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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.

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191 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Estimating the Relative Water Content of Leaves in a Cotton Canopy

Remotely sensing plant canopy water status remains a long-term goal of remote sensing research. Established approaches to estimating canopy water status the Crop Water Stress Index, the Water Deficit Index and the Equivalent Water Thickness involve measurements in the thermal or reflective infrared. Here we report plant water status estimates based upon analysis of polarized visible imagery of a cotton canopy measured by ground Multi-Spectral Polarization Imager (MSPI). Such estimators potentially provide access to the plant hydrological photochemistry that manifests scattering and absorption effects in the visible spectral region.Twice during one day, +- 3 hours from solar noon, we collected polarized imagery and relative water content data on a cotton test plot located at the Arid Land Agricultural Research Center, United States Department of Agriculture, Maricopa, AZ. The test plot, a small portion of a large cotton field, contained stressed plants ready for irrigation. The evening prior to data collection we irrigated several rows of plants within the test plot. Thus, ground MSPI imagery from both morning and afternoon included cotton plants with a range of water statuses. Data analysis includes classifying the polarized imagery into sunlit reflecting, sunlit transmitting, shaded foliage and bare soil. We estimate the leaf surface reflection and interior reflection based upon the per pixel polarization and sunview directions. We compare our cotton results with our prior polarization results for corn and soybean leaves measured in the lab and corn leaves measured in the field.

Cotton Canopy↗
NASA NTRS2017

Assessment of Satellite-Derived Surface Reflectances by NASA's CAR Airborne Radiometer over Railroad Valley, Nevada

CAR (Cloud Absorption Radiometer) is a multi-angular and multi-spectral airborne radiometer instrument, whose radiometric and geometric characteristics are well calibrated and adjusted before and after each flight campaign. CAR was built by NASA (National Aeronautics and Space Administration) in 1984. On 16 May 2008, a CAR flight campaign took place over the well-known calibration and validation site of Railroad Valley in Nevada (38.504 deg N, 115.692 deg W).The campaign coincided with the overpasses of several key EO (Earth Observation) satellites such as Landsat-7, Envisat and Terra. Thus, there are nearly simultaneous measurements from these satellites and the CAR airborne sensor over the same calibration site. The CAR spectral bands are close to those of most EO satellites. CAR has the ability to cover the whole range of azimuth view angles and a variety of zenith angles depending on altitude and, as a consequence, the biases seen between satellite and CAR measurements due to both unmatched spectral bands and unmatched angles can be significantly reduced. A comparison is presented here between CARs land surface reflectance (BRF or Bidirectional Reflectance Factor) with those derived from Terra/MODIS (MOD09 and MAIAC), Terra/MISR, Envisat/MERIS and Landsat-7. In this study, we utilized CAR data from low altitude flights (approx. 180 m above the surface) in order to minimize the effects of the atmosphere on these measurements and then obtain a valuable ground-truth data set of surface reflectance. Furthermore, this study shows that differences between measurements caused by surface heterogeneity can be tolerated, thanks to the high homogeneity of the study site on the one hand, and on the other hand, to the spatial sampling and the large number of CAR samples. These results demonstrate that satellite BRF measurements over this site are in good agreement with CAR with variable biases across different spectral bands. This is most likely due to residual aerosol effects in the EO derived reflectances.

calibration↗
NASA NTRS2019

Earth Science Technology Office (ESTO) New Observing Strategies (NOS) and NOS-Testbed (NOS-T)

With the advancement of space hardware technologies such as smaller spacecraft, component and instrument miniaturization and high performance space processors, and with the advancement of software technologies in artificial intelligence, big data analysis and autonomous decision making, Earth Science is looking at novel ways to observe phenomena that previously could not have been studied or would have been too expensive to study with traditional missions. In particular, the New Observing Strategies (NOS) component of the NASA Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) Program aims at leveraging these novel technologies as well as low cost and easy access to space to acquire multi-temporal or simultaneous multi-angular, multi-locations, multi-resolution and multi-spectral observations that will provide better multi-source measurements and will build a more dynamic and comprehensive picture of Earth Science phenomena that need to be studied and analyzed. For applications such as water resources management, air quality monitoring, biodiversity studies or disaster management, NOS will integrate the use of small instruments, small spacecraft, constellations of spacecraft and networks of sensors to design new missions that will provide the necessary measurements to improve future forecast and science modeling systems.Measurement acquisition will therefore be approached as a system of systems rather than on a mission basis, and a system of this complexity should not be expected to work without full integration and experimental characterization. Although most of the individual technologies enabling to link and coordinate multi-source observations are more or less mature, a few technologies need to be developed and all of them need to be integrated and tested as a system. In order for this validation to occur, the AIST Program is developing the NOS Testbed that includes 3 main goals:1.Validate novel NOS technologies, independently and as a system2.Demonstrate novel distributed operations concepts3.Socialize new Distributed Spacecraft Mission (DSM) and SensorWeb (SW) technologies and concepts to the science community by significantly retiring the risk of integrating these new technologies.The NOS Testbed will consist of multiple sensing nodes, simulated or actual, representing space, air and/or ground measurements, that are interconnected by a communications fabric (infrastructure that permits nodes to transmit and receive data between one another and interact with each other). Each node will be supported by hardware capabilities required to perform nodes monitoring and command & control, as well as intelligent "onboard" computing. The nodes will work together in a collaborative manner to demonstrate optimal science capabilities. The testbed will enable to validate technologies such as inter-node communication models, techniques and protocols; inter-node coordination; real-time data fusion and understanding; planning; sensor re-targeting; etc. Additionally, the testbed will have the capability to interact with various mission design tools, OSSEs and one or several forecast models. More details about the NOS Testbed will be presented at the confererence.

Earth Science missions; Advanced information Syste↗
NASA NTRS2016

MUlti-SpEctral, MUlti-SpEcies, MUlti-SatEllite (MUSES) Retrieval Algorithm: Towards Extending Multi-Decadal NASA EOS Atmospheric Composition Data Records

Multi-Spectra, Multi-Species, Multi-Sensors (MUSES): Builds off of heritage from the Tropospheric Emission Spectrometer (TES) optimal estimation (OE) algorithm to combine a priori and satellite data, including rigorous error analysis diagnostics and observation operators needed for trend analysis, climate model evaluation, and data assimilation; has generic design to incorporate forward model radiances from hyperspectral measurements from multiple sensors into the joint retrieval algorithm.

Fu, Dejian↗
NASA NTRSDate not supplied

Virtual Sensing with Unsupervised Image-to-Image Translation

Earth observing satellites carrying multi-spectral sensors are widely used to monitor the physical and biological states of the atmosphere, land, and oceans. These satellites have different vantage points above the earth and different spectral imaging bands resulting in inconsistent imagery from one to another. This presents challenges in building downstream applications. What if we could generate synthetic bands for existing satellites from the union of all domains? We tackle the problem of generating synthetic spectral imagery for multispectral sensors as an unsupervised image-to-image translation problem with partial labels and introduce a novel shared spectral reconstruction loss. Simulated experiments performed by dropping one or more spectral bands show that cross-domain reconstruction outperforms measurements obtained from a second vantage point. On a downstream cloud detection task, we show that generating synthetic bands with our model improves segmentation performance beyond our baseline. Our proposed approach enables synchronization of multispectral data and provides a basis for more homogeneous remote sensing datasets.

Geostationary satellites↗
NASA NTRS2021

Spatial Registration Assessments for the SNPP and N20 VIIRS Reflective Solar Bands Using Unscheduled Lunar Observations

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a multi-spectral Earth-observing instrument on board the Suomi-NPP (SNPP) and NOAA-20 (N20) spacecraft, with spectral bands ranging in wavelength from 0.41 to 12.2 μm. For the reflective solar bands (RSB), the bands are calibrated on orbit using both solar diffuser (SD) and lunar observations. The lunar observations use near-monthly scheduled spacecraft maneuvers in order to view the Moon within a desired phase angle range. While the primary purpose of the maneuvers is for radiometric calibration, these observations can also be used to characterize the spatial performance of the instrument, including the band-to-band and detector-to-detector registration (BBR/DDR). The Moon can also be observed without spacecraft maneuvers. However, these observations are over a larger phase angle range. While the geometry of these unscheduled observations is more varied, they can still be used to assess the sensor performance. In this work, we will use unscheduled Moon data to analyze the BBR and DDR of the SNPP and N20 VIIRS RSB. For the BBR, we implemented an image cross-correlation approach, which removes the residual oscillations in the trending data when compared to previous methodologies. For the DDR, we developed an edge fitting approach that accounts for the lunar motion across the VIIRS focal plane array on a scan-by-scan basis using lunar and satellite ephemeris data. In our analysis, we find that the BBR and DDR for both VIIRS RSB are stable on orbit.

Band-to-band registration (BBR)↗
NASA NTRS2020

Performance Assessments of the SNPP and N20 VIIRS DNB Using Observations of Bright Stars

The Visible Infrared Imaging Radiometer Suite (VIIRS) on-board the Suomi-NPP (SNPP) and NOAA-20 (N20) spacecrafts is a multi-spectral Earth-observing instrument with spectral channels ranging from the visible to the long-wave infrared. In addition to these spectral channels, the VIIRS instrument features a panchromatic Day/Night band (DNB) with 3 gain stages (low, mid, and high) which allows for Earth-view (EV) radiance retrievals over 7 orders of magnitude. The DNB is able to collect data ranging from dim city lights during nighttime up to reflected sunlight from cloud-tops during daytime. The DNB data collection is divided into 32 aggregation modes, where sub-pixel aggregation is used to maintain a nearly constant pixel footprint on ground during EV data collection throughout each scan. The DNB is also sensitive enough to observe bright stars through the instrument space-view (SV) port. Data from these bright stars can be used to track the temporal stability of the sensor gain and as a method of intercomparison between the two VIIRS instruments. In this work, we will present several sensor performance assessments of the VIIRS DNB using stars. In addition to the sensor gain trending, these assessments will include a comparison of the differences between the daytime and nighttime data due to the effects of stray-light, a comparison of the measured signal as a function of aggregation mode, a comparison between the two high-gain sub-stages, and half-angle mirror side for both the SNPP and N20 VIIRS DNB.

VIIRS↗
NASA NTRS2020

Analysis of the on-orbit Response-Versus-Scan-angle for the MODIS SWIR Bands Derived from Lunar Observations

The Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua platforms is a multi-spectral, whiskbroom scanning radiometer with 36 spectral bands covering wavelengths from 0:4 - 14:2 μm. Among these bands, the 20 reflective solar bands (RSB, 0:4 - 2:1 μm) use an on-board solar difuser (SD) and SD stability monitor (SDSM) to track the detector gain changes on orbit. In addition to this, lunar and Earth-view (EV) observations are used in order to characterize the scan-mirror response versus scan angle (RVS), which improves the Earth-view retrieval over the full width of each scan. For the short-wave infrared bands (SWIR, 1:2 - 2:1 μm), the prelaunch RVS has been used for both Aqua and Terra MODIS throughout each mission. While these bands are not expected to have a significant change in the RVS on-orbit, issues such as electronic crosstalk and an out-of-band optical leak have prevented the use of the Moon for deriving the on-orbit RVS for validation. However, recent improvements to the MODIS lunar calibration for the SWIR bands allow us to mitigate the electronic crosstalk impact and derive the RVS for the SWIR bands. In this work, we will assess the impact of this newly derived RVS on the EV data compared to MODIS Collection 6.1. By comparing to EV data obtained from pseudo-invariant calibration sites at different angles-of-incidence on the scan mirror, we can assess the effectiveness of the newly derived RVS from the Moon. We find that while the change in the RVS on-orbit for the SWIR bands is relatively small, applying a correction to the RVS based on the Moon and SD data can provide an improvement for some bands.

MODIS↗
NASA NTRSDate not supplied

Dual and Multi-Spectral Band SLS Infrared Camera Systems

Based on the recent success of our strained-layer superlattice (SLS)-based infrared (IR) camera that performed Earth imaging from the International Space Station (ISS) in 2019 we have built, what we consider, to be the next generation multi-band SLS imaging system. The Compact Thermal Imager (CTI) was installed on the Robotic Refueling Mission 3 (RRM3) and attached to the exterior of the ISS. From this location we were able to capture 15 million images of a multitude of fires around the globe in 2019. This unexpected trove of data initiated quite a bit of scientific interest to further utilize this imaging capability but would include features to more precisely monitor terrestrial fires and other surface phenomena. To this end, we developed a technique to install specific bandpass filters directly onto the SLS detector hybrid assembly. Utilizing this technique we have built a CTI-2 camera system with two filters, 4 and 11µm, and have made a second detector assembly with six filter bands from 4-12µm. This second system will also be used to supplement Landsat remote imaging monitoring approximate land surface temperatures, monitor evapotranspiration, sea ice and glacier dynamics. The CTI-2 camera is based on a 1,024x1,024 (1kx1k) format SLS detector hybridized to a FLIR ISC0404 readout integrated circuit (ROIC). The six band SLS focal plane array is based on the 640x512 FLIR ISC 9803 ROIC. This camera system is based on the Landsat 8 and 9 Thermal IR Sensors (TIRS) instrument and one of its purposes is to perform ground truthing for the Landsat 8/9 data at higher spectral resolution. Both Landsat TIRS instruments are dual band thermal IR sensors centered on 11 and 12µm (each with about a 1µm bandpass). Both of our SLS systems utilize a Ricor K548 cryocooler. To streamline costs and development time we used commercial optics and both commercial and custom NASA electronic components. A primary feature of these camera systems is the incorporation of specific filters to collect fire data at ~3.9µm and thermal data at ~11µm. The CTI-2 instrument is designed for 37 m /pixel spatial resolution from 410km orbit (ISS orbit). In this paper, we will present the design and performance of the focal plane, optics, electronics and mechanical structure of the dual‑band CTI-2 and the focal plane performance of the six‑band focal plane.

Murzy Jhabvala↗
NASA NTRSDate not supplied

Mission Overview and Status IGARSS Conference

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a strategic climate continuity mission that was defined in the 2010 document Responding to the Challenge of Climate and Environmental Change: NASA’s Plan for Climate-Centric Architecture for Earth Observations and Applications from Space (referred to as the “Climate Initiative”). Scheduled for launch in January 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records [1]. PACE will extend the high quality ocean ecological, ocean biogeochemical, cloud, and aerosol particle data records begun by NASA in the 1990s, building on the exceptional heritages of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), and the Visible Infrared Imaging Radiometer Suite (VIIRS). The PACE project office at NASA’s GSFC is responsible for the satellite development, launch and operations. The NASA Headquarters PACE Program Science office is responsible for supporting the science data processing system and assembling competed community science teams, which will include field-based vicarious calibration and data product validation efforts to support the PACE Project Science team. The mission is planned for launch into a Sun synchronous polar orbit at 676.5 km with an inclination of 98 degrees and a 1 pm local ascending node crossing time. The PACE observatory is comprised of three instruments, an Ocean Color Instrument (OCI) and two polarimeters, the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone). The OCI is the primary instrument on the observatory and is being developed at Goddard Space Flight Center (GSFC). The OCI is a hyperspectral scanning radiometer designed to measure spectral radiances from the ultraviolet to shortwave infrared (SWIR) to enable advanced ocean color and heritage cloud and atmospheric aerosol science [2]. The HARP2 and SPEXone are complimentary instruments on the PACE observatory, acquired outside of GSFC. The HARP2 is multi-spectral, wide swath (supporting atmospheric correction of OCI), and hyper-angular, with capabilities for cloud science that exceed what is capable from OCI alone. The SPEXone is narrow swath, multi-angular, and hyperspectral, with capabilities for atmospheric aerosol science that exceed what can be accomplished with OCI. NASA Headquarters directed the mission development to be guided by a Design-to-Cost (DTC) process. All elements of the mission, other than the cost, are in the DTC trade space. At the heart of the DTC process are the mission studies, performed across all the mission elements. The mission studies were used to define appropriate approaches within and across elements while maximizing science capabilities at a high cost confidence. Mission baseline requirements development is also embedded within the DTC process, as these requirements were not established at the onset of the mission concept development. Baseline mission requirements are a product of the mission studies and are defined by the project office as part of the DTC process. At the time of this writing, OCI, HARP2 and SPEXone are integrated onto the PACE spacecraft, creating the PACE observatory. The instruments and spacecraft subsystems are undergoing combined functional performance testing in preparation for observatory-level environmental testing prior to shipment to the launch site. Here, we will provide an update on science capabilities and observatory readiness for its early 2024 launch.

remote sensing↗
NASA NTRSDate not supplied

Pace OCI Crosstalk Characterization Based on Pre-Launch Testing

Scheduled to launch in 2024, the Ocean Color Instrument (OCI) onboard the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will collect hyperspectral data from 315 nm – 895 nm via two grating spectrometers (in both the blue and red spectral regions) and 9 multi-spectral bands in the short-wave infrared (940 nm – 2260 nm). The increased spectral resolution and radiometric accuracy is expected to improve upon data collected by heritage sensors such as SeaWiFs, MODIS, and VIIRS, allowing new applications in ocean color, aerosol, and cloud science. During ground testing, higher than expected spatial-spectral crosstalk was measured for the hyperspectral bands in the blue spectrograph. Using a monochromatic-collimated light source, light from a single science pixel (1km x 1km) was found to produce crosstalk signals over 31 pixels in the cross-track direction. This spatial augmentation is caused by the spectral crosstalk’s asynchronous spatial movement during Time Delay Integration (TDI). To fully characterized the magnitude and spectral dependency from this, a crosstalk model was developed by synthesizing data collected from monochromatic-collimated light and monochromatic light that filled the OCI optical aperture. The model was validated by showing good agreement between predicted values and other relevant test data collected using both monochromatic and white light sources.

PACE↗