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At least 559 records · Page 31

Formaldehyde Column Density Measurements as a Suitable Pathway to Estimate Near-Surface Ozone Tendencies from Space

In support of future satellite missions that aim to address the current shortcomings in measuring air quality from space, NASA's Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) field campaign was designed to enable exploration of relationships between column measurements of trace species relevant to air quality at high spatial and temporal resolution. In the DISCOVER-AQ data set, a modest correlation (r2 = 0.45) between ozone (O3) and formaldehyde (CH2O) column densities was observed. Further analysis revealed regional variability in the O3-CH2O relationship, with Maryland having a strong relationship when data were viewed temporally and Houston having a strong relationship when data were viewed spatially. These differences in regional behavior are attributed to differences in volatile organic compound (VOC) emissions. In Maryland, biogenic VOCs were responsible for approx.28% of CH2O formation within the boundary layer column, causing CH2O to, in general, increase monotonically throughout the day. In Houston, persistent anthropogenic emissions dominated the local hydrocarbon environment, and no discernable diurnal trend in CH2O was observed. Box model simulations suggested that ambient CH2O mixing ratios have a weak diurnal trend (+/-20% throughout the day) due to photochemical effects, and that larger diurnal trends are associated with changes in hydrocarbon precursors. Finally, mathematical relationships were developed from first principles and were able to replicate the different behaviors seen in Maryland and Houston. While studies would be necessary to validate these results and determine the regional applicability of the O3-CH2O relationship, the results presented here provide compelling insight into the ability of future satellite missions to aid in monitoring near-surface air quality.

Surface Conditions from Column and Vertically Reso↗

Modeling the Height of Young Forests Regenerating from Recent Disturbances in Mississippi using Landsat and ICESat data

Many forestry and earth science applications require spatially detailed forest height data sets. Among the various remote sensing technologies, lidar offers the most potential for obtaining reliable height measurement. However, existing and planned spaceborne lidar systems do not have the capability to produce spatially contiguous, fine resolution forest height maps over large areas. This paper describes a Landsat-lidar fusion approach for modeling the height of young forests by integrating historical Landsat observations with lidar data acquired by the Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud, and land Elevation (ICESat) satellite. In this approach, "young" forests refer to forests reestablished following recent disturbances mapped using Landsat time-series stacks (LTSS) and a vegetation change tracker (VCT) algorithm. The GLAS lidar data is used to retrieve forest height at sample locations represented by the footprints of the lidar data. These samples are used to establish relationships between lidar-based forest height measurements and LTSS-VCT disturbance products. The height of "young" forest is then mapped based on the derived relationships and the LTSS-VCT disturbance products. This approach was developed and tested over the state of Mississippi. Of the various models evaluated, a regression tree model predicting forest height from age since disturbance and three cumulative indices produced by the LTSS-VCT method yielded the lowest cross validation error. The R(exp 2) and root mean square difference (RMSD) between predicted and GLAS-based height measurements were 0.91 and 1.97 m, respectively. Predictions of this model had much higher errors than indicated by cross validation analysis when evaluated using field plot data collected through the Forest Inventory and Analysis Program of USDA Forest Service. Much of these errors were due to a lack of separation between stand clearing and non-stand clearing disturbances in current LTSS-VCT products and difficulty in deriving reliable forest height measurements using GLAS samples when terrain relief was present within their footprints. In addition, a systematic underestimation of about 5 m by the developed model was also observed, half of which could be explained by forest growth that occurred between field measurement year and model target year. The remaining difference suggests that tree height measurements derived using waveform lidar data could be significantly underestimated, especially for young pine forests. Options for improving the height modeling approach developed in this study were discussed.

Li, Ainong↗

Thermal studies of Martian channels and valleys using Termoskan data

The Termoskan instrument on board the Phobos '88 spacecraft acquired the highest spatial resolution thermal data ever obtained for Mars. Included in the thermal images are 2 km per pixel observations of 4 major channel and valley systems: Shalbatana Vallis; Simud Vallis, Hydraotes Chaos, and the associated outflow channel connecting it with the eastern end of Vallis Marineris; Al-Qahira Vallis; and Ma'adim Vallis. Simultaneous broad band visible channel data were obtained for all but Ma'adim Vallis. We find that all 4 valley systems have higher inertias than their surroundings, consistent with previous thermal studies of Martian channels. We conclude that the higher inertia is likely due to some intrinsic difference associated with the channel floors, rather than due only to dark aeolian deposits as suggested by some previous studies. Our conclusion is based largely upon the localized nature of the dark deposits in contrast with the thermal homogeneity of the channel floors. Thus, these channels and valleys show an uncommon (for Mars) connection between morphology and inertia.

Betts, B. H.↗

Comparing Level-2 and Level-3 Satellite Ocean Color Retrieval Validation Methodologies

Many ocean color data applications leverage global spatially composited Level-3(L3) satellite data, because of their regular Earth-grid frame of reference. However, ocean color satellite retrieval performance is routinely evaluated on Level-2 (L2) data at the native satellite swath resolution and geometries. This study assesses how accurately binned and gridded L3 data represent L2 satellite data products via satellite-to-in situ match-up activities.L2 and L3 satellite data retrievals of the photosynthetic pigment chlorophyll-a are compared with a common in situ data set, revealing similar L2 and L3 satellite-to-in situ performance for both MODIS-Aqua and VIIRS-SNPP. This agreement implies that L2 validation results are generally applicable to L3 data. However, uncertainties are introduced during the generation of L3 data from L2 data. L3 data comparisons introduce a wider temporal window between the time of in situ measurement and the time of the satellite observation, which can unintentionally reflect on the quality of the satellite retrieval or algorithm performance. The choice of L3 map projection may introduce additional uncertainty by spatially distorting the true location of the satellite retrievals. Each manipulation of satellite data beyond theinstrument's native spatio temporal reference (L2) reduces the applicability of L2 validation results to higher data processing levels.

Ocean Color↗

Elliptical storm cell modeling of digital radar data

A model for spatial distributions of reflectivity in storm cells was fitted to digital radar data. The data were taken with a modified WSR-57 weather radar with 2.6-km resolution. The data consisted of modified B-scan records on magnetic tape of storm cells tracked at 0 deg elevation for several hours. The MIT L-band radar with 0.8-km resolution produced cross-section data on several cells at 1/2 deg elevation intervals. The model developed uses ellipses for contours of constant effective-reflectivity factor Z with constant orientation and eccentricity within a horizontal cell cross section at a given time and elevation. The centers of the ellipses are assumed to be uniformly spaced on a straight line, with areas linearly related to log Z. All cross sections are similar at different heights (except for cell tops, bottoms, and splitting cells), especially for the highest reflectivities; wind shear causes some translation and rotation between levels. Goodness-of-fit measures and parameters of interest for 204 ellipses are considered.

Altman, F. J.↗

Comparison of the Distribution of the In Situ Ring Current Density Based on Magnetic Field Observations From THEMIS, MMS and Cluster

Abstract We review and extend previous work on the ring current density distribution by applying the curlometer technique to multi‐spacecraft magnetic field data on different spatial scales, directly comparing the results of three space missions: Time History of Events and Macroscale Interactions during Substorms (THEMIS), The Magnetospheric Multiscale (MMS) and Cluster, that have so far accrued decades of data on a range of spatial separation scales. We suggest that the statistical results of Cluster data over two decades can illustrate well the overall form of the ring current (in the ring plane), but can underestimate the current density, while THEMIS (partially) and MMS are able to observe the detailed structure of the ring current and indeed show different behavior on some (smaller) spatial scales. These results suggest that, in addition to the local time and radial form, reported previously, the morphology of the ring current may also depend on magnetic latitude, as influenced by the tilt angle between the inner and outer magnetic fields.

Tan, X. [School of Space and Earth Sciences Beihan↗

The Spectral Image Processing System (SIPS): Software for integrated analysis of AVIRIS data

The Spectral Image Processing System (SIPS) is a software package developed by the Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, in response to a perceived need to provide integrated tools for analysis of imaging spectrometer data both spectrally and spatially. SIPS was specifically designed to deal with data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the High Resolution Imaging Spectrometer (HIRIS), but was tested with other datasets including the Geophysical and Environmental Research Imaging Spectrometer (GERIS), GEOSCAN images, and Landsat TM. SIPS was developed using the 'Interactive Data Language' (IDL). It takes advantage of high speed disk access and fast processors running under the UNIX operating system to provide rapid analysis of entire imaging spectrometer datasets. SIPS allows analysis of single or multiple imaging spectrometer data segments at full spatial and spectral resolution. It also allows visualization and interactive analysis of image cubes derived from quantitative analysis procedures such as absorption band characterization and spectral unmixing. SIPS consists of three modules: SIPS Utilities, SIPS_View, and SIPS Analysis. SIPS version 1.1 is described below.

Kruse, F. A.↗

GMI-IPS: Python Processing Software for Aircraft Campaigns

NASA's Atmospheric Tomography Mission (ATom) seeks to understand the impact of anthropogenic air pollution on gases in the Earth's atmosphere. Four flight campaigns are being deployed on a seasonal basis to establish a continuous global-scale data set intended to improve the representation of chemically reactive gases in global atmospheric chemistry models. The Global Modeling Initiative (GMI), is creating chemical transport simulations on a global scale for each of the ATom flight campaigns. To meet the computational demands required to translate the GMI simulation data to grids associated with the flights from the ATom campaigns, the GMI ICARTT Processing Software (GMI-IPS) has been developed and is providing key functionality for data processing and analysis in this ongoing effort. The GMI-IPS is written in Python and provides computational kernels for data interpolation and visualization tasks on GMI simulation data. A key feature of the GMI-IPS, is its ability to read ICARTT files, a text-based file format for airborne instrument data, and extract the required flight information that defines regional and temporal grid parameters associated with an ATom flight. Perhaps most importantly, the GMI-IPS creates ICARTT files containing GMI simulated data, which are used in collaboration with ATom instrument teams and other modeling groups. The initial main task of the GMI-IPS is to interpolate GMI model data to the finer temporal resolution (1-10 seconds) of a given flight. The model data includes basic fields such as temperature and pressure, but the main focus of this effort is to provide species concentrations of chemical gases for ATom flights. The software, which uses parallel computation techniques for data intensive tasks, linearly interpolates each of the model fields to the time resolution of the flight. The temporally interpolated data is then saved to disk, and is used to create additional derived quantities. In order to translate the GMI model data to the spatial grid of the flight path as defined by the pressure, latitude, and longitude points at each flight time record, a weighted average is then calculated from the nearest neighbors in two dimensions (latitude, longitude). Using SciPya's Regular Grid Interpolator, interpolation functions are generated for the GMI model grid and the calculated weighted averages. The flight path points are then extracted from the ATom ICARTT instrument file, and are sent to the multi-dimensional interpolating functions to generate GMI field quantities along the spatial path of the flight. The interpolated field quantities are then written to a ICARTT data file, which is stored for further manipulation. The GMI-IPS is aware of a generic ATom ICARTT header format, containing basic information for all flight campaigns. The GMI-IPS includes logic to edit metadata for the derived field quantities, as well as modify the generic header data such as processing dates and associated instrument files. The ICARTT interpolated data is then appended to the modified header data, and the ICARTT processing is complete for the given flight and ready for collaboration. The output ICARTT data adheres to the ICARTT file format standards V1.1. The visualization component of the GMI-IPS uses Matplotlib extensively and has several functions ranging in complexity. First, it creates a model background curtain for the flight (time versus model eta levels) with the interpolated flight data superimposed on the curtain. Secondly, it creates a time-series plot of the interpolated flight data. Lastly, the visualization component creates averaged 2D model slices (longitude versus latitude) with overlaid flight track circles at key pressure levels. The GMI-IPS consists of a handful of classes and supporting functionality that have been generalized to be compatible with any ICARTT file that adheres to the base class definition. The base class represents a generic ICARTT entry, only defining a single time entry and 3D spatial positioning parameters. Other classes inherit from this base class; several classes for input ICARTT instrument files, which contain the necessary flight positioning information as a basis for data processing, as well as other classes for output ICARTT files, which contain the interpolated model data. Utility classes provide functionality for routine procedures such as: comparing field names among ICARTT files, reading ICARTT entries from a data file and storing them in data structures, and returning a reduced spatial grid based on a collection of ICARTT entries. Although the GMI-IPS is compatible with GMI model data, it can be adapted with reasonable effort for any simulation that creates Hierarchical Data Format (HDF) files. The same can be said of its adaptability to ICARTT files outside of the context of the ATom mission. The GMI-IPS contains just under 30,000 lines of code, eight classes, and a dozen drivers and utility programs. It is maintained with GIT source code management and has been used to deliver processed GMI model data for the ATom campaigns that have taken place to date.

Damon, M. R.↗

Effects of sensor spatial resolution on cloud properties retrieved from imagery data

Techniques being applied to test the sensitivity of the physical characteristics of clouds, as determined by remote sensing, to the spatial resolution of the scans are described. The sensitivity is being evaluated with an error assessment of data from the AVHRR instrument on Nimbus-7. A spatial coherence analysis is being applied to AVHRR data for a 250 sq km region in the Pacific off the Mexican coast. Errors in the derived cloud cover and radiances from which cloud-free regions and cloud-covered regions are being estimated on the basis of radiance values in pixel-sized areas.

Coakley, J. A., Jr.↗

Anticipating HESSI's Spatially Resolved View of Spectral Evolution

The spectral evolution of observed flares' hard X-ray emission is found to conform to certain patterns in color-color diagrams. 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.

Newton, Elizabeth K.↗

Forest Classification Accuracy as Influenced by Multispectral Scanner Spatial Resolution

The author has identified the following significant results. A supervised classification within two separate ground areas of the Sam Houston National Forest was carried out for two sq meters spatial resolution MSS data. Data were progressively coarsened to simulate five additional cases of spatial resolution ranging up to 64 sq meters. Similar processing and analysis of all spatial resolutions enabled evaluations of the effect of spatial resolution on classification accuracy for various levels of detail and the effects on area proportion estimation for very general forest features. For very coarse resolutions, a subset of spectral channels which simulated the proposed thematic mapper channels was used to study classification accuracy.

Nalepka, R. F.↗

Interfaces between statistical analysis packages and the ESRI geographic information system

Interfaces between ESRI's geographic information system (GIS) data files and real valued data files written to facilitate statistical analysis and display of spatially referenced multivariable data are described. An example of data analysis which utilized the GIS and the statistical analysis system is presented to illustrate the utility of combining the analytic capability of a statistical package with the data management and display features of the GIS.

Masuoka, E.↗

Statistical relation between monthly mean precipitable water and surface-level humidity over global oceans

Monthly summaries of atmospheric soundings taken over 17 years from 49 midocean stations at small islands and weather ships distributed over major oceans are examined. Over tropical oceans, precipitable water is found to be a better predictor of surface-level humidity than surface-level air temperature. A statistical relation in the form of a polynomial is derived; from this relation, the monthly-mean, surface-level mixing ratio can be computed from monthly-mean precipitable water. The root-mean-square differences between the measured and derived values were found to be less than 8 x 10 to the -4th over most ocean areas. Such a relation is useful in deriving large-scale evaporation and latent heat flux data from the ocean, using spaceborne observations. The temporal and spatial variabilities of data deviations from this relation are examined. This relation is found to be applicable to all major ocean basins and can be used to monitor interannual variability. Boundary-layer thermodynamics of different air masses are suggested as an explanation of some characteristics of this relation.

Liu, W. T.↗

Radar interferometry studies of the earth's topography

Digital elevation models (DEMs) which have been acquired using the TOPSAR interferometric radar sensor are directly applicable to geological and geophysical studies. Attention is presently given to three illustrative examples of the use of DEMs: the correction of remote-sensing observations for local slope and topographic effects, topographic expressions of erosion and uplift in alluvial fans, and volcanology. The greatest advantages of TOPSAR over conventional photogrammetry include rapidity of data collection, high spatial and vertical resolution, and the ability to obtain contiguous data independent of cloud cover.

Evans, Diane L.↗

Initial OSSE Sky Survey Observation: Deep Survey in the Virgo Region

The OSSE Virgo survey covered about 1% of the sky, using eight weeks of observing time on the Compton Gamma Ray Observatory, and was performed in conjunction with the EGRET and COMPTEL instruments on CGRO. A fundamental aspect of the survey was the development of data reduction techniques for the scanning data from the OSSE detectors. A direct algebraic approach for spatially deconvolving the data was developed. For the survey work, no background observations isolated from the survey region were acquired, unlike normal discrete source observations. The survey, reduction method covers the mapped region with a grid of pixels, and estimates the flux from each pixel by least-squares agreement with the data, assuming positivity constraints. This technique has led to the detection of compact sources at hard X-ray energies within the Virgo region, and the OSSE survey studies have been extended to the south galactic pole and north Ecliptic pole, with similar analysis techniques.

Cameron, Robert↗

A Novel Machine Learning-Based Gap-Filling of Fine-Resolution Remotely Sensed Snow Cover Fraction Data By Combining Downscaling and Regression

Satellite-based remotely sensed observations of snow cover fraction (SCF) can have data gaps in spatially distributed coverage from sensor and orbital limitations. We mitigate these limitations in the example fine-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data by gap-filling using auxiliary 1-km datasets that either aid in downscaling from coarser-resolution (5 km) MODIS SCF wherever not fully covered by clouds, or else by themselves via regression wherever fully cloud covered. This study’s prototype predicts a 1-km version of the 500-m MOD10A1 SCF target. Due to noncollocatedness of spatial gaps even across input and auxiliary datasets, we consider a recent gap-agnostic advancement of partial convolution in computer vision for both training and predictive gap-filling. Partial convolution accommodates spatially consistent gaps across the input images, effectively implementing a two-dimensional masking. To overcome reduced usable data from noncollocated spatial gaps across inputs, we innovate a fully generalized three-dimensional masking in this partial convolution. This enables a valid output value at a pixel even if only a single valid input variable and its value exist in the neighborhood covered by the convolutional filter zone centered around that pixel. Thus, our gap-agnostic technique can use significantly more examples for training (∼67%) and prediction (∼100%), instead of only less than 10% for the previous partial convolution. We train an example simple three-layer legacy super-resolution convolutional neural network (SRCNN) to obtain downscaling and regression component performances that are better than baseline values of either climatology or MOD10C1 SCF as relevant. Our generalized partial convolution can enable multiple Earth science applications like downscaling, regression, classification, and segmentation that were hindered by data gaps.

Soni Yatheendradas↗

Comparison of satellite based cloud retrieval methods for cirrus and stratocumulus

One difficulty in using satellite remote sensing data is the spatial variability of cloud properties on scales smaller than most meteorological satellite fields of view (approx. 4 to 8 km). The variation is examined of satellite derived cloud cover as a function of the satellite sensor spatial resolution for seven cloud cover retrieval methods: (1) Reflectance threshold; (2) Temperature threshold; (3) ISCCP; (4) HBTM (Hybrid Bispectral Threshold Method); (5) NCLE; (6) Spatial coherence; and (7) Functional Box Counting. The first two methods are simple single spectral thresholds which specify a satellite pixel as cloud filled if the measured reflectance is greater than the threshold, or if the measured equivalent blackbody temperature is less than the threshold. The next three methods are bispectral, using one visible wavelength window channel and one thermal infrared wavelength window. The final two algorithms rely on the spatial variability within the cloud field to determine cloud cover. Spatial coherence assumes only that the cloud field occurs in a single layer and that the clouds are optically thick in the infrared window. LANDSAT Thematic Mapper (TM) data is used to test the spatial resolution dependence of the cloud algorithms. The ISCCP bispectral threshold applied to the full resolution data is used as the reference or truth cloud cover, after which the retrieval methods are applied to the spatial resolutions. Studies of the fraction of pixels in the scene at cloud edge, and of the profile of reflectance and temperature near cloud edges indicate an uncertainty in the reference cloud fraction of 1 to 5 percent.

Parker, Lindsay↗

Combining S-space and image spatial information for characterization of land-cover elements using AVIRIS data

Imaging spectroscopy data provides information that is both spatially and spectrally rich. However,hyperspectral classification and identification procedures are generally based solely on the spectral data. The S-space concept treats the spectrum as a 'spatial' dimension thereby facilitating the exploration of a relationship between the spectral and spatial image domains. This paper presents the ideas that will be researched as well as some preliminary results.

AVIRIS image spectroscopy↗