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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Atmospheric Infrared Sounder Version 7 Near-Real-Time Product and Imagery Released by NASA GES DISC

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has been the home of data processing, archive, and distribution services for data from the Atmospheric Infrared Sounder (AIRS) mission since its launch in 2002. The GES DISC provides service to both AIRS routine and Near Real-Time (NRT) products. The AIRS NRT products are an important element in the Land, Atmosphere Near real-time Capability for EOS (LANCE). In collaboration with AIRS Project, the GES DISC has just released products from the Version 7 algorithm. The new version algorithm provides significant improvements over the previous version. The most substantial advances are: improved consistency between day and night water vapor; improved total column ozone and temperature; improved infrared-only (IR-only) retrievals, especially in high latitude regions; an improved Stochastic Cloud Clearing Neural Network used as a first guess at the initial value in the iterative retrieval process; and removal of ambiguity in surface classification in the IR-only retrieval algorithm. In addition, the GES DISC produces AIRS NRT imagery. The AIRS NRT imagery are generated by mosaicking and mapping the available AIRS 6-minute retrieval granules to a global projection. The images are constantly refreshed when new granules are produced. The AIRS NRT Viewer and LANCE Worldview provide visualization services to online users for AIRS NRT imagery. The data products used to generate this imagery include atmospheric temperature, humidity, precipitation, cloud, Dust Score, CO, and SO2. In this presentation, we will demonstrate visualization of the AIRS NRT imagery from the new Version 7, and demonstrate some improvements over the previous version. Progress on improving the AIRS NRT imagery, a collaboration project with the AIRS Applications Development Team at NASA Jet Propulsion Laboratory (JPL), will also be presented.

Feng Ding↗
NASA NTRS2023

Automated Global-Scale Detection and Characterization of Anthropogenic Activity using Multi-Source Satellite-Based Remote Sensing Imagery

Satellite-based remote sensing imagery is an effective means for detecting objects and structures in support of many applications. However, detecting the spatial and temporal bounds of a specific activity in satellite imagery is inherently more complex and research in this area is nascent. One reason for this is that describing an activity implies defining both spatial and temporal bounds and while activity is inherently continuous in nature, the geospatial (imagery) time series for any particular swath of ground provided by satellite imagery is relatively sparse and discrete in comparison. The IARPA Space-Based Machine Automated Recognition Technique (SMART)1 program is the first large-scale research program to target advancing the state of the art for automatically detecting, characterizing, and monitoring large-scale anthropogenic activity in global, multispectral satellite imagery. The program has two primary research objectives: 1) the “harmonization” of multiple imagery sources and 2) automated reasoning at scale to detect, characterize, and monitor activities of interest. This paper provides details on the goals, dataset, metrics, and lessons learned of the IARPA SMART program. By releasing the annotated dataset, the program aims to foster additional research in this area by the community at large.

Hirsh R Goldberg↗
NASA NTRSDate not supplied

DELTA: An Open-Source Framework to Simplify Machine Learning with Satellite Imagery

DELTA (Deep Earth Learning, Tools, and Analysis) is an open-source framework developed at NASA to simplify running and training machine learning (ML) models on satellite imagery. Users new to machine learning can run existing ML models on satellite imagery with minimal setup and configuration. For experienced ML users, DELTA helps simplify data engineering, preprocessing steps, and reduces the need for boilerplate code that needs written to make satellite imagery datasets palatable for machine learning. This lets data scientists focus on model development while DELTA handles the imagery manipulation. This presentation will demonstrate DELTA’s functionality and share some examples from an active project using it for flood mapping using imagery from multiple satellite sources.

deep learning↗
DOE OSTI2025

Transfer learning-based soybean LAI estimations by integrating PROSAIL, UAV, and PlanetScope imagery

Accurate Leaf Area Index (LAI) estimations at the soybean plot scale is achievable using high-resolution Unmanned Aerial Vehicle (UAV) imagery and field measurement samples. However, the limited coverage of UAV flights restricts large-scale remote sensing monitoring in expansive soybean fields. This study leverages the broad coverage and 3-m resolution of PlanetScope satellite imagery to extend LAI prediction from UAV to satellite scales through transfer learning, using UAV-scale LAI estimates as a benchmark to validate cross-scale consistency. To address this challenge, this study proposed the LAI-TransNet, a two-stage transfer learning framework designed for precise and scalable soybean LAI prediction across large areas, demonstrating its effectiveness in cross-scale monitoring. In Stage 1, a UAV-scale benchmark is established using PROSAIL-simulated UAV reflectance data (UAV-Sim) and field-measured soybean LAI. Traditional machine learning, deep learning, and transfer learning models are trained on a hybrid UAV-Sim and field-measured dataset (UAV-Sim_Measured), with the transfer learning model CNN-TL, fine-tuned using pre-trained weights derived from UAV-Sim, achieving the highest accuracy (R 2 = 0.81, RMSE = 0.64 m 2 /m 2 , rRMSE = 11.5 %). In Stage 2, LAI-TransNet is developed by fine-tuning the CNN-TL model on PlanetScope simulated data (PS-Sim), preprocessed via cross-domain mapping to align UAV and satellite spectral features. Real PlanetScope imagery is corrected for reflectance consistency with reference to UAV imagery spectral profiles. LAI-TransNet outperforms other deep learning models trained directly on PS-Sim (R 2 = 0.69 vs. 0.60–0.63), ensuring robust cross-scale consistency. In conclusion, by bridging UAV and satellite scales, LAI-TransNet enables large-scale soybean LAI monitoring, enhancing precision agriculture management through improved monitoring with the PlanetScope imagery.

Leaf area index (LAI)↗
NASA NTRS1972

Proposal to evaluate the use of ERTS-A imagery in mapping and managing soil and range resources in the Sand Hills Region of Nebraska

The author has identified the following significant results. Visual examination of RB-57F color infrared imagery of range sites within Test Site 313 indicates that early season imagery will show significant differences in appearance of sub-irrigated sites as compared to dry valley sites. Differences appear to be significant also when comparing the previous two sites to sands sites. Comparison of existing soil map soils delineations with vegetative growth patterns shows reasonably good agreement between the two patterns over rather broad areas. Visual examination of ERTS-1 imagery has also shown that rangeland burned by prairie fire within the last six months can be distinguished. Three confirmed fire areas have been shown on the imagery. Since only broad estimates of burned acres are available, more accurate acreage measurements will be attempted. Known acreage of burned areas will be of value to those agencies responsible for deferred grazing payments to land owners. The relative speed with which this acreage information would become available to these agencies through ERTS-1 imagery would be of much benefit.

Drew, J. V.↗
NASA NTRS1973

Use of satellite imagery for wildland resource evaluation in the Great Basin

The author has identified the following significant results. Color composites and diazachrome transparencies of ERTS-1 imagery have greatly increased interpretation capabilities. Vegetation green-up and flooding due to late summer precipitation has been identified on such imagery. MSS imagery in all bands has not proven as valuable for similar determinations. Snow cover has been found to be valuable in the identification of fire scars, pinyon/juniper chainings, and subtle ecotones not previously identified with any other type of imagery. It is felt that a greater understanding of the effects of snow cover on vegetation remote sensing will enable investigators to extend capabilities relating to the mapping and identification of these resources. Highly reflective phreatophytic vegetation has been mapped and quantified using the MSS 5 and 7 bands and diazachrome color composites. Approximately 10 man-hours were required to complete the entire state. Native meadow and hay meadow vegetation has been mapped in Elko County, Nevada, using ERTS-1 imagery. Future plans include a statewide inventory of this resource.

Tueller, P. T.↗
NASA NTRS1973

Satellite geological and geophysical remote sensing of Iceland: Preliminary results from analysis of MSS imagery

A binational, multidisciplinary research effort in Iceland is directed at an analysis of MSS imagery from ERTS-1 to study a variety of geologic, hydrologic, oceanographic, and agricultural phenomena. A preliminary evaluation of available MSS imagery of Iceland has yielded several significant results - some of which may have direct importance to the Icelandic economy. Initial findings can be summarized as follows: (1) recent lava flows can be delineated from older flows at Askja and Hekla; (2) MSS imagery from ERTS-1 and VHRR visible and infrared imagery from NOAA-2 recorded the vocanic eruption on Heimaey, Vestmann Islands; (3) coastline changes, particularly changes in the position of bars and beaches along the south coast are mappable; and (4) areas covered with new and residual snow can be mapped, and the appearance of newly fallen snow on ERTS-1, MSS band 7 appears dark where it is melting. ERTS-1 imagery provides a means of updating various types of maps of Iceland and will permit the compilation of special maps specifically aimed at those dynamic environmental phenomena which impact on the Icelandic economy.

Williams, R. S., Jr.↗
NASA NTRS1973

Facilitating the exploitation of ERTS imagery using snow enhancement techniques

The author has identified the following significant results. Comparative analysis of snow-free and snow-covered imagery of the New England Test Area has resulted in a larger number of lineaments mapped from snow-covered imagery in three out of four sets of comparative imagery. Analysts unfamiliar with the New England Test Area were utilized; the quality of imagery was independently judged to be uniform. In all image sets, a greater total length of lineaments was mapped with the snow-covered imagery. The value of this technique for fracture mapping in areas with thick soil cover is suggested. A number of potentially useful environmental applications of snow enhancement related to such areas as mining, land use, and hydrology have been identified.

Wobber, F. J.↗
NASA NTRS1973

Satellite geological and geophysical remote sensing of Iceland: Preliminary results of geologic, hydrologic, oceanographic, and agricultural studies with ERTS-1 imagery

The author has identified the following significant results. The wide variety of geological and geophysical phenomena which can be observed in Iceland, and particularly their very direct relation to the management of the country's natural resources, has provided great impetus to the use of ERTS-1 imagery to measure and map the dynamic natural phenomena in Iceland. MSS imagery is being used to study a large variety of geological and geophysical eruptive products, geologic structure, volcanic geomorphology, hydrologic, oceanographic, and agricultural phenomena of Iceland. Some of the preliminary results from this research projects are: (1) a large number of geological and volcanic features can be studied from ERTS-1 imagery, particularly imagery acquired at low sun angle, which had not previously been recognized; (2) under optimum conditions the ERTS-1 satellite can discern geothermal areas by their snow melt pattern or warm spring discharge into frozen lakes; (3) various maps at scales of 1:1 million and 1:500,000 can be updated and made more accurate with ERTS-1 imagery; (4) the correlation of water reserves with snowcover can improve the basis for planning electrical production in the management of water resources; (5) false-color composites (MSS) permitted the mapping of four types of vegetation: forested; grasslands, reclaimed, and cultivated areas, and the seasonal change of the vegetation, all of high value to rangeland management.

Williams, R. S., Jr.↗
NASA NTRS1974

Evaluation of ERTS-1 imagery in mapping and managing soil and range resources in the Sand Hills region of Nebraska

The author has identified the following significant results. Interpretations of imagery from the Earth Resources Technology Satellite (ERTS-1) indicate that soil associations and attendant range sites can be identified on the basis of vegetation and topography using multitemporal imagery. Optical density measurements of imagery from the visible red band of the multispectral scanner(MSS band 5) obtained during the growing season were related to field measurements of vegetative biomass, a factor that closely parallels range condition class on specific range sites. ERTS-1 imagery also permitted inventory and assessment of center-pivot irrigation systems in the Sand Hills region in relation to soil and topographic conditions and energy requirements. Four resource maps of the Upper Loup Natural Resource District located entirely within the Sand Hills region were prepared from ERTS-1 imagery.

Drew, J. V.↗
NASA NTRS1974

Remote sensing in Iowa agriculture: Identification and classification of Iowa's crops, soils and forestry resources using ERTS-1 and complimentary underflight imagery

The author has identified the following significant results. Springtime ERTS-1 imagery covering pre-selected test sites in Iowa showed considerable detail with respect to broad soil and land use patterns. Additional imagery has been incorporated into a state mosaic. The mosaic was used as a base for soil association lines transferred from an existing map. The regions of greatest contrast are between the Clarion-Nicollet-Webster soil association area and adjacent areas. Landscape characteristics in this area result in land use patterns with a high percentage of pasture, hay, and timber. The soil association areas of the state that have patterns interpreted to be associated with intensive row crop production are: Moody, Galva-Primghar-Sac, Clarion-Nicollet-Webter, Tama-Muscatine, Dinsdale-Tama, Cresco-Lourdes, Clyde, Kenyon-Floyd-Clyde, and the Luton-Onawa-Salix area on the Missouri River floodplain. Forestland estimates have been attained for an area in central Iowa using wintertime ERTS-1 imagery. Visual analysis of multispectral, temporal imagery indicates that temporal analysis for cropland identification and acreage analyses procedures may be a very useful tool. Combinations of wintertime, springtime, and summertime ERTS-1 imagery separate most vegetation types. Timing can be critical depending upon crop development and harvesting times because of the dynamic nature of agricultural production.

Mahlstede, J. P.↗
NASA NTRS1974

The application of ERTS imagery to mapping snow cover in the western United States

The author has identified the following significant results. In much of the western United States a large part of the utilized water comes from accumulated mountain snowpacks; thus, accurate measurements of snow distributions are required for input to streamflow prediction models. The application of ERTS-1 imagery for mapping snow has been evaluated for two geographic areas, the Salt-Verde watershed in central Arizona and the southern Sierra Nevada in California. Techniques have been developed to identify snow and to differentiate between snow and cloud. The snow extent for these two drainage areas has been mapped from the MSS-5 (0.6 - 0.7 microns) imagery and compared with aerial survey snow charts, aircraft photography, and ground-based snow measurements. The results indicate that ERTS imagery has substantial practical applications for snow mapping. Snow extent can be mapped from ERTS-1 imagery in more detail than is depicted on aerial survey snow charts. Moreover, in Arizona and southern California cloud obscuration does not appear to be a serious deterrent to the use of satellite data for snow survey. The costs involved in deriving snow maps from ERTS-1 imagery appear to be very reasonable in comparison with existing data collection methods.

Barnes, J. C.↗
NASA NTRS1974

Vegetation mapping from ERTS imagery of the Okavango Delta

The Okavango is Botswana's major water resource. The present study has been specifically directed at mapping vegetation types within the delta and generally concerned with finding what information of value to plant and animal ecologists could be extracted from the imagery. To date it has been found that. (1) It is possible to map broad vegetation types from the imagery. (2) Imagery of the delta records the state of the system in a manner which will facilitate long-term studies of plant succession. (3) Phenological events can be detected. (4) The imagery can be used to detect and map wild fires. This will be useful in determining the role of fire in the ecology of the region. Using the imagery it is thus possible to map existing vegetation and monitor both short and long-term changes.

Willamson, D. T.↗
NASA NTRS1974

Geologic hypotheses of Lake Tanganyika region, Zaire, drawn from ERTS imagery

Based on initial work in the Lake Tanganyika area of eastern Zaire, it has been concluded that ERTS imagery is extremely useful for reconnaissance level geologic mapping and analysis in this region of the humid tropics. In particular, ERTS imagery has proven useful for recognizing and mapping regional structural units, for recognizing major structural features, and for arriving at some preliminary hypotheses about the mineral potential of the area. Results so far indicate that ERTS imagery can make a major contribution to the development of the mineral resources of the country. Research has concentrated on applications of ERTS imagery in the field of cartography, geology, forestry, hydrology and agriculture. For the work in geology, a test site was chosen in eastern Zaire on the shore of Lake Tanganyika in the vicinity of the Lukuga River. This area was selected because of its varied geology and the existence of two frames of cloud-free ERTS imagery.

Wolyce, U.↗
NASA NTRS1983

Hue-saturation-intensity split-spectrum processing of Seasat radar imagery

Characteristics of terrain features in orbital radar imagery are investigated on the basis of radar physics and the human visual system. Signal scattering, the dominant factor in radar imagery, is caused by look angle and relief displacement. Power spectra characteristics are explored, noting that surface scattering dominates the low frequencies and slope significantly affects the high frequency tone variations. The maximum human visual response has been identified for achromatic scenes as 7 cycles per degree, whereas in radar imagery the low frequency half-power point occurs near 2.5 cycles/deg. Image feature size at a scale of 1:500,000 has been calculated for the maximum human response at 1.25 km. A hue-saturation-intensity transformation is presented for displaying two or three data sets on the same image and is demonstrated for Seasat SAR imagery. The transformation is shown to restore low spatial frequencies to the SAR imagery in order to identify terrain features which would otherwise not be visually discernible.

Daily, M.↗
NASA NTRS1984

Evaluation of SIR-B imagery for geologic and geomorphic mapping, hydrology, and oceanography in Australia

The objectives of a study to evaluate the potential of Shuttle Imaging Radar-B (SIR-B) imagery for various applications are outlined. Specific goals include: the development of techniques for registration multiple acquisition, varied illumination, and incidence-angle SIR-B imagery, and a model for estimation of the relative contributions to the backscattered radiation of topography, surface roughness, and dielectric and conductivity components; (2) the evaluation of SIR-B imagery for delineation of agricultural lands affected by secondary salinity in the southwest and southeast agricultural regions of Australia; (3) the development of techniques for application of SIR-B imagery for geologic, geomorphologic and soils mapping and mineral exploration; and (4) the evaluation of the use of SIR-B imagery in determining ocean currents, current shear patterns, internal waves and bottom features for specific locations off the Australian coast.

Honey, F. R.↗
NASA NTRS1990

Normalization of satellite imagery

Sets of Thematic Mapper (TM) imagery taken over the Washington, DC metropolitan area during the months of November, March and May were converted into a form of ground reflectance imagery. This conversion was accomplished by adjusting the incident sunlight and view angles and by applying a pixel-by-pixel correction for atmospheric effects. Seasonal color changes of the area can be better observed when such normalization is applied to space imagery taken in time series. In normalized imagery, the grey scale depicts variations in surface reflectance and tonal signature of multi-band color imagery can be directly interpreted for quantitative information of the target.

Kim, Hongsuk H.↗
NASA NTRS1992

Sea surface velocities from visible and infrared multispectral atmospheric mapping sensor imagery

High resolution (100 m), sequential Multispectral Atmospheric Mapping Sensor (MAMS) images were used in a study to calculate advective surface velocities using the Maximum Cross Correlation (MCC) technique. Radiance and brightness temperature gradient magnitude images were formed from visible (0.48 microns) and infrared (11.12 microns) image pairs, respectively, of Chandeleur Sound, which is a shallow body of water northeast of the Mississippi delta, at 145546 GMT and 170701 GMT on 30 Mar. 1989. The gradient magnitude images enhanced the surface water feature boundaries, and a lower cutoff on the gradient magnitudes calculated allowed the undesirable sunglare and backscatter gradients in the visible images, and the water vapor absorption gradients in the infrared images, to be reduced in strength. Requiring high (greater than 0.4) maximum cross correlation coefficients and spatial coherence of the vector field aided in the selection of an optimal template size of 10 x 10 pixels (first image) and search limit of 20 pixels (second image) to use in the MCC technique. Use of these optimum input parameters to the MCC algorithm, and high correlation and spatial coherence filtering of the resulting velocity field from the MCC calculation yielded a clustered velocity distribution over the visible and infrared gradient images. The velocity field calculated from the visible gradient image pair agreed well with a subjective analysis of the motion, but the velocity field from the infrared gradient image pair did not. This was attributed to the changing shapes of the gradient features, their nonuniqueness, and large displacements relative to the mean distance between them. These problems implied a lower repeat time for the imagery was needed in order to improve the velocity field derived from gradient imagery. Suggestions are given for optimizing the repeat time of sequential imagery when using the MCC method for motion studies. Applying the MCC method to the infrared brightness temperature imagery yielded a velocity field which did agree with the subjective analysis of the motion and that derived from the visible gradient imagery. Differences between the visible and infrared derived velocities were 14.9 cm/s in speed and 56.7 degrees in direction. Both of these velocity fields also agreed well with the motion expected from considerations of the ocean bottom topography and wind and tidal forcing in the study area during the 2.175 hour time interval.

Pope, P. A.↗