This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 11 flights aboard a DOE B-200 aircraft over California, Nevada, Oregon, and Washington, U.S., on 2001-08-22 to 2001-08-31. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.

This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during four flights aboard a DOE B-200 aircraft over Nevada, Arizona, and New Mexico, U.S., from 2007-10-01 to 2007-10-04. A focus of this data collection was the USDA Jornada Experimental Range (Jornada) in southern New Mexico. To complement the programs of ground measurements, JORNEX (JORNada EXperiment) began in 1995 to collect remotely sensed data from aircraft and satellite platforms to provide spatial and temporal data on physical and biological states of the Jornada rangeland. JORNEX uses remote sensing techniques to study arid rangeland and the responses of vegetation to changing hydrologic fluxes and atmospheric driving forces. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.

This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during five flights aboard a DOE B-200 aircraft over Nevada, U.S., from 2006-05-26 to 2006-06-01. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.

ArkansasView is a member of the AmericaView consortium, a national network focused on Earth observation education and research capacity. Established in 2002 by University of Arkansas’ Center for Advanced Spatial Technologies (CAST), ArkansasView has been a strong supporter of remote sensing within CAST, the campus community, Arkansas, and the United States. Recent efforts have focused on a) the development of new degree and certificate programs including PhD Geosciences, MS Geography, and online certificates in geospatial technologies aligned with remote sensing, b) collaboration with faculty and graduate students in Arkansas seeking to apply remote sensing in their research, c) advances in geospatial provenance (to support education, transparency, and reproducibility and replicability or R&R in remote sensing workflows) including a section in Remote Sensing Handbook (CRC Press), and d) related advances in geospatial unmanned aircraft systems (UAS). Through a 2014-2016 partnership with Communities Unlimited, a nonprofit organization serving communities in Arkansas and six neighboring states, ArkansasView sponsored a geospatial internship for developing remote sensing-assisted workflows that address persistently poor rural communities’ access to basic water infrastructure. In 2016-2017 ArkansasView played a key role in the creation of the first two UAS courses at University of Arkansas. These courses support new agricultural, environmental, and other UAS applications in Arkansas. In 2018-2020 ArkansasView sponsored two graduate student interns, created a geoprocessing and workflows (GW or “Gigawatt”) tool, and organized a national AmericaView GitLab group with an introductory primer for new users.

This is the dataset presented in the paper The Mountain Habitats Segmentation and Change Detection Dataset accepted for publication in the IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Beach, HI, USA, January 6-9, 2015. The full-sized images and masks along with the accompanying files and results can be downloaded here. The size of the dataset is about 2.1 GB.

The dataset is released under the Creative Commons Attribution-Non Commercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/legalcode).

The dataset documentation is hosted on GitHub at the following address: http://github.com/fjean/mhscd-dataset-doc. Direct download links to the latest revision of the documentation are provided below:

• PDF format: http://github.com/fjean/mhscd-dataset-doc/raw/master/mhscd-dataset-doc.pdf
• Text format: http://github.com/fjean/mhscd-dataset-doc/raw/master/mhscd-dataset-doc.rst

This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a DOE B-200 aircraft over Colorado and Utah, U.S., on 2004-07-01. Objectives of this deployment included mapping geological substrates and their mineral content. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.

This dataset is about: Master tracks in different resolutions from POLAR 6 flight P6_246_HAMAG_2024_2402190601. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.967666 for more information.

This dataset is about: Master track from POLAR 6 flight P6_246_HAMAG_2024_2402130301 in 1 sec resolution (zipped, 362 KB).

The data content of remote sensing (RS) images of sea surface temperature (SST) and normalized water-leaving radiance (nLw), for the year 1998, with respect to the River Rhine plume, is investigated. Questions that this study tries to answer are: is it possible to identify the plume from the available RS images, and under which conditions is this possible? How much information on the plumes behaviour can be derived from these images? Does or can this information contribute to our general knowledge of the plume? The images provide a spatial resolution of I km2 and a temporal resolution of I or 2 images per day per sensor for nLw and SST, respectively (in the case of a cloudless atmosphere). In the presence of clouds, no signal is detected for the area of surface water underneath the clouds. Two hypotheses are set up to explain how the RS images can be used to trace the plume. In the hypotheses links are established between salinity gradients that delimit the plume and SST and nLw respectively. The results are based on these hypotheses. From the available images, 9 SST images in spring provide detailed information on the stratified plume and allow for derivation of indirect information on sub-surface processes. In winter the temperature gradients as visible on SST imagery seem to indicate the broad plume patterns. From the nLw images it was not possible to identify the boundaries of the plume. However it is expected that the nLw images are an excellent source for monitoring suspended particulate matter (SPM) in the North Sea. The general conclusion of this study is that the RS data used in this project provide a valuable source of information, with respect to the Dutch coastal zone, in addition to the currently available measurement techniques and computer models. The SST imagery turns out to be particularly useful for tracing stratification, whereas nLw imagery seems to be an excellent source for monitoring SPM in the North Sea. For detailed monitoring of the DCZ and the plume, increased spatial and temporal resolutions are required.

A data collection campaign using the MODIS/ASTER airborne simulator (MASTER) was conducted in the greater Phoenix metropolitan area in July 2011 to collect visible through mid-infrared multispectral imagery. High resolution (7 m/pixel) land surface temperature products for day and night periods were calculated using the mid-infrared bands of data; surface reflectance, albedo, and Normalized Difference Vegetation Index (NDVI) products were calculated using the visible through shortwave infrared band data for 41 select neighborhoods. While the full MASTER dataset has been processed to at-sensor radiance, it did not include native geolocation data. As georeferencing the entire dataset was not possible with funds available, the processed data described above were extracted for the 41 spatially discrete Phoenix Area Social Survey neighborhoods within the MASTER flight boundary.

This data set contains a collection of attributes associated with CloudSat identified echo objects (or contiguous regions of radar/dBZ echo) from15 June 2006 till 17 January 2013. CloudSat is a NASA satellite that carries a 94 GHz (3 mm) nadir pointing cloud profiling radar (CPR). CloudSat makes approximately 14 orbits per day with an equator passing time of 0130 and 1330 local time. Echo objects were identified using CloudSat's 2B-GEOPROF product that includes 2D arrays (alongtrack x vertical) of the radar reflectivity factor and gaseous attenuation correction. Also included in the product is a "cloud mask" with values ranging between 0 and 40 with higher values indicating a greater likelihood of cloud detection. An EO was defined as a contiguous region of cloud mask greater than or eaqual to 20, consisting of at least three pixels with their edges and not merely their corners touching. Each echo object (EO) is assigned multiple attributes. The geographic attributes include minimum, mean, and maximum latitude and longitude, minimum and maximium location along the CloudSat orbit track, and the underlying surface altitude and land mask data, which allows the EOs to be catagorized as occuring over land, sea, or the coast. The geometric attributes include top, mean, and bottom height, width, and the total number of pixels within the EO. Attributes describing the internal structure of the EO are also available including the number of pixels and cells (i.e., group of pixels) greater than 0 dBZ and -17 dBZ. Finally, the time of day of occurance was also recorded to compare the statistics of EOs ocurring during the daytime versus nighttime. In total, we identified 15,181,193 EOs from 15 June 2006 to 17 January 2013. After 17 April 2011, data were only collected during the day due to a battery failure onboard CloudSat. Each attribute is organized as a 1D array where the size of the array corresponds to the number of EOs. This organization allows subsets of EOs to be easily identified using simple "where" statements when writing code. The attributes were used to identify cloud types and analyze global cloud climatology according to season, surface type, and region (i.e., Riley 2009; Riley and Mapes 2009). The varability of EOs across the MJO was also analyzed (Riley et al. 2011). Methods Data:

Raw files were downloaded from ftp1.cloudsat.cira.colostate.edu in directory 2B-GEOPROF.R04 Processed files are in netcdf format

Processing:

Data were processed and analyzed using IDL. See CloudSat_code_README.txt for details The initial processing was done while I was a graduate student at the Univerisity of Miami working on my masters from 2006-2009 Code is available at https://github.com/erileydellaripa/CYGNSS_code

Data file description:

Once the tar.gz file is unpacked, the EO attributes are provided in the EO_masterlistYYYY.nc files, where YYYY corresponds to the different years. I transferred the EO attributes from IDL .save files to netcdf files for sharing. A description of each EO attribute is provide in the README.md and if you do an ncdump -h in a terminal window.

The attributes are organized in 1D arrays, where the element of each array corresponds to a unique EO and the total size of the array corresponds to the total number of EOs identified.

Data are processed from the start of CloudSat 15 June 2006 till 17 January 2013 for the EO attributes.

In total, there are 15,181,193 EOs.

There was a battery failure 17 April 2011. CloudSat resumed collecting data 27 October 2011, but only during the day.

References:

Riley, E. M., B. E. Mapes, and S. N. Tulich, 2011: Clouds Associated with the Madden-Julian Oscillation: A New Perspective from CloudSat. J. Atmos. Sci., 68, 3032-3051, https://doi.org/10.1175/JAS-D-11-030.1.

Riley, E. M., and B. E. Mapes, 2009: Unexpected peak near -15°C in CloudSat echo top climatology. Geophys. Res. Lett., 36, L09819, https://doi.org/10.1029/2009GL037558.

Riley, E. M., 2009: A global survey of clouds by CloudSat. M.S. thesis, Division of Meteorology and Physical Oceanography, University of Miami, 134 pp, https://scholarship.miami.edu/esploro/outputs/991031447848002976.

This dataset is about: Master tracks in different resolutions from POLAR 5 flight P5-256_COMPEX-EC_2025_2504060301. Please consult parent dataset @ https://doi.pangaea.de/10.1594/PANGAEA.982817 for more information.

This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during four flights aboard a DOE B-200 and a NASA ER-2 aircraft over California and New Mexico, U.S., 2008-10-20 to 2008-10-29. A focus of this data collection was the USDA Jornada Experimental Range (Jornada) in southern New Mexico. To complement the programs of ground measurements, JORNEX (JORNada EXperiment) began in 1995 to collect remotely sensed data from aircraft and satellite platforms to provide spatial and temporal data on physical and biological states of the Jornada rangeland. JORNEX uses remote sensing techniques to study arid rangeland and the responses of vegetation to changing hydrologic fluxes and atmospheric driving forces. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California, and the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 30-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.

This dataset is about: Master track from POLAR 5 flight P5-256_COMPEX-EC_2025_2504080501 in 1 sec resolution (zipped, 776 KB).

This data set was used in Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling the regional climate MAR model with the ice sheet model PISM mitigates the melt-elevation positive feedback, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2023-15, in review, 2023 to allow the spinup for the ice shee model PISM. 'temp_time_seris,oisotopestimes ,sealeveltimes,sealevel_time_series' were used. Originally this data set was provided by the University of Montana ("Jesse Johnson, Brian Hand, Tim Bocek - University of Montana" )

but was offline during publication. We therefore upload it here, so it can be assessd. Original descripion of the sea rise master data set was:

There are currently three different versions of the Greenland Present Day Data Set available: * Greenland Obsolescent Data Set (Greenland_5km_dev1.0.nc) * Greenland Standard Data Set ((Greenland_5km_v1.1.nc) * Greenland Developmental Data Set (Greenland_5km_dev1.2.nc) Each of these data sets contains the fields listed below. Data Fields: * Longitude * Latitude * Bed Topography (Bamber 2001) and bathymetry (Jakobsson et al. 2008). Data courtesy of Ed Bueler. See note 1 below. * Ice Thickness (Bamber 2001). See note 1 below. * Surface Elevaton (Bamber 2001). See note 1 below. * Mean Annual Near-surface (2 meter) Air Temperature. See note 2 below. * Mean Annual Precipitation. See note 2 below. * Basal Heat Flux (Shapiro and Ritzwoller 2004). Data courtesy of Ed Bueler. * Interferometrically Measured Surface Velocity (Joughin, Smith, Howat, and Scambos, in preparation). * Surface Balance Velocity - Created at the University of Montana by Jesse Johnson in July 2009. * Time Rate of Change of Ice Sheet Surface Height (Bea Csathol, Toni. Schenk, C.J. van der Veen, William B Krabill, Presented at the AGU 2009 Fall Meeting). * Land Cover (Bea Csathol, Toni. Schenk, C.J. van der Veen, William B Krabill, Presented at the AGU 2009 Fall Meeting). * Oxygen Isotopes Record and associated Temperature Time Series from the Greenland Ice Core Project (GRIP). Notes: 1. The bed topography in each of the three available data sets have been modified from that given by Bamber to incorporate the Center for Remote Sensing of Ice Sheets (CReSIS) data in the Jakobshavn region. In the "Obsolescent" and "Standard" Data Sets local spatial averages of the CReSIS data were calculated for each 5km grid point. Values on the border of the region for which there is CReSIS data were assigned an average of the new values and the original values to decrease artificial gradients outside. The "Developmental" Data Set uses an algorithm developed by Ute Herzfeld which preserves the continuity and depth of the trough below the glacier. The changes to the bed topography in the Jakobshavn region also affect the ice thickness and upper surface fields. (The upper surface is only affected at a few grid points where the Cresis data places the topography above the original upper ice surface.) 2. The climate data differs between the "Obsolescent" Data Set and the later "Standard" and "Developmental" Data Sets. The "Obsolescent" Data Set uses the temperature parameterization of Fausto et al (2009) and a juxtaposition of precipitation data provided by Evan Burgess (Burgess et al 2009) for regions where there is permanent ice with data provided by Bea Csatho (van der Veen, Bromwich, Csatho, and Kim 2001) for regions where there is not permanent ice. The later ("Standard" and "Developmental") Data Sets use climate data provided by Janneke Ettema (Ettema et al 2009). This data includes Runoff, Surface Mass Balance, and Surface Temperature fields in addition to the Two-meter Temperature and Precipitaion fields. Janneke Ettema (personal correspondence) provides the following comment: "I would recommend to use the surface temperature as boundary condition for ice dynamic model instead of the 2 meter temperature. They might differ significantly, especially for Greenland where Ts is limited to 0C and T2m could rise over the melting point. Furthermore, T2m is a result of interpolating the temperature at the lowest atmospheric model layer and the surface temperature using a certain lapse rate. The surface temperature is a direct result from the energy balance computed at the ice sheet surface."

Arctic Vegetation Archive - Alaska: ATLAS-2 The ATLAS-2 dataset is part of larger NSF-funded Arctic Transition in Land-Atmosphere System (ATLAS) project. ATLAS-2 contains the Seward Peninsula portion of the project with 52 plots at Council and Quartz Creek. The full ATLAS Transect also includes 15 releves at locations on the North Slope at Barrow, Atkasuk, Oumalik, and Ivotuk which are in the ATLAS-1 dataset (Edwards et al. 2000). The focus of the ATLAS project was to improve understanding of controls over spatial and temporal variability of terrestrial processes in the Arctic that have potential consequences for the climate system, i.e., processes that affect the exchange of water and energy with atmosphere, the exchange of radiatively active gases with the atmosphere, and delivery of freshwater to the Arctic Ocean. The purpose of the ATLAS vegetation studies were: 1) to characterize the major zonal vegetation types found along the North Slope climate gradient, 2) to quantify differences between acidic and non-acidic tundra along the same gradient, and 3) to investigate relationships between plant biomass, Leaf Area Index (LAI), and Normalized Difference Vegetation Index (NDVI). The data reported here are from a National Science Foundation funded, ATLAS study by D. A. Walker and colleagues titled ‘Arctic Climate Change, Substrate and Vegetation’ (OPP-9908829). The data from fieldwork completed in 2000 are compiled in a data report by Raynolds et al. (2002). Reconnaissance fieldwork was conducted in 1998, while plot data (species and environmental) were collected in 2000. Similar to ATLAS-1 100 x 100 m grids were established an the plots were located within the grid. Although the plots were not permanently marked, there are latitude and longitude coordinates for all but one site. In the report there are 53 plots, however one plot (CC-C) had environmental data, but no species data and it was dropped from the Alaska-AVA dataset. The source species and environmental data for the plots were obtained from homogenous areas of dominant vegetation within the grids. In some cases where the vegetation was more heterogeneous, as for example in patterned ground, areas with frost boils, stone stripes, or closely spaced water tracks, samples were divided into representative microhabitats with each releve representing a microsite within the grid. These microsites were labeled with the letters A, B, C as needed. Forty-five plots were assigned to 5 different arctic community types: a) tall forb and shrub vegetation on mesic-moist soil (13 plots); b) dwarf-shrub heath and low shrub vegetation on acidic poor substrate (13 plots); c) bog vegetation, acidic mires, including tussock tundra (8) plots); d) dry and mesic dwarf-shrub heath and graminoid vegetation on non-acidic tundra (9 plots); and e) lichen communities on silicate rocks (2 plots). Of the remaining 7 plots, 6 are undefined forest types (C1, C4-A, C-9, C-A, C-C, C-E) and plot C-F is not included due to insufficient community data. The report (Raynolds et al. 2002) includes select soil descriptions, soil physical and chemical data, select LAI data, subjective site assessments, and active layer depths. In addition the report includes a preliminary Landsat MSS-derived map of the Seward Peninsula, and a report on the comparison of forest composition and structure of old and new growth Picea glauca forests. Additional information on the ATLAS vegetation studies may be found at: http://www.geobotany.uaf.edu/atlas/atlas_sites.html These data were subsequently used in several reports and publications listed below. References Ahn, J. Y. 2014. Monitoring Regional Vegetation Changes in Seward Peninsula, Alaska, using Remote Sensing Technique. Masters Thesis. Department of Geomatics Engineering, University of Calgary, Calgary, Alberta, Canada. Raynolds, M. K., Martin, C. R., Walker, D. A., Moody, A., Wirth, D., and C. Thayer-Snyder. 2002. Atlas Vegetation Studies: Seward Peninsula, Alaska, 2000, vegetation, soil, and site information, with Seward vegetation map. Alaska Geobotany Center, Institute of Arctic Biology, University of Alaska, Fairbanks. 125 pp. Thayer-Snyder, C. R., H. A. Maier, and D. A. Walker. 2003. A preliminary Landsat-derived land-cover map of the Seward Peninsula, Alaska: classification methods and comparison with existing data sets. ATLAS The Arctic Transitions in the Land-Atmosphere System (ATLAS) project: Seward Peninsula Sites. http://www.eol.ucar.edu/projects/atlas/. Accessed 12 May 2015. Walker, D. A., G. J. Jia, H. E. Epstein, M. K. Raynolds, F. S. Chapin III, C. Copass, L. D. Hinzman, J. A. Knudson, H. A. Maier, G. J. Michaelson, F. Nelson, C. L. Ping, V. E. Romanovsky and N. Shiklomanov. 2003. Vegetation-soil-thaw-depth relationships along a low-arctic bioclimate gradient, Alaska: synthesis of information from the ATLAS studies. Permafrost and Periglacial Processes. 14:103-123.