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NASA NTRSDate not supplied

Spaceborne Remote Sensing of Hail: Retrievals, Climatologies, and Challenges Going Forward

In addition to the myriad threats that severe hailstorms pose to society, infrastructure and agriculture, severe hail is difficult to measure in situ, and surface-based hail reporting and detection methods are inconsistent and subject to geographical or societal biases. This motivates the use of spaceborne remote-sensing platforms to retrieve hail and construct climatologies in the most globally consistent way. Passive-microwave algorithms leverage the sensitivity of spaceborne passive-microwave radiometers to scattering by hail, particularly in the channels from 10 to 89 GHz. These retrievals are used to construct global climatologies of severe hail. The Bang and Cecil (2019) retrievals has been applied to several different spaceborne sensors: the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Global Precipitation Measurement Mission (GPM) Microwave Imager, Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors and used to construct [near] global climatologies of severe hail. This retrieval, and others, have been tested using Global Precipitation Measurement (GPM) Ku-band precipitation radar, to assess their effectiveness and regional variability. A successful retrieval and climatology are those that correspond tightly to radar reflectivity and give minimal appearance of regional biases, especially with latitude. Satellite platforms offer consistent observations, even in remote, data-sparse, and oceanic regions that ground-based networks exclude. There are, however, potential disconnects between the processes identified aloft by the satellite and the resultant weather at the ground, leading to uncertainties in the retrievals that may propagate into satellite-based climatologies, particularly in the Tropics, where there are abundant strong - but not necessarily hailing - storms that are strongly represented in the current satellite climatologies. There are ongoing efforts to assess and mitigate the contributing factors to these uncertainties, chiefly among them the effects of non-uniform beam filling in the passive-microwave footprint, and the relationships between the size distributions of hailstones aloft and the dynamic processes and environments with which they interact throughout their trajectories.

Sarah D Bang↗

NASA NTRS1993

Evaluation of the Shuttle remote manipulator

The objective initially proposed was to analyze shuttle remote manipulator (SRM) performance data collected during a Shuttle Flight. The data was to consist of video TRAC data collected via a video recorder. Unfortunately, the flight never collected the data due to higher priority experiments superseding it. As a result, the research team at Texas A&M was directed to work on several other pressing issues regarding the TRAC sensor. All but one of these issues were reported earlier in the form of periodic status reports. In fulfillment of the grant conditions, the last issue investigated is being reported as the final report. Ordinarily, a TRAC sensor determines the orientation of an object by analyzing the image reflected from a mirror target. The concern addressed is to develop a method for using the TRAC sensor when the target does not reflect a usable image.

Everett, Louis J.↗

NASA NTRS1996

Programmable Multiresolution CMOS Active Pixel Sensor

CMOS active pixel sensors (APS) allow the flexibility of placing signal processing circuitry on the imaging focal plane. The multiresolution CMOS APS can perform block averaging on-chip to eliminate the off-chip software intensive image processing. This 128 x 128 APS array can be read out at any user-defined resolution by configuring a set of digital shift registers. The resolution frame rate is 30 Hz with higher rates for all other image resolutions.

APS↗

NASA NTRS1989

Analysis of the boreal forest-tundra ecotone: A test of AVIRIS capabilities in the Eastern Canadian subarctic

A comparison was conducted between ground reflectance spectra collected in Schefferville, Canada and imaging spectrometer observations acquired by the AVIRIS sensor in a flight of the ER-2 Aircraft over the same region. The high spectral contrasts present in the Canadian Subarctic appeared to provide an effective test of the operational readiness of the AVIRIS sensor. Previous studies show that in this location various land cover materials possess a wide variety of visible/near infrared reflectance properties. Thus, this landscape served as an excellent test for the sensing variabilities of the newly developed AVIRIS sensor. An underlying hypothesis was that the unique visible/near infrared spectral reflectance patterns of Subarctic lichens could be detected from high altitudes by this advanced imaging spectrometer. The relation between lichen occurrence and boreal forest-tundra ecotone dynamics was investigated.

Goward, Samuel N.↗

NASA NTRS1984

Evoluation of Landsat-4 image quality for the interpretation of forest, agricultural and soil resources

Landsat-4 Multispectral Scanner (MSS) and Thematic Mapper (TM) sensor performance is being evaluated through the analysis of images generated by computer-compatible tape data and commercially available film products. Natural targets are used to evaluate spectral variability, spatial resolution, radiometric sensitivity, and geometric fidelity. Spectral characteristics are being evaluated through the interpretation of the image tone and texture variability of known features, while spatial characteristics are evaluated through lineal and areal estimates of similar features and the plotting and analysis of residual errors derived from regressions between relative image coordinates and map coordinates.

Degloria, S. D.↗

NASA NTRS1986

The Sequential Filter Imaging Radiometer (SFIR), a new instrument configuration for Earth observations

The sequential filter imaging radiometer (SFIR) concept is presented, contrasted with other sensor configurations, and its strengths and weaknesses discussed. In a pushbroom SFIR the optics images the scene onto a long, narrow area array. The length of the array defines the field of view. The spectral defining filters are sequentially placed over the full array, a sample of data for that band taken, and then the next filter placed in front of the array. All of the filters are placed over the array in the time that it takes the image of the scene to advance the array width. Thus the entire scene is observed in each band. Advantages of the SFIR are: spectral bands can be broad, narrow or overlap; it is easy to improve signal to noise ratio; and it is simple to make bands polarized. Its main disadvantage is that it requires more detectors than other instrument configurations.

Maxwell, M. S.↗

NASA NTRS1988

The sequential filter imaging radiometer (SFIR)- A new instrument configuration for earth observations

The sequential filter imaging radiometer (SFIR) concept is presented, contrasted with other sensor configurations, and its strengths and weaknesses discussed. In a pushbroom SFIR the optics images the scene onto a long, narrow area array. The length of the array defines the field of view. The spectral defining filters are sequentially placed over the full array, a sample of data for that band taken, and then the next filter is placed in front of the array. All filters are placed over the array in the time that it takes the image of the scene to advance the array width. Thus, the entire scene is observed in each band. Advantages of the SFIR are: spectral bands can be broad, narrow or overlap; it is easy to improve signal to noise ratio; and it is simple to make bands polarized. Its main disadvantage is that it requires more detectors than other instrument configurations.

Maxwell, Marvin S.↗

NASA NTRS1991

Optical design of the Moderate Resolution Imaging Spectrometer - Tilt (MODIS-T) for the Earth Observing System (Eos)

The Moderate Resolution Imaging Spectrometer (MODIS) is an Earth viewing sensor that is planned as a facility instrument for the Earth Observing System (Eos) scheduled to begin functioning in the late 1990's. The MODIS is composed of two mutually supporting sensors one of which is MODIS-T, where 'T' signifies a tiltable along-track field of view. MODIS-T is a 32 channel imaging spectrometer with a required 10 nm to 15 nm spectral resolution (FWHM) in the 400 nm to 880 nm spectral range with less than 2.3 percent instrument induced linear polarization. The instrument provides at nadir a 33 km by 1500 km swath with a 1.1 km spatial resolution and an along-track pointing capability of +/- 50 deg about nadir. The heart of the optical design consists of a f/3 grating-type reflecting Schmidt camera.

Maymon, Peter W.↗

NASA NTRS1999

Venus Aerobot Surface Science Imaging System (VASSIS)

The VASSIS task was to design and develop an imaging system and container for operation above the surface of Venus in preparation for a Discovery-class mission involving a Venus aerobot balloon. The technical goals of the effort were to: a) evaluate the possible nadir-viewed surface image quality as a function of wavelength and altitude in the Venus lower atmosphere, b) design a pressure vessel to contain the imager and supporting electronics that will meet the environmental requirements of the VASSIS mission, c) design and build a prototype imaging system including an Active-Pixel Sensor camera head and VASSIS-like optics that will meet the science requirements. The VASSIS science team developed a set of science requirements for the imaging system upon which the development work of this task was based.

Greeley, Ronald↗

NASA NTRSDate not supplied

Use of TEMPO as a Proxy for Hyperspectral Geostationary Ocean Color Measurements from the GeoXO OCX Instrument: Harnessing Machine Learning and Principal Component Techniques for Atmospheric and Glint Correction

Retrievals of ocean color from space are important for better understanding the ocean ecosystem. The launch of atmospheric geostationary hyperspectral sensors such as TEMPO, provides a unique opportunity to examine the diurnal variability in ocean ecology. While TEMPO does not have as high spatial resolution or full spectral coverage as planned coastal ocean sensors such as the Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) or GeoXO Ocean Color instrument (OCX), its hourly measurements provide coverage of regions such as Lake Erie and the Gulf of Mexico at spatial scales of approximately 5 km. These data can be useful for testing new algorithms. We will apply our newly developed machine learning based atmospheric correction approach for ocean color retrievals to TEMPO data. Our approach begins by decomposing measured radiances from hyperspectral sensors into spectral features that describe the scattering and absorption of the atmosphere as well as the underlying surface reflectance. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from collocated MODIS/VIIRS physically-based retrievals. This machine learning approach does not rely on radiative transfer modeling, and the use of MODIS/VIIRS data for training accounts for possible calibration b in hyperspectral data. Previously, we applied our approach using blue and UV wavelengths with the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can estimate ocean color properties in less-than-ideal conditions such as lightly to moderately clouded conditions as well as sun glint and thus improve the spatial coverage of ocean color measurements. TEMPO provides an opportunity to improve on this approach since it will provide collocated measurements at green and red wavelengths that were not available from OMI and TROPOMI and are important particularly for coastal waters. Additionally, our technique can be applied early in the mission and has potential to demonstrate the value of near real time ocean color products that are important for monitoring of harmful algae blooms and other oceanic phenomena.

Zachary Fasnacht↗

NASA NTRS1985

Large space antenna technology applied to radar-imaging, rain-rate measurements, and ocean wind sensing

During the last decade, the utility of spaceborne microwave remote sensing systems for ocean windspeed measurement, ocean wave imaging and sea ice studies was demonstrated. Development of large space antennas offers some interesting possibilities for rain rate measurements, ocean and ice studies, and radar imaging. The joint use of active and passive sensors using the 15 m antenna for ocean, ice, and soil moisture studies; rain rate measurements; and radar imaging is considered. Verification of the frequency agile rain radar concept with Shuttle offers the possibility of much needed rain rate statistics over the ocean.

Moore, R. K.↗

NASA NTRS1997

Utility of BRDF Models for Estimating Optimal View Angles in Classification of Remotely Sensed Images

Statistical classification of remotely sensed images attempts to discriminate between surface cover types on the basis of the spectral response recorded by a sensor. It is well known that surfaces reflect incident radiation as a function of wavelength producing a spectral signature specific to the material under investigation. Multispectral and hyperspectral sensors sample the spectral response over tens and even hundreds of wavelength bands to capture the variation of spectral response with wavelength. Classification algorithms then exploit these differences in spectral response to distinguish between materials of interest. Sensors of this type, however, collect detailed spectral information from one direction (usually nadir); consequently, do not consider the directional nature of reflectance potentially detectable at different sensor view angles. Improvements in sensor technology have resulted in remote sensing platforms capable of detecting reflected energy across wavelengths (spectral signatures) and from multiple view angles (angular signatures) in the fore and aft directions. Sensors of this type include: the moderate resolution imaging spectroradiometer (MODIS), the multiangle imaging spectroradiometer (MISR), and the airborne solid-state array spectroradiometer (ASAS). A goal of this paper, then, is to explore the utility of Bidirectional Reflectance Distribution Function (BRDF) models in the selection of optimal view angles for the classification of remotely sensed images by employing a strategy of searching for the maximum difference between surface BRDFs. After a brief discussion of directional reflect ante in Section 2, attention is directed to the Beard-Maxwell BRDF model and its use in predicting the bidirectional reflectance of a surface. The selection of optimal viewing angles is addressed in Section 3, followed by conclusions and future work in Section 4.

Valdez, P. F.↗

NASA NTRS2011

Online Visualization and Analysis of Global Half-Hourly Infrared Satellite Data

nfrared (IR) images (approximately 11-micron channel) recorded by satellite sensors have been widely used in weather forecasting, research, and classroom education since the Nimbus program. Unlike visible images, IR imagery can reveal cloud features without sunlight illumination; therefore, they can be used to monitor weather phenomena day and night. With geostationary satellites deployed around the globe, it is possible to monitor weather events 24/7 at a temporal resolution that polar-orbiting satellites cannot achieve at the present time. When IR data from multiple geostationary satellites are merged to form a single product--also known as a merged product--it allows for observing weather on a global scale. Its high temporal resolution (e.g., every half hour) also makes it an ideal ancillary dataset for supporting other satellite missions, such as the Tropical Rainfall Measuring Mission (TRMM), etc., by providing additional background information about weather system evolution.

Liu, Zhong↗

NASA NTRS2005

The New CCSDS Image Compression Recommendation

The Consultative Committee for Space Data Systems (CCSDS) data compression working group has recently adopted a recommendation for image data compression, with a final release expected in 2005. The algorithm adopted in the recommendation consists of a two-dimensional discrete wavelet transform of the image, followed by progressive bit-plane coding of the transformed data. The algorithm can provide both lossless and lossy compression, and allows a user to directly control the compressed data volume or the fidelity with which the wavelet-transformed data can be reconstructed. The algorithm is suitable for both frame-based image data and scan-based sensor data, and has applications for near-Earth and deep-space missions. The standard will be accompanied by free software sources on a future web site. An Application-Specific Integrated Circuit (ASIC) implementation of the compressor is currently under development. This paper describes the compression algorithm along with the requirements that drove the selection of the algorithm. Performance results and comparisons with other compressors are given for a test set of space images.

Yeh, Pen-Shu↗

NASA NTRS2021

A Robust Hybrid Deep Learning Model for Spatiotemporal Image Fusion

Dense time-series remote sensing data with detailed spatial information are highly desired for the monitoring of dynamic earth systems. Due to the sensor tradeoff, most remote sensing systems cannot provide images with both high spatial and temporal resolutions. Spatiotemporal image fusion models provide a feasible solution to generate such a type of satellite imagery, yet existing fusion methods are limited in predicting rapid and/or transient phenological changes. Additionally, a systematic approach to assessing and understanding how varying levels of temporal phenological changes affect fusion results is lacking in spatiotemporal fusion research. The objective of this study is to develop an innovative hybrid deep learning model that can effectively and robustly fuse the satellite imagery of various spatial and temporal resolutions. The proposed model integrates two types of network models: super-resolution convolutional neural network (SRCNN) and long short-term memory (LSTM). SRCNN can enhance the coarse images by restoring degraded spatial details, while LSTM can learn and extract the temporal changing patterns from the time-series images. To systematically assess the effects of varying levels of phenological changes, we identify image phenological transition dates and design three temporal phenological change scenarios representing rapid, moderate, and minimal phenological changes. The hybrid deep learning model, alongside three benchmark fusion models, is assessed in different scenarios of phenological changes. Results indicate the hybrid deep learning model yields significantly better results when rapid or moderate phenological changes are present. It holds great potential in generating high-quality time-series datasets of both high spatial and temporal resolutions, which can further benefit terrestrial system dynamic studies. The innovative approach to understanding phenological changes’ effect will help us better comprehend the strengths and weaknesses of current and future fusion models.

spatiotemporal fusion↗

NASA NTRS2003

In-Flight MTF Analysis of ADAR 5500 Aircraft Sensor

The Modulation Transfer Function (MTF) of an imaging system is one parameter that can be used to describe the spatial resolution or the image quality of an imaging system. MTF is the system's frequency response to an input. The MTF at a specific frequency normally ranges from 0-1, where 0 indicates no frequency response and 1 indicates perfect frequency response. The MTF value at Nyquist frequency is calculated because the Nyquist frequency is the maximum sampling frequency of the system. MTF analysis was performed on data acquired with an ADAR 5500 flown by Positive Systems on September 13-14, 2000 over Brookings, SD. Four edges from ground targets were used as inputs to the system. The system's MTF was calculated using a Matlab-based algorithm, which applied edge detection, numerical differentiation, and Fourier transformation methods to the target images.

Zanoni, Vicki↗

NASA NTRS1995

Active pixel sensor with intra-pixel charge transfer

An imaging device formed as a monolithic complementary metal oxide semiconductor integrated circuit in an industry standard complementary metal oxide semiconductor process, the integrated circuit including a focal plane array of pixel cells, each one of the cells including a photogate overlying the substrate for accumulating photo-generated charge in an underlying portion of the substrate, a readout circuit including at least an output field effect transistor formed in the substrate, and a charge coupled device section formed on the substrate adjacent the photogate having a sensing node connected to the output transistor and at least one charge coupled device stage for transferring charge from the underlying portion of the substrate to the sensing node.

Fossum, Eric R.↗

NASA NTRS2003

Active pixel sensor with intra-pixel charge transfer

Fossum, Eric R.↗