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NASA NTRS1984

Cartographic accuracy of Landsat-4 MSS and TM image data

Investigations of the cartographic quality of Landsat-4 MSS and TM image data in CCT-p formats have produced rectification accuracies (rmse/xy/ values) of + or - 2/3 to + or - 1 data pixel for both whole and subscene areas using polynomials of the first through third degree. In order to achieve these accuracies with MSS data, 15 or more Ground Control Points (GCPs) are required, whereas with the TM data sets as few as 5-10 GCPs will suffice. Factors which limit the cartographic rectification accuracies of the Landsat-4 data include: (1) spatial resolution of the data; (2) map and digitizing errors; and (3) terrain relief. Of these factors, data resolution is the most significant, limiting the location of GCPs to about + or - 0.5 pixel. Horizontal displacements due to terrain relief can be minimized by selecting GCPs at or near midrange elevations. Overall, the representative rmse(xy) values of + or - 25 and + or - 55 m for TM and MSS data sets are within U.S. National Map Accuracy Standards for cartographic products of 1:100,000 and 1:200,000 scale, respectively.

Welch, R.↗

NASA NTRS1992

Cloud-property retrieval using merged HIRS and AVHRR data

A technique is developed that uses a multispectral, multiresolution method to improve the overall retrieval of mid- to high-level cloud properties by combining HIRS sounding channel data with higher spatial resolution AVHRR radiometric data collocated with the HIRS footprint. Cirrus cloud radiative and physical properties are determined using satellite data, surface-based measurements provided by rawinsondes and lidar, and aircraft-based lidar data collected during the First International Satellite Cloud Climatology Program Regional Experiment in Wisconsin during the months of October and November 1986. HIRS cloud-height retrievals are compared to ground-based lidar and aircraft lidar when possible. Retrieved cloud heights are found to have close agreement with lidar for thin cloud, but are higher than lidar for optically thick cloud. The results of the reflectance-emittance relationships derived are compared to theoretical scattering model results for both water-droplet spheres and randomly oriented hexagonal ice crystals. It is found that the assumption of 10-micron water droplets is inadequate to describe the reflectance-emittance relationship for the ice clouds seen here. Use of this assumption would lead to lower cloud heights using the ISCCP approach. The theoretical results show that use of hexagonal ice crystal phase functions could lead to much improved results for cloud retrieval algorithms using a bispectral approach.

Baum, Bryan A.↗

NASA NTRS1999

Anticipating HESSI's View of Spectral Evolution in Flare Hard X-Ray Emission

The spectral evolution of observed flares' hard X-ray emission is found to conform to certain patterns in color-color diagrams (CCDs). By combining the spectral resolution of BATSE data with the spatial resolution of HXT data, we are able to address the nature of flare energy release and anticipate what kind of observations HESSI may make of the energy release/particle acceleration site in flares. The implications of these observations for theoretical models of acceleration and propagation is considered.

Newton, Elizabeth K.↗

NASA NTRSDate not supplied

Single Field-of-View Sounding Atmospheric Products

The Single Field-of-view Sounder Atmospheric Products (SiFSAP) answer the need for a novel high spatial resolution atmospheric data product for major hyper-spectral infrared (IR) sounder missions. SiFSAP include a complete set of atmospheric vertical profiles, cloud, and surface properties, which are physically retrieved from top-of-atmosphere (TOA) spectral radiances under all-sky conditions via a rigorously defined radiative transfer relationship. By using a state-of-art fast radiative transfer model and a carefully designed optimal estimation based physical retrieval scheme, the SiFSAP algorithm ensures both an ultra-fast data processing speed needed for operational weather applications and the radiometric consistency desired by long-term climate studies. SiFSAP supplement existing operational products by providing data at the native spatial resolution of the sounder instruments, the direct and accurate retrieval of cloud scattering properties, and the establishment of ‘radiance closure.’

single-field-of-view, retrieval, hyperspectral sou↗

NASA NTRSDate not supplied

Mapping Albedo Variations to Constrain the History of the Didymos System From Dart and Liciacube Observations

The Double Asteroid Redirection Test (DART) relied on its high-resolution, broad-band camera DRACO to successfully execute its impact experiment on September 26, 2022. While targeting Dimorphos, the ~150 m diameter moon of the Didymos binary asteroid system, DART also imaged the ~760 m diameter primary. LICIACube, a ride-along CubeSat released from DART prior to impact, carried two cameras, LEIA and LUKE, pan and wide-angle RGB color cameras, respectively, to witness the impact and also image both bodies. In addition to critical data that enabled and observed the impact, these cameras provided the highest spatial resolution data of any binary asteroid system and the first for near-Earth asteroids. Mapping and comparing albedo units on both the primary and secondary and placing them in the context of other S-type asteroids will help constrain the physical relationships and prior interactions between the two bodies and thus reveal the formational and evolutionary history of the Didymos system.

J. M. Sunshine↗

NASA NTRS2021

Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery

Crop yield monitoring is an important component in agricultural assessment. Multispectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 µm), red-edge (0.726 µm), and near-infrared (NIR - 0.865 µm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R^2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R^2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.

agriculture; yield; within-field; corn; soybean; r↗

NASA NTRS2014

Experimental and Computational Sonic Boom Assessment of Boeing N+2 Low Boom Models

Near-field pressure signatures were measured and computational predictions made for several sonic boom models representing Boeing’s Quiet Experimental Validation Concept (QEVC) supersonic transport, as well as for three axisymmetric calibration models. The concept was designed under a NASA Research Announcement (NRA) contract to address environmental and performance goals, specifically for low sonic boom loudness levels and high cruise efficiency, for an aircraft anticipated to enter service in the 2020-timeframe. Wind tunnel tests were conducted on the aircraft and calibration models during Phases I and II of the NRA contract from 2011 to 2013 in the NASA Ames 9- by 7-Foot and NASA Glenn 8- by 6-Foot Supersonic Wind Tunnels. Sonic boom pressure signatures were acquired primarily at Mach 1.6 and 1.8, and force and moment data were acquired from Mach 0.8 to 1.8. The sonic boom test data were obtained using a 2-in. flat-top pressure rail and a 14-in. tapered “reflection factor 1” (RF1) pressure rail. Both rails capture an entire pressure signature in one data point, and successive signatures at varying positions along or above the rail were used to improve data quality through spatial averaging. The sonic boom data obtained by the rails were validated with high-fidelity numerical simulations of offbody pressures. The test results showed good agreement between the computational and experimental data when a variety of testing techniques including spatial averaging of a series of pressure signatures were employed. The two wind tunnels generally produced comparable data.

Durston, Donald A.↗

NASA NTRS2007

High-Resolution N-Band Observations of the Nova RS Ophiuchi with the Keck Interferometer Nuller

We report new observations of the nova RS Ophiuchi (RS Oph) using the Keck Interferometer Nulling Instrument, approximately 3.8 days following the most recent outburst that occurred on 2006 February 12. The Keck Interferometer Nuller (KIN) operates in K-band from 8 to 12.5 pm in a nulling mode, which means that the central broad-band interference fringe is a dark fringe - with an angular width of 25 mas at mid band - rather than the bright fringe used ill a conventional optical interferometer. In this mode the stellar light itself is suppressed by the destructive fringe, effectively enhancing the contrast of the circumstellar material located near the star. By subsequently shifting the neighboring bright fringe onto the center of the source brightness distribution and integrating, a second spatial regime dominated by light from the central portion of the source is almost simultaneously sampled. The nulling technique is the sparse aperture equivalent of the conventional corongraphic technique used in filled aperture telescopes. By fitting the unique KIK inner and outer spatial regime data, we have obtained an angular size of the mid-infrared continuum of 6.2, 4.0. or 5.4 mas for a disk profile, gaussian profile (fwhm), and shell profile respectively. The data show evidence of enhanced neutral atomic hydrogen emission located in the inner spatial regime relative to the outer regime. There is also evidence of a 9.7 micron silicate feature seen outside of this region. Importantly, we see spectral lines excited by the nova flash in the outer region before the blast wave reaches these regions. These lines are from neutral, weakly excited atoms which support the following interpretation. We discuss the present results in terms of a unifying model of the system that includes an increase in density in the plane of the orbit of the two stars created by a spiral shock wave caused by the motion of the stars through the cool wind of the red giant star. These data show the power and potential of the nulling technique which has been developed for the detection of Earth-like planets around nearby stars for the Terrestrial Planet Finder Mission and Darwin missions.

Barry, R. K.↗

NASA NTRS2013

Urban Image Classification: Per-Pixel Classifiers, Sub-Pixel Analysis, Object-Based Image Analysis, and Geospatial Methods: Chapter - 10

Remote sensing methods used to generate base maps to analyze the urban environment rely predominantly on digital sensor data from space-borne platforms. This is due in part from new sources of high spatial resolution data covering the globe, a variety of multispectral and multitemporal sources, sophisticated statistical and geospatial methods, and compatibility with GIS data sources and methods. The goal of this chapter is to review the four groups of classification methods for digital sensor data from space-borne platforms; per-pixel, sub-pixel, object-based (spatial-based), and geospatial methods. Per-pixel methods are widely used methods that classify pixels into distinct categories based solely on the spectral and ancillary information within that pixel. They are used for simple calculations of environmental indices (e.g., NDVI) to sophisticated expert systems to assign urban land covers. Researchers recognize however, that even with the smallest pixel size the spectral information within a pixel is really a combination of multiple urban surfaces. Sub-pixel classification methods therefore aim to statistically quantify the mixture of surfaces to improve overall classification accuracy. While within pixel variations exist, there is also significant evidence that groups of nearby pixels have similar spectral information and therefore belong to the same classification category. Object-oriented methods have emerged that group pixels prior to classification based on spectral similarity and spatial proximity. Classification accuracy using object-based methods show significant success and promise for numerous urban 3 applications. Like the object-oriented methods that recognize the importance of spatial proximity, geospatial methods for urban mapping also utilize neighboring pixels in the classification process. The primary difference though is that geostatistical methods (e.g., spatial autocorrelation methods) are utilized during both the pre- and post-classification steps. Within this chapter, each of the four approaches is described in terms of scale and accuracy classifying urban land use and urban land cover; and for its range of urban applications. We demonstrate the overview of four main classification groups in Figure 1 while Table 1 details the approaches with respect to classification requirements and procedures (e.g., reflectance conversion, steps before training sample selection, training samples, spatial approaches commonly used, classifiers, primary inputs for classification, output structures, number of output layers, and accuracy assessment). The chapter concludes with a brief summary of the methods reviewed and the challenges that remain in developing new classification methods for improving the efficiency and accuracy of mapping urban areas.

Myint, Soe W.↗

NASA NTRS2014

Examining the Link Between Food Prices and Food Insecurity: a Multi-Level Analysis of Maize Price and Birthweight in Kenya

In developing countries where many poor people rely on rainfed, locally produced food for the majority of their caloric intake, shifts in climate and weather patterns can dramatically reduce agricultural productivity. The reduction in agricultural productivity reduces overall food availability and ultimately impacts food accessibility, putting millions of people at risk for malnutrition. In this project we focus on Kenya where roughly a third of households are food insecure. We examine the relationship of the price of maize and low birth weight to help quantify the impact of local food prices on one outcome of household food insecurity. Using spatially referenced data from recent Kenyan Demographic and Health Survey datasets, price data, livelihood information, and a remotely sensed-based measure of local growing season productivity, we develop a dataset linking pregnancies occurring from 2001 to 2008 to the spatially and temporally relevant maize price data. We construct several regression models to examine the impact of local maize prices and remotely sensed based estimates of crop production on infant birth weight - specifically low birth weight. The results of the models highlight the importance of including community crop production to evaluate maize price impacts on low birth weight outcomes. Also, because of the positive correlation between pre-pregnancy maize prices and birth weight, the results suggest that some households may benefit from high prices or that high prices may impact the number of conceptions. More generally, our work demonstrates that multilevel models that account for community-level variation are important for disentangling these complex relationships and can contribute to the discussion of how to design more effective food policies.

Low birth weight↗

NASA NTRS1981

Use of MAGSAT anomaly data for crustal structure and mineral resources in the US Midcontinent

The analysis and preliminary interpretation of investigator-B MAGSAT data are addressed. The data processing included: (1) removal of spurious data points; (2) statistical smoothing along individual data tracks, to reduce the effect of geomagnetic transient disturbances; (3) comparison of data profiles spatially coincident in track location but acquired at different times; (4) reduction of data by weighted averaging to a grid with 1 deg xl deg latitude/longitude spacing, and with elevations interpolated and weighted to a common datum of 400 km; (5) wavelength filtering; and (6) reduction of the anomaly map to the magnetic pole. Agreement was found between a magnitude data anomaly map and a reduce-to-the-pole map supporting the general assumption that, on a large scale (long wavelength), it is induced crustal magnetization which is responsible for major anamalies. Anomalous features are identified and explanations are suggested with regard to crustal structure, petrologic characteristics, and Curie temperature isotherms.

Carmichael, R. S.↗

NASA NTRS2012

Resolution Enhancement of MODIS-Derived Water Indices for Studying Persistent Flooding

Monitoring coastal marshes for persistent flooding and salinity stress is a high priority issue in Louisiana. Remote sensing can identify environmental variables that can be indicators of marsh habitat conditions, and offer timely and relatively accurate information for aiding wetland vegetation management. Monitoring activity accuracy is often limited by mixed pixels which occur when areas represented by the pixel encompasses more than one cover type. Mixtures of marsh grasses and open water in 250m Moderate Resolution Imaging Spectroradiometer (MODIS) data can impede flood area estimation. Flood mapping of such mixtures requires finer spatial resolution data to better represent the cover type composition within 250m MODIS pixel. Fusion of MODIS and Landsat can improve both spectral and temporal resolution of time series products to resolve rapid changes from forcing mechanisms like hurricane winds and storm surge. For this study, using a method for estimating sub-pixel values from a MODIS time series of a Normalized Difference Water Index (NDWI), using temporal weighting, was implemented to map persistent flooding in Louisiana coastal marshes. Ordinarily NDWI computed from daily 250m MODIS pixels represents a mixture of fragmented marshes and water. Here, sub-pixel NDWI values were derived for MODIS data using Landsat 30-m data. Each MODIS pixel was disaggregated into a mixture of the eight cover types according to the classified image pixels falling inside the MODIS pixel. The Landsat pixel means for each cover type inside a MODIS pixel were computed for the Landsat data preceding the MODIS image in time and for the Landsat data succeeding the MODIS image. The Landsat data were then weighted exponentially according to closeness in date to the MODIS data. The reconstructed MODIS data were produced by summing the product of fractional cover type with estimated NDWI values within each cover type. A new daily time series was produced using both the reconstructed 250-m MODIS, with enhanced features, and the approximated daily 30-m high-resolution image based on Landsat data. The algorithm was developed and tested over the Calcasieu-Sabine Basin, which was heavily inundated by storm surge from Hurricane Ike to study the extent and duration of flooding following the storm. Time series for 2000-2009, covering flooding events by Hurricane Rita in 2005 and Hurricane Ike in 2008, were derived. High resolution images were formed for all days in 2008 between the first cloud free Landsat scene and the last cloud-free Landsat scene. To refine and validate flooding maps, each time series was compared to Louisiana Coastwide Reference Monitoring System (CRMS) station water levels adjusted to marsh to optimize thresholds for MODIS-derived time series of NDWI. Seasonal fluctuations were adjusted by subtracting ten year average NDWI for marshes, excluding the hurricane events. Results from different NDWI indices and a combination of indices were compared. Flooding persistence that was mapped with higher-resolution data showed some improvement over the original MODIS time series estimates. The advantage of this novel technique is that improved mapping of extent and duration of inundation can be provided.

Underwood, L. W.↗

NASA NTRS2012

Validation of Shock Layer Radiation: Perspectives for Test Cases

This paper presents a review of the analysis and measurement of radiation data obtained in the NASA Ames Research Center's Electric Arc Shock Tube (EAST) facility. The goal of these experiments was to measure the level of radiation encountered during atmospheric entry. The data obtained from these experiments is highlighted by providing the first spectrally and spatially resolved data for high speed Earth entry and measurements of the CO 4th positive band for conditions relevant to Mars entry. Comparisons of the EAST data with experimental results obtained from shock tunnels at JAXA and the University of Queensland are presented. Furthermore, the paper will detail initial analyses in to the influence and characterization of the measure non-equilibrium radiation.

Brandis, Aaron↗

DOE OSTI2024

Deep learning-driven super-resolution in Raman hyperspectral imaging: Efficient high-resolution reconstruction from low-resolution data

Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. In conclusion, the results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs.

42 ENGINEERING↗

NASA NTRS2003

Software for Simulating Remote Sensing Systems

The Application Research Toolbox (ART) is a collection of computer programs that implement algorithms and mathematical models for simulating remote sensing systems. The ART is intended to be especially useful for performing design-tradeoff studies and statistical analyses to support the rational development of design requirements for multispectral imaging systems. Among other things, the ART affords a capability to synthesize coarser-spatial-resolution image-data sets from finer-spatial-resolution data sets and multispectral-image-data products from hyperspectral-image-data products. The ART also provides for synthesis of image-degradation effects, including point-spread functions, misregistration of spectral images, and noise. The ART can utilize real or synthetic data sets, along with sensor specifications, to create simulated data sets. In one example of a typical application, simulated data pertaining to an existing multispectral sensor system are used to verify the data collected by the system in operation. In the case of a proposed sensor system, the simulated data can be used to conduct trade studies and statistical analyses to ensure that the sensor system will satisfy the requirements of potential scientific, academic, and commercial user communities.

Zanoni, Vicki↗

NASA NTRS2003

Software for Simulating Remote Sensing Systems

Vicki Zanoni↗

NASA NTRS1987

Requirements and principles for the implementation and construction of large-scale geographic information systems

This paper provides a brief survey of the history, structure and functions of 'traditional' geographic information systems (GIS), and then suggests a set of requirements that large-scale GIS should satisfy, together with a set of principles for their satisfaction. These principles, which include the systematic application of techniques from several subfields of computer science to the design and implementation of GIS and the integration of techniques from computer vision and image processing into standard GIS technology, are discussed in some detail. In particular, the paper provides a detailed discussion of questions relating to appropriate data models, data structures and computational procedures for the efficient storage, retrieval and analysis of spatially-indexed data.

Smith, Terence R.↗

NASA NTRS1991

Environmental analysis using integrated GIS and remotely sensed data - Some research needs and priorities

This paper discusses some basic scientific issues and research needs in the joint processing of remotely sensed and GIS data for environmental analysis. Two general topics are treated in detail: (1) scale dependence of geographic data and the analysis of multiscale remotely sensed and GIS data, and (2) data transformations and information flow during data processing. The discussion of scale dependence focuses on the theory and applications of spatial autocorrelation, geostatistics, and fractals for characterizing and modeling spatial variation. Data transformations during processing are described within the larger framework of geographical analysis, encompassing sampling, cartography, remote sensing, and GIS. Development of better user interfaces between image processing, GIS, database management, and statistical software is needed to expedite research on these and other impediments to integrated analysis of remotely sensed and GIS data.

Davis, Frank W.↗