserved hydrologic fluxes and reduce uncertainty in net ecosystem exchange (NEE), carbon balance [3] discharge estimates, and crop yield forecasts [4] .With the high albedo and insulating properties of snow, monitoring, SWE accumulation would provide a key constraint on the potential runoff during spring ablation while monitoring SWE disappearance rates would provide a key constraint on SWE partition into runoff vs. infiltration/recharge. [5] demonstrated that knowledge of early-spring SWE generally contributes most to streamflow forecast skill in the Western U.S. SWE is also a source of water storage that provides the water resources during spring snowmelt. Despite such potentially transformative contributions, accurate RZSM and SWE measurements are unattainable with current technology. While active/passive L-band methods (e.g. SMAP, SMOS) can reliably retrieve surface soil moisture in the top 5 cm of soil [6], [7]. RZSM estimates are only available through model assimilation of brightness temperatures with a radiative transfer and land surface models [8]. SWE estimation uses multi-frequency passive microwave techniques (e.g. [9]-[11]), which have significant problems with deeper snow and in forested and mountainous environments [12]. Signals of opportunity (SoOp) in P-band (200-400 MHz) is a new remote sensing technique with the capability of estimating both essential hydrologic variables, RZSM and SWE, circumventing many of the aforementioned limitations under all weather conditions day and night. SoOp is the re-utilization of existing powerful satellite transmissions within bands allocated for communications or navigation. P-band SoOp sensitivity to soil moisture has been demonstrated in an airborne experiment over Oklahoma in 2016 [13]. Recent theory [14] and experiments [15] have also confirmed that the reflection coefficient phase is proportional to SWE.

Garrison, J. L.↗

Enhancements to the Open Access Spectral Band Adjustment Factor Online Calculation Tool for Visible Channels

With close to 40 years of satellite observations, from which, cloud, land-use, and aerosol parameters can be measured, inter-consistent calibrations are needed to normalize retrievals across satellite records. Various visible-sensor inter-calibration techniques have been developed that utilize radiometrically stable Earth targets, e.g., deep convective clouds and desert/polar ice pseudo-invariant calibration sites. Other equally effective, direct techniques for intercalibration between satellite imagers are simultaneous nadir overpass comparisons and ray-matched radiance pairs. Combining independent calibration results from such varied techniques yields robust calibration coefficients, and is a form of self-validation. One potential source of significant error when cross-calibrating satellite sensors, however, are the often small but substantial spectral discrepancies between comparable bands, which must be accounted for. As such, visible calibration methods rely on a Spectral Band Adjustment Factor (SBAF) to account for the spectral-response function- induced radiance differences between analogous imagers. The SBAF is unique to each calibration method as it is a function of the Earth-reflected spectra. In recent years, NASA Langley pioneered the use of SCIAMACHY-, GOME-2-, and Hyperion-retrieved Earth spectra to compute SBAFs. By carefully selecting hyperspectral footprints that best represent the conditions inherent to an inter-calibration technique, the uncertainty in the SBAF is greatly reduced. NASA Langley initially provided the Global Space-based Inter-calibration System processing and research centers with online SBAF tools, with which users select conditions to best match their calibration criteria. This article highlights expanded SBAF tool capabilities for visible wavelengths, with emphasis on the use of the spectral range filtering for the purpose of separating scene conditions for the channel that the SBAF is needed based on the reflectance values of other bands. In other words, spectral filtering will enable better scene-type selection for bands where scene determination is difficult without information from other channels, which should prove valuable to users in the calibration community.

Scarino, Benjamin↗

Cloud Detection over Snow and Ice with Oxygen A- and B-band Observations from the Earth Polychromatic Imaging Camera (EPIC)

Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and cold/bright surfaces; cloud mask algorithms often heavily rely on shortwave infrared (IR) channels over such surfaces. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) does not have infrared channels, which makes cloud detection over snow and ice surfaces even more challenging. This study investigates the methodology of applying EPIC's two oxygen absorption band pair ratios in the A band (764, 780 nm) and B band (688, 680 nm) for cloud detection over the snow and ice surfaces. We develop a novel elevation and zenith-angle-dependent threshold scheme based on radiative transfer model simulations that achieves significant improvements over the existing algorithm. When compared against a composite cloud mask based on geosynchronous Earth orbit (GEO) and low Earth orbit (LEO) sensors, the positive detection rate over snow and ice surfaces increased from around 36 % to 65 % while the false detection rate dropped from 50 % to 10 % for observations of January 2016 and 2017. The improvement in July is less substantial due to relatively better performance in the current algorithm. The new algorithm is applicable for all snow and ice surfaces including Antarctic, sea ice, high-latitude snow, and high-altitude glacier regions. This method is less reliable when clouds are optically thin or below 3 km because the sensitivity is low in oxygen band ratios for these cases.

EPIC↗

S-NPP and N20 VIIRS RSB Bands Detector-to-Detector Calibration Differences Assessment Using a Homogeneous Ground Target

The S-NPP and N20 satellites have successfully operated since their launches on October 28, 2011 and November 18, 2017, respectively. This paper provides an assessment of the detector calibration stability for the reflective solar bands (RSBs) observed from both S-NPP and N20 VIIRS. Top-of-atmosphere radiances from near-nadir observations over the homogeneous Libya 4 desert site are extracted from the S-NPP VIIRS Collection 1 and N20 Collection 2 Level-1B products. The radiances from individual detectors per Half‐angle Mirror side are studied. The comparisons of the normalized radiance to all detector average values indicate that the detector calibration differences are wavelength dependent. The S-NPP detector differences have been slowly increasing in the past 8.5 years and bands M1-M4 have 1.3%- 2.2% detector differences in 2019. N20 detector differences are stable and small in the past two years except SWIR M bands. N20 M10 and M11 have 1.3% and 2.1% detector differences, respectively. S-NPP DNB detector differences are about 0.8% and N20 DNB detector differences are about 0.5%. Most bands HAM side differences are less than 0.25% in the past years except N20 VIIRS M1 HAM side differences are 0.57% in 2018 and 0.54% in 2019. The Libya 4 images have small but noticeable striping in S-NPP M1-M4 data as well as in N20 M1, M8, M10, and M11 data. These study results have been applied in the S-NPP Collection 2 new algorithm to remove the detector differences. This research help scientists and VIIRS users better understand detector calibration differences in different version VIIRS products.

Libya 4↗

SNPP VIIRS Reflective Solar Bands On-Orbit Calibration Using the Moon

The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite has been on orbit for more than eight years since its launch on October 28, 2011. The VIIRS has 22 spectral bands, among which 14 are reflective solar bands(RSBs) covering a spectral range from 0.41 to 2.25 m. The VIIRS RSBs are primarily calibrated on-orbit by an onboard solar diffuser (SD) panel and a solar diffuser stability monitor (SDSM). Besides the SD and SDSM calibration, the RSBs are scheduled to view the Moon approximately monthly through the instrument’s space view (SV). The lunar observations have also been used to calibrate the RSBs on-orbit since early mission. Due to the non uniformity of the SD’s degradation, the calibration coefficients that are derived from the SD/SDSM calibration have long-term biases, especially at short wavelengths. In addition, the SDSM has no capability to monitor the SD degradation beyond 0.935 m, resulting in long-term bias in the shortwave infrared bands, about 0.72% for band M8 (1.238 m). These biases induce significant errors and long-term drifts in the VIIRS sensor data records (SDR) and consequently in the environmental data records (EDR). Unlike the SD, the Moon is a known stable target and any temporal drifts observed while viewing the Moon can be attributed to the sensor’s degradation. Thus, the VIIRS lunar calibration is used to track the RSB on-orbit changes, especially to provide an accurate long-term baseline. Due to the non-uniformity of the lunar surface, the lunar irradiance, instead of the lunar radiance, is used to calibrate the RSBs. The lunar irradiance strongly depends on lunar view geometry and it is still a challenge to accurately characterize the geometric effects associated with the lunar measurements and any residual errors can induce seasonal oscillations in the derived calibration coefficients. The errors of the geometric dependence correction induce seasonal oscillations in the derived RSB lunar calibration coefficients. In this paper, the algorithms for the view geometric effect correction are significantly improved, resulting in a significant reduction in the seasonal oscillations observed in the calibration coefficient time-series. The lunar and SD/SDSM calibration results are properly incorporated to generate a set of hybrid calibration coefficients and implementation of these coefficients is shown to significantly improve the long-term stability of the VIIRS SDR. This is of fundamental importance in making accurate Earth observations from which reliable and high quality science products are generated. The consequent improvements in SNPP VIIRS RSB SDRs and EDRs are shown and discussed. The lunar calibration methodology can be directly applied to follow-on VIIRS instruments.

Junqiang Sun↗

RainCube, a Ka-band Precipitation Radar in a 6U CubeSat

RainCube (Radar in a CubeSat) is a 6U CubeSat mission between the Jet Propulsion Laboratory (JPL) and Tyvak Nano-Satellite Systems (Tyvak). The objective of the mission is to develop, launch, and operate a 35.75 GHz nadirpointing precipitation profiling radar payload to validate a new architecture for Ka-band radars and an ultra-compact deployable Ka-band antenna design in the space environment. RainCube will also demonstrate the feasibility of a radar payload on a CubeSat platform. The radar payload is the evolution of two previous JPL research and development technologies – the miniaturized Ka-band atmospheric radar (miniKaAR) and the 0.5m diameter Ka-band parabolic deployable antenna (KaPDA). JPL has contracted Tyvak to develop the spacecraft bus, integrate the payload, and operate the spacecraft. RainCube is currently in integration and test with an expected ready-for-delivery in September 2017.

Williams, Austin↗

High-Rate Ka-Band Modulator for the NISAR Mission

In order to meet ever-increasing data return requirements, more satellites are using the near-Earth Ka-band (25.5 – 27.0 GHz) to achieve higher downlink rates. The paper discusses the Universal Space Transponder - Ka-band Modulator (UST-KaM) developed at the NASA Jet Propulsion Laboratory for the NASA-ISRO SAR (NISAR) mission, which is capable of transmitting up to 1.74 Gbps with 7/8 LDPC encoding. The UST-KaM utilizes OQPSK with both baseband and RF filtering to contain the 1 Gsps transmission spectrum within the 1.5 GHz Ka-band, even with the use of an external, saturated amplifier. Due to the high data rates involved, several technical hurdles were overcome in both the digital and RF designs. The UST-KaM is a software defined radio with two digital circuit board assemblies: a low speed housekeeper board for commanding and telemetry, and a high-rate signal processing board known as the Signal Processing Module (SPM). The SPM receives data from the spacecraft via a SERDES interface at up to 2 Gbps, processes and encodes the data using a Xilinx Virtex-5 FPGA, and produces 1 Gsps OQPSK I and Q waveforms via synchronized, multiplexed DACs. The RF Electronics in the UST-KaM employ a heterodyne architecture in which the I/Q digital waveforms are filtered and then up-converted using a sub-harmonic IQ mixer. The LO of the converter, which is included in the exciter assembly, is at 13.125GHz, and the RF output is a 26.25GHz carrier which is modulated with the OQPSK waveforms. The output of the mixer is then filtered using low loss quartz thinfilm edge coupled Chebyshev filters and amplified through a series of low gain Ka-Band amplifiers. The exciter assembly also has a 2GHz, low-phase-noise, PLL synthesizer to supply the clock to the DACs for the high rate digital waveforms.

Spurgers, Carl↗

Optical and Mechanical Designs of the Multi-band SWIR Receiver for the Lunar Flashlight CubeSat Mission

Mapping and quantifying lunar water ice addresses one of NASA’s Strategic Knowledge Gaps to understand the lunar resource potential for future human exploration of the Moon. Lunar Flashlight is an innovative NASA CubeSat mission dedicated to mapping water ice in the permanently-shadowed and occasionally-sunlit regions in the vicinity of the lunar South Pole. Lunar Flashlight will acquire these measurements from lunar orbit using a multi-band laser reflectometer composed of an optical receiver aligned with four lasers emitting different wavelengths in the shortwave infrared spectral region between 1 µm and 2 µm. The receiver measures the laser radiance reflected from the lunar surface in each spectral band and continuum/absorption reflectance band ratios are then analyzed to quantify water ice concentration in the illuminated spot. The receiver utilizes a 70×70-mm, aluminum, off-axis paraboloidal mirror with a focal length of 70 mm, which collects the incoming light onto a single, 2 mm diameter InGaAs detector with a cutoff wavelength of 2.4 µm. We present the optical and mechanical designs of the receiver, including its optimization for rejection of solar stray-light from outside its intended field of view. This highly mass- and volume-constrained instrument payload will demonstrate several firsts, including being one of the first instruments onboard a CubeSat performing science measurements beyond low Earth orbit and the first planetary mission to use multi-band active reflectometry from orbit.

Sellar, R. Glenn↗

Ka-band High-Rate Downlink System for the NISAR Mission

This paper provides a description and analyses of the high-rate Ka-band telecom system for the upcoming NISAR (NASA-ISRO Synthetic Aperture Radar) mission. NISAR is a collaborative Earth-Science mission between National Aeronautics and Space Administration (NASA) and Indian Space Research Organization (ISRO), which features an L-band SAR instrument and an S-band SAR instrument. The simultaneous dual-frequency radar system at peak rates will produce data at gigabit-per-second speeds, which drives the data-volume requirements. The key driving requirement for the payload communication subsystem is to provide a minimum of 26 Terabits per day of radar science data to the ground. The high-rate transmitter on the flight system is a software-defined radio developed at the Jet Propulsion Laboratory (JPL), based on the Universal Space Transponder platform, providing an offset quadrature phase shift key modulated waveform with Low-Density Parity-Check encoding of the data transfer frames. Two transmitters used in a dual-polarization configuration with each transmitter providing two giga-symbols per second (Gsps) of coded data provides an aggregate rate of four Gsps. In this system, only one watt of signal power is necessary on each polarization to overcome propagation losses and achieve a successful RF link. Several Near Earth Network (NEN) ground station sites (Alaska in the United States, Svalbard in Norway, and Punta Arenas in Chile) are baselined for the space-to-Earth communications link. Each ground station will also feature multiple upgrades to support NISAR’s transmission starting with new Ka-band antennas, wideband downconverters and high-rate receivers. In addition, a baseband data processor called Data Acquisition Processor and Handling Network Environment (DAPHNE), newly developed by the NEN, provides data storage and connectivity to backhaul networks. With NISAR’s large quantities of data (over 3.5 Petabytes over the mission), the processing of science data will be primarily performed on a cloud system to reduce the overall cost to the mission. The system described herein will be the first operational use of Gsps-class downlink rates on an Earth-Science mission.

Dissanayake, Asoka↗

Convective precipitation retrievals from space-borne dual frequency (Ka- and W-band) radar observations

The 2017 Decadal Survey highlighted Earth System Science themes, science and application questions that led to the inclusion aerosols (A), clouds, convection, and precipitation (CCP) in the list of designated observables (DOs) to be pursued within the decade following the survey. Multiple observing system architectures were considered during the NASA A-CCP study, and dual frequency Ka-W band radars have been identified as components of the most likely architectures due to their ability to significantly contribute to the achievement of the science objectives and their synergy with other essential instruments. We present an algorithm to estimate convective precipitation from dual frequency, Ka- and W-band, observations applicable to the observing system architectures considered in the A-CCP study. The algorithm is based on the radar profiling algorithm used within the operational NASA GPM combined algorithm but features an additional module that makes the retrieval problem well-posed in cases of severe attenuation and potential loss of signal below the freezing level. The additional module is developed from GPM combined retrievals and consists of a procedure that generates an ensemble of potential retrievals consistent with the portions of the Ka-band and W-band observation profiles above the noise level. The ensemble solution is refined based on estimates of Path Integrated Attenuation (PIA) from the Surface Reference Technique (SRT) and radiometric observations.

Mircea Grecu↗

Assessment of VIIRS Day-Night Band High Gain Calibration Using Stars

The VIIRS has a unique spectral band that can sense reflected light from space during both day and nighttime, thus referred to as the day-night band (DNB). This band operates in three different gain stages: low, mid, and high gain (LG, MG, and HG) that cover a remarkable dynamic range with its HG stage being extremely sensitive to low-light scenes. Similar to its reflective solar bands, the VIIRS uses an on-board solar diffuser for its DNB LG stage calibration, whereas the DNB MG and HG calibrations are performed using relative or ratioing approaches. In this paper, we provide an assessment of VIIRS DNB HG calibrations using stars that appear in the field-of-view of its space view (SV), focusing on the computation of the gain trending and associated tool development. We also describe various strategies and procedures developed for DNB HG calibration stability monitoring and inter-comparisons among different VIIRS instruments and discuss some of the complications and limits to this approach, including under-sampling of the stellar image, saturation, and crowding. For S-NPP VIIRS, it includes the use of the modulated relative spectral response (RSR) resulting from wavelength-dependent degradation of the sensor optics. Results show that the star-based response trending for the DNB HG is consistent with that derived from on-board solar diffuser and that the calibration difference between S-NPP and N-20 VIIRS DNB requires additional effort to address, especially for research studies and applications using data from both sensors.

VIIRS↗

Reflectivity, Emissivity, and Brightness from Multilayered Soil with Linearly Varying Permittivity at P- and L- bands

Passive sensing of root zone soil moisture is theoretically investigated using P and L band frequencies. The soil is considered to be a stratified medium. The reflectivity from the soil surface is found by considering the soil to be composed of a finely layered structure with layer widths that are small compared to a wavelength. A well-known transmission line algorithm is then used to compute the reflectivity at the surface. Emissivity is obtained as (1 – reflectivity). It assumed that the soil is at a constant temperature of 300o K when determining the brightness temperature. A soil moisture profile consisting of two homogeneous layers separated by a layer of linearly varying soil moisture is investigated. The study is made at an incident angle of 40O for horizontal polarization at 0.8 and 1.4 GHz. Two variations of the basic profile are considered: a transition from a layer of lesser moisture to a layer of greater moisture and the opposite. In both cases the P band signal shows greater penetration than the L band case. There is greater penetration in the transition to drier soils. In both cases the L band signals show sensitivity to only the upper surface of the soil as expected.

Ming Li↗

Vicarious Calibration of the Long Near Infrared Band: Cross-Sensor Differences in Sensitivity

Numerous assumptions and approximations are employed when translating satellite-derived radiance to surface remote sensing reflectance (RRS) for ocean color applications. Among these is the vicarious calibration coefficient (g) of the “long” near infrared band (NIRL) used for atmospheric correction. For this band, the prelaunch calibration has always been deemed sufficient [thus g(NIRL) = 1.00] as long as other bands are vicariously calibrated. Recent research, however, suggests that Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua RRS time series is quite sensitive to g(NIRL) (and associated vicarious gains in other bands). In this work, we assessed the sensitivity of Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-orbiting Partnership satellite (VIIRS/SNPP) RRS to NIRL calibration and compared our results to previous MODIS/Aqua and Sea-viewing Wide Field-of-View Sensor onboard OrbView2 (SeaWiFS)/OrbView2 analysis. In doing so, we note that g(NIRL) sensitivities of mission-averaged RRS time series are lower for VIIRS and Sea-WiFS, relative to MODIS. At the scale of monthly climatologies (MCs), however, all sensors show prominent g(NIRL) sensitivity with that of SeaWiFS being the most substantial. These findings informed simulation analyses, whereby we identified signal-tonoise ratio (SNR) and radiant path geometry, as well as their interaction, as having notable impacts on g(NIRL) sensitivity. As such, g(NIRL) sensitivity is a necessary consideration for reflectance uncertainty budgets, especially for sensors with higher NIR SNR or particular prevailing radiant path geometries. Given the geometry components embedded within g(NIRL) sensitivity, such studies should be coupled with cross-sensor intercalibrations [e.g., using simultaneous same view (SSV) measurements] toward minimizing NIRL errors between satellite instruments, but such efforts will not completely remediate remaining cross-sensor biases in RRS. Index Terms—Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua, ocean color, ocean gyres, Sea-viewing Wide Field-of-View Sensor onboard OrbView2 (SeaWiFS), system vicarious calibration (SVC), Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-orbiting Partnership satellite (VIIRS/SNPP).

MODIS↗

Vicarious Calibration of the Long Near Infrared Band: Cross-Sensor Differences in Sensitivity

Numerous assumptions and approximations are employed when translating satellite-derived radiance to surface remote sensing reflectance (RRS) for ocean color applications. Among these is the vicarious calibration coefficient (g) of the ‘long’ near infrared band (NIRL) used for atmospheric correction. For this band, the pre-launch calibration has always been deemed sufficient [thus g(NIRL) = 1.00] as long as other bands are vicariously calibrated. Recent research, however, suggests that MODIS/Aqua RRS time series are quite sensitive to g(NIRL) (and associated vicarious gains in other bands). In this work, we assessed the sensitivity of VIIRS/SNPP RRS to NIRL calibration, and compared our results to previous MODIS/Aqua and SeaWiFS/OrbView2 analysis. In doing so, we note g(NIRL) sensitivities of mission-averaged RRS timeseries are lower for VIIRS and SeaWiFS, relative to MODIS. At the scale of monthly climatologies, however, all sensors show prominent g(NIRL) sensitivity, with that of SeaWiFS being the most substantial. These findings informed simulation analyses, whereby we identified signal-to-noise ratio (SNR) and radiant path geometry, as well as their interaction, as having notable impacts on g(NIRL) sensitivity. As such, g(NIRL) sensitivity is a necessary consideration for reflectance uncertainty budgets, especially for sensors with higher NIR SNR or particular prevailing radiant path geometries. Given the geometry components embedded within g(NIRL) sensitivity, such studies should be coupled with cross-sensor intercalibrations (e.g., using simultaneous same view measurements) toward minimizing NIRL errors between satellite instruments, but such efforts will not completely remediate remaining cross-sensor biases in RRS.

Brian B Barnes↗

Evaluation of Spectral Band Adjustment Factors for Cross-Calibration of Visible Imagers

The NASA CERES EBAF dataset provides TOA SW and LW fluxes for long-term monitoring of the Earth’s energy balance and to validate climate models. The EBAF products, based on the Terra, Aqua, and NOAA20 CERES instrument observed radiances, rely on coincident measurements from the onboard MODIS or VIIRS imagers to determine cloud properties used for angular distribution model scene selection, which is required to convert the CERES observed radiances into flux values. Furthermore, CERES utilizes geostationary imager (GEO) hourly fluxes and clouds to infer the regional averaged daily flux in the SYN1deg product. A seamless transition of fluxes and clouds can only occur if the analogous MODIS, VIIRS, and GEO channels are properly inter-calibrated. The analogous band SRFs differ noticeably and require scene dependent spectral band adjustment factors (SBAF) for proper radiometric scaling between them. Given their disparate but overlapping SRFs, the coincident VIIRS I1 and M5 band TOA reflectance measurements provide the optimal opportunity to validate SBAFs over many surface and cloud conditions. The CERES project maintains SCIAMACHY, GOME-2, and Hyperion scene-stratified hyper-spectral reflectance measurements that can be convolved with sensor pair SRFs to compute the corresponding SBAF. This study highlights the SCIAMACHY, GOME-2, and Hyperion based SBAFs for different MODIS, VIIRS, and GEO inter-calibration targets, including all-sky tropical ocean (ATO), Libya-4, Dome-C, and deep convective clouds. To mitigate the observed sensor radiance fluctuations due to atmospheric absorption and aerosol variations, the Earth target observed radiances are correlated with multiple atmospheric parameters, such as precipitable water and ozone. The remaining M5 and I1 mean spectral band radiance difference should be resolved by the SBAF correction. The formulation of hyper-spectral sensor based SBAFs and validation methods can be verified in future utilizing high-accuracy, SI-traceable hyper-spectral measurements from CLARREO Pathfinder.

David R Doelling↗

MastCam Multispectral Examination of Rocks in the Sulfate-Bearing Terrain of Marker Band Valley, Gale Crater, Mars

A primary objective of the 4th extended mission of the Mars Science Laboratory rover Curiosity is to characterize the sulfate-bearing beds exposed on the slopes of Mt. Sharp (Aeolis Mons) above the clay-bearing beds of the Glen Torridon region characterized in the 3rd extended mission. Orbital reflectance spectroscopy from CRISM has indicated the presence of polyhydrated Mg sulfates in the portion of Mt. Sharp dubbed “Marker Band Valley” (MBV). The Marker Band is a darker toned, high-Ca pyroxene bearing unit that extends around much of Mt. Sharp above the first exposures of sulfate-bearing beds. Standoff imaging of the Marker Band indicates that it is not monolithic, but contains at least two subunits. Curiosity measures multispectral reflectance in the 430 to 1012 nm range with its Mastcam. While diagnostic spectral features of Mg-sulfates are not present in the Mastcam spectral range, a water overtone feature of some polyhydrated sulfates can be observed as a drop in reflectance in the longest wavelength one to three bands of Mastcam multispectral data. Other minerals associated with sulfate-generating alteration processes, most notably Fe-bearing minerals, are more readily detectable in Mastcam multispectral data.

W H Farrand↗

Design of synthetic collagens that assemble into supramolecular banded fibers as a functional biomaterial testbed

Collagens are the most abundant proteins of the extracellular matrix, and the hierarchical folding and supramolecular assembly of collagens into banded fibers is essential for mediating cell-matrix interactions and tissue mechanics. Collagen extracted from animal tissues is a valuable commodity, but suffers from safety and purity issues, limiting its biomaterials applications. Synthetic collagen biomaterials could address these issues, but their construction requires molecular-level control of folding and supramolecular assembly into ordered banded fibers, comparable to those of natural collagens. Here, we show an innovative class of banded fiber-forming synthetic collagens that recapitulate the morphology and some biological properties of natural collagens. The synthetic collagens comprise a functional-driver module that is flanked by adhesive modules that effectively promote their supramolecular assembly. Multiscale simulations support a plausible molecular-level mechanism of supramolecular assembly, allowing precise design of banded fiber morphology. We also experimentally demonstrate that synthetic fibers stimulate osteoblast differentiation at levels comparable to natural collagen. This work thus deepens understanding of collagen biology and disease by providing a ready source of safe, functional biomaterials that bridge the current gap between the simplicity of peptide biophysical models and the complexity of in vivo animal systems.

Jinyuan Hu↗

Juno Gravity Science: Five Years of Radio Science Operations with Ka-band Uplink

Since entering orbit on July 4, 2016, the Juno spacecraft has executed 34 closest approach passes of Jupiter, completing the prime mission. During each closest approach, called perijove, the spacecraft comes within 4,000 km of the cloud tops and the motion of the spacecraft becomes perturbed by the gravitational field of Jupiter. These small changes in the motion of the spacecraft are detected using the Juno Gravity Science Instrument by measuring the Doppler shift of the radio link between the Juno spacecraft and NASA’s Deep Space Network (DSN). During a majority of these closest approach passes, the 34-meter DSS-25 antenna transmits simultaneous X-band and Ka-band uplink to the spacecraft. Juno’s onboard X-band transponder and Ka-band translator phase-coherently return the signals back to Earth for reception at the same DSS-25 antenna. The precise frequency of these signals is measured by processing open-loop recordings of the signal. These measurements, characterized by ~5-10 micron/sec accuracies (after calibration of charged particle noise and Earth troposphere), have probed the gravity field of Jupiter to unprecedented precision, allowing for discoveries of Jupiter’s core size and depth of the zonal winds. Successful operations of the instrument during perijoves requires careful planning and coordination between DSN engineers, the Juno project, and the Juno science team. This work discusses the operations of the Juno Gravity Science Instrument after five years of prime mission operations. Lessons learned are documented to be applied to future missions and the Juno extended mission. Although the Juno extended mission formally started on August 1, 2021, on June 7, 2021, the trajectory was modified with a flyby of Ganymede, the third Galilean moon of Jupiter. Gravity and radio science investigations of Jupiter and its moons will continue to play a key role in Juno’s objectives during the extended mission.

Oudrhiri, Kamal↗