Image navigation and registration (INR) processing for the Geostationary Lightning Mapper (GLM) assigns geographic coordinates to lightning events using orbit and attitude telemetry and a geometric calibration that matches coastline features from the GLM background scene against a digital map. Required performance is expressed as an optical angle at the aperture of the instrument of 112 μrad (3σ), equivalent to 4 km at the subsatellite point. This is only one-half the linear dimension of the ground footprint of a detector element at the center of the field of view and is challenging to both attain and validate. Our validation approach uses imagery from the Advanced Baseline Imager (ABI) Band 3 (B03), the ABI channel closest to the GLM spectrally, with 1-km pixel resolution at the subsatellite point, as an INR reference. The finer spatial resolution and the high-accuracy INR (<28 μrad, 3σ) of ABI make it well suited for this application. Since both instruments are on the same platform, no parallax correction is required. We measure positions of feature templates extracted from the GLM background scene relative to the ABI reference image to assign spatial coordinates to the center-points of each template. Almost any feature, except clear-sky ocean scenes, can be used for matching, which allows for spatially dense measurement of INR errors in the forward navigation calculation of a GLM pixel. Cloud motion is compensated during the time between the acquisition of the ABI reference image and the GLM background scene, which further improves validation accuracy. We show that GLM on both GOES-16 and -17 spacecraft satisfy their INR requirements. The spatial and temporal density of validation measurements permits examination of smaller systematic errors, including thermally driven alignment offsets, a discontinuity between focal plane hemispheres arising from a readout artifact, the optical distortion of the GLM lens, and a calibration of the telescope focal length.
K. Chance, X. Liu, C. Chan Miller, G. González Abad, G. Huang, C. Nowlan, A. Souri, R. Suleiman, K. Sun, H. Wang, L. Zhu, P. Zoogman, J. Al-Saadi, J. -C. Antuña-Marrero, J. Carr, R. Chatfield, M. Chin, R. Cohen, D. Edwards, J. Fishman, D. Flittner, J. Geddes, M. Grutter, J. Herman, D. Jacob, S. Janz, J. Joiner, J. Kim, N. Krotkov, B. Lefer, R. Martin, O. Mayol-Bracero, A. Naeger, M. Newchurch, G. Pfister, K. Pickering, R. Pierce, C. Rivera Cárdenas, A. Saiz-Lopez, W. Simpson, E. Spinei, R. J. Spurr, J. Szykman, O. Torres, J. Wang
The NASA/Smithsonian Tropospheric Emissions: Monitoring of Pollution (TEMPO; tempo.si.edu) satellite instrument will measure atmospheric pollution and much more over Greater North America at high temporal resolution (hourly or better in daylight, with selected observations at 10 minute or better sampling) and high spatial resolution (10 km2 at the center of the field of regard). It will measure ozone (O3) profiles (including boundary layer O3), and columns of nitrogen dioxide (NO2), nitrous acid (HNO2), sulfur dioxide (SO2), formaldehyde (H2CO), glyoxal (C2H2O2), water vapor (H2O), bromine oxide (BrO), iodine oxide (IO), chlorine dioxide (OClO), as well as clouds and aerosols, foliage properties, and ultraviolet B (UVB) radiation. The instrument has been delivered and is awaiting spacecraft integration and launch in 2022. This talk describes a selection of TEMPO applications based on the TEMPO Green Paper living document (http://tempo.si.edu/publications.html).
Applications to air quality and health will be summarized. Other applications presented include: biomass burning and O3 production; aerosol products including synergy with GOES infrared measurements; lightning NOx; soil NOx and fertilizer application; crop and forest damage from O3; chlorophyll and primary productivity; foliage studies; halogens in coastal and lake regions; ship tracks and drilling platform plumes; water vapor studies including atmospheric rivers, hurricanes, and corn sweat; volcanic emissions; air pollution and economic evolution; high-resolution pollution versus traffic patterns; tidal effects on estuarine circulation and outflow plumes; air quality response to power blackouts and other exceptional events.
The Johns Hopkins University Applied Physics Laboratory (JHU/APL) is developing a compact, light-weight, and lowpower midwave-infrared (MWIR) imager called the Compact Midwave Imaging Sensor (CMIS), under the support of the NASA Earth Science Technology Office Instrument Incubator Program. The goal of this CMIS instrument development and demonstration project is to increase the technical readiness of CMIS, a multi-spectral sensor capable of retrieving 3D winds and cloud heights 24/7, for a space mission. The CMIS instrument employs an advanced MWIR detector that requires less cooling than traditional technologies and thus permits a compact, low-power design, which enables accommodation on small spacecraft such as CubeSats. CMIS provides the critical midwave component of a multi-spectral sensor suite that includes a high-resolution Day-Night Band and a longwave infrared (LWIR) imager to provide global cloud characterization and theater weather imagery. In this presentation, an overview of the CMIS project, including the high-level sensor design, the concept of operations, and measurement capability will be presented. System performance for a variety of different scenes generated by a cloud resolving model (CRM) will also be discussed.
KEYWORDS: Modulation transfer functions, Signal to noise ratio, Sensors, Radiometry, Systems modeling, Imaging systems, Digital filtering, Scanning probe microscopy, Point spread functions, Data modeling
Traditionally, optical remote sensing payload design satisfies highly defined specifications arrived at by consensus of the
scientific constituency. Designs are constrained by required performance such as resolution, Modulation Transfer
Function (MTF), and Signal-to-Noise-Ratio (SNR). Payload designers satisfy the specification by performing hardware
and cost trades. This process may lack continuous feedback between the performance of the scientific algorithms and the
payload design, potentially missing optimal design points.
The traditional method has produced separate and specific designs for imagery (over-sampling ratio Q > 0.8) vs.
radiometry (Q < 0.8). Radiometers are scientifically precise, with highly accurate scene collection over a tightly defined
pixel size exclusive of other scene points, often across several spectral channels. Imagers reveal sharper features, but
have considerable "bleeding" of scene radiance into adjacent pixels, causing errors in application of multispectral
scientific algorithms.
Recently, we created end-to-end models that optimize end scientific data products by considering the payload design and
data processing algorithms together, rather than simply satisfying a payload specification. In this process, we uncovered
optimal payload design points and insights.
We explore end-to-end modeling results that show an optimal single converged payload design, and data processing
algorithms that produce simultaneous radiometer and imager products. We show how payload design choices for
Instantaneous Field of View (IFOV) and Ground Sampling Distance (GSD) maximize SNR for multiple data products,
resulting in an optimized design that increases flexibility of space assets. This approach is beneficial as we move towards
distributed and fused image systems.
Accurate and automatic image navigation and registration (INR) of remotely sensed data will be an essential element of future NASA satellite observation systems. INR describes the process by which geographic locations of the image pixels are computed and successive images from the same sensor are aligned to each other over time. For sensors such as the Advanced Geosynchronous Studies Imager (AGSI), a number of distortions prevent successive images from being perfectly registered to each other or to a fixed coordinate system. Most distortions in such images are the combined effects of sensor operation, satellite orbit and attitude, and atmospheric and terrain effects. These distortions are usually corrected by two methods; systematic correction, which relies on image acquisition models taking into account satellite orbit and attitude, sensor characteristics, platform/sensor relationship, and terrain models, and precision correction, which is feature-based, starting from the result of the systematic correction, and refining the geolocation or relative registration to subpixel precision. This paper describes the AGSI INR requirements and concepts, the image navigation model, a description of some potential precision correction methods utilizing edge and wavelet features, and a study of all the different error sources. The issues of swath-to-swath correlation and channel-to- channel coregistration are also described.
The AGSI design permits scan rates slow enough to detect stars as dim as visual magnitude eight in the coarse of normal imaging. This gives many times the number of stars seen with the current Geosynchronous Operational Environmental Satellite (GOES) Imager and can eliminate the need to schedule special star looks. Besides improving image navigation and registration accuracy, the frequency observations enable the Imager to fly aboard a spacecraft with loose attitude control. The slow scan rate is thanks to the long CCD detector arrays and to the time delay integration made possible by the unique windshield wiper scan pattern. The Bremer star detection algorithm describe can be implemented onboard to reduce downlink requirements and so permit star detection across a dedicated full silicon passband. The wide passband increases the number of detectable stars, and cross checking with narrower science passbands eliminates false alarms from high energy particles while preserving low detection thresholds and sensitivity.
This paper demonstrates improvements in GOES on-orbit Image Navigation and Registration (INR) performance obtained from a careful method of landmark selection, coupled with changes in landmark measurement techniques. An iterative process was used which began with a search for characteristics influencing landmark stability over time, such as topography, thermal changes, and apparent sea/land boundaries, and ended with measurable improvements in the areas of navigation, within- frame, frame-to-frame, and channel-to-channel registration. An operational shift from manual to automatic landmark correlation proved to be an essential requirement influencing the overall results. Specification requirements are used as an absolute means to estimate INR performance changes for GOES 8, but whenever possible, results obtained here are also placed in perspective by comparison with previous GOES INR results. Finally, the methodology and tools described are applied to the new GOES 10 satellite in order to estimate its performance.
An independent audit of the in-orbit behavior of the GOES-8 and GOES-9 satellites has been conducted for the NASA/GSFC. This audit utilized star and landmark observations from the GOES imager to determine long-term histories for spacecraft attitude, orbital position, and instrument internal misalignments. The paper presents results from this audit. Long-term drifts are found in the attitude histories, whereas the misalignment histories are shown to be diurnally stable. The GOES image navigation and registration system is designed to compensate for instrument internal misalignments, and both the diurnally repeatable and drift components of the attitude. Correlations between GOES-8 and GOES-9 long-term roll and pitch drifts implicate the Earth sensor as the origin of these observed drifts. This results clearly demonstrates the enhanced registration stability to be obtained with stellar inertial attitude determination replacing or supplementing Earth sensor control on future GOES missions.
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