This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Remote Sensing. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Remote Sensing. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

These datasets contain all input and output data used for the paper 'Satellite altimetry reveals intensifying global river water level variability'. Detailed descriptions of the datasets and their attributes can be found in the accompanying technical documentation. The code used to generate these datasets is available in our GitHub repository at https://github.com/Fangchq/Satellite-rivers/tree/master.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Environmental Remote Sensing. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Environmental Remote Sensing. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

BreizhSR, a super-resolution Sentinel-2 to SPOT-6/7 dataset

1. Dataset motivation

BreizhSR is a dataset targetting super-resolution of (RGB bands of) Sentinel-2 images by providing time series colocated in space and time with SPOT-6/7 acquisitions. This dataset is composed of cloud free Sentinel-2 time series (visible bands at 10m resolution) and SPOT-6/7 pansharpened color images resampled 2.5m resolution. The study area is the region of Brittany (Breizh in the local language), located on the northwestern coast of France with an oceanic climate. The dataset covers about 35 000 km² with mostly agricultural areas (about 80 %). All acquisitions are from 2018 in the Brittany region of France.

2. Dataset organization

The dataset folder follows the structure detailed below :

BreizhSR
├── dataset_test.pkl
├── dataset_train.pkl
├── README.md
├── x
├── x_test
├── y
└── y_test

The README.md file contains the same information as this description.

Actual image patches are stored in the x and x_test folders for Sentinel-2 patches, and in the y and y_test folders for ground truth SPOT patches. Subfolders are organized using a integer identifier (e.g. 8355) that denote the series identifier. Therefore, for the S2 series x/8355, the corresponding SPOT patch is in subfolder y/8355.

This organization and additional metadata are described in two Pandas Dataframes : dataset_train.pkl and dataset_test.pkl. These files are Dataframes serialized using the pickle Python serialization protocol. The columns available in these Dataframes are described in the table below.

3. Data collection and preprocessing

Sentinel-2

Sentinel-2 constellation has twin satellites launched by the European Space Agency (ESA) in 2015 and 2017 that cover all Earth’s surfaces every five days at the equator. Level-2A images of the BreizhSR dataset are gathered via the THEIA platform, which employs the MAJA pre-processing algorithm to obtain atmospherically corrected ground reflectance. To match the SPOT-6 spectral characteristics, only RGB bands at a 10-meter spatial resolution (B4, B3,and B2) are used in the analysis. The images were collected for the nine tiles covering the Brittany region from the 1st of April 2018 to the 31st of August 2018, filtering images with a cloud cover under 5 %. Since the SPOT-6 data was acquired in the summer of 2018, the Sentinel-2 time period was chosen to include images from before and after the SPOT-6 acquisitions while staying in a range of similar seasonal and climate conditions.

Sentinel-2 tiles are cropped into 3x74x74 patches. The dataset is preprocessed with a min-max normalization, using the 2% and 98% percentile as an estimation of minimum and maximum values of Sentinel-2 data to take into account the presence of outliers due to artifacts such as clouds and their shadows.

SPOT-6/7

Orthorectified SPOT data under the Licence Ouverte is collected from the DINAMIS platform. Multispectral images at 6m resolution are pansharpened using the panchromatic 1.5m reference using the RCS algorithm Orfeo ToolBox, similar to the Brovey pansharpening algorithm. The pansharpened tiles are preprocessed with a min-max normalization, downsampled at 2.5m resolution and patches are finally cropped with dimensions 3x296x296.

4. License

SPOT images and the Sentinel-2 Theia L2A products are released under the Licence Ouverte 2.0 from the French government. This dataset contains modified Coprnicus Sentinel data from 2018, made available under free access by EU law. Other files in the dataset are licensed under Creative Commons Attribution 4.0 (CC BY 4.0).

Acknowledgements

We thank the support of GDR IASIS for funding this work under the SESURE project, the DINAMIS consortium, CNES/Airbus and IGN for access to the SPOT-6 data, and ESA for access to Sentinel-2 data. During the conduct of this research, Simon Donike received a European scholarship to engage in Master Copernicus in Digital Earth, Erasmus Mundus Joint Master Degree (EMJMD). We thank Dirk Tiede (Uni. Salzburg) for his help and feedback on BreizhSR. This work was performed using HPC resources from GENCI–IDRIS (grant 2022-AD011013003).

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.

The shared archived combined in Supplementary Datasets represent the actual databases used in the investigation considered in two papers:

Meteorological conditions affecting black vulture (Coragyps atratus) soaring behavior in the southeast of Brazil: Implications for bird strike abatement (in submission)

Remote sensing applications for abating the aircraft-bird strike risks in the southeast of Brazil (Human-Wildlife Interactions Journal, in print)

The papers were based on my Master’s thesis defended in 2016 in the Institute of Biology of the University of Campinas (UNICAMP) in partial fulfilment of the requirements for the degree of Master in Ecology. Our investigation was devoted to reducing the risk of aircraft collision with Black vultures. It had two parts considered in these two papers. In the first one we studied the relationship between soaring activity of Black vultures and meteorological characteristics. In the second one we explored the dependence of soaring activity of vultures on superficial and anthropogenic characteristics. The study was implemented within surroundings of two airports in the southeast of Brazil taken as case studies. We developed the methodological approaches combining application of GIS and remote sensing technologies for data processing, which were used as the main research instrument. By dint of them we joined in the georeferenced databases (shapefiles) the data of bird's observation and three types of environmental factors: (i) meteorological characteristics collected together with the bird’s observation, (ii) superficial parameters (relief and surface temperature) obtained from the products of ASTER imagery; (iii) parameters of surface covering and anthropogenic pressure obtained from the satellite images of high resolution. Based on the analyses of the georeferenced databases, the relationship between soaring activity of vultures and environmental factors was studied; the behavioral patterns of vultures in soaring flight were revealed; the landscape types highly attractive for this species and forming the increased concentration of birds over them were detected; the maps giving a numerical estimation of hazard of bird strike events over the airport vicinities were constructed; the practical recommendations devoted to decrease the risk of collisions with vultures and other bird species were formulated.

This archive contains all materials elaborated and used for the study, including the GIS database for two papers, remote sensing data, and Microsoft Excel datasets. You can find the description of supplementary files in the Description of Supplementary Dataset.docx. The links on supplementary files and their attribution to the text of papers are considered in the Attribution to the text of papers.docx. The supplementary files are in the folders Datasets, GIS_others, GIS_Raster, GIS_Shape.

For any question please write me on this email: natalieenov@gmail.com

Natalia Novoselova

This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 18 flights aboard a DOE B-200 aircraft over California, Nevada, Arizona, New Mexico, Washington, Colorado, and Texas, U.S., on 1999-09-13 to 1999-10-06. 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.

OISST-V2-AVHRR Daily 1/4 degree Dimensioned By time, depth, latitude, longitude. _CoordSysBuilder=ucar.nc2.dataset.conv.CF1Convention cdm_data_type=Grid comment=Data was converted from NetCDF-3 to NetCDF-4 format with metadata updates in November 2017. Conventions=CF-1.4, ACDD-1.3 Easternmost_Easting=359.875 geospatial_lat_max=89.875 geospatial_lat_min=-89.875 geospatial_lat_resolution=0.25 geospatial_lat_units=degrees_north geospatial_lon_max=359.875 geospatial_lon_min=0.125 geospatial_lon_resolution=0.25 geospatial_lon_units=degrees_east history=Final file created using preliminary as first guess, and 3 days of AVHRR data. Preliminary uses only 1 day of AVHRR data. ; FMRC Best Dataset id=oisst-avhrr-v02r01.20250610.nc infoUrl=https://www.ncei.noaa.gov/thredds/catalog/ncFC/fc-oisst-daily-avhrr-only-dly/catalog.html?dataset=ncFC/fc-oisst-daily-avhrr-only-dly/OISST_Daily_AVHRR-only_Feature_Collection_best.ncd institution=NOAA/NCEI instrument=Earth Remote Sensing Instruments > Passive Remote Sensing > Spectrometers/Radiometers > Imaging Spectrometers/Radiometers > AVHRR > Advanced Very High Resolution Radiometer instrument_vocabulary=Global Change Master Directory (GCMD) Instrument Keywords keywords_vocabulary=Global Change Master Directory (GCMD) Earth Science Keywords location=Proto fmrc:OISST_Daily_AVHRR-only_Feature_Collection metadata_link=https://doi.org/10.25921/RE9P-PT57 naming_authority=gov.noaa.ncei ncei_template_version=NCEI_NetCDF_Grid_Template_v2.0 Northernmost_Northing=89.875 platform=Ships, buoys, Argo floats, MetOp-A, MetOp-B platform_vocabulary=Global Change Master Directory (GCMD) Platform Keywords processing_level=NOAA Level 4 references=Reynolds, et al.(2007) Daily High-Resolution-Blended Analyses for Sea Surface Temperature (available at https://doi.org/10.1175/2007JCLI1824.1). Banzon, et al.(2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies (available at https://doi.org/10.5194/essd-8-165-2016). Huang et al. (2020) Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version v02r01, submitted.Climatology is based on 1971-2000 OI.v2 SST. Satellite data: Pathfinder AVHRR SST, Navy AVHRR SST, and NOAA ACSPO SST. Ice data: NCEP Ice and GSFC Ice. sensor=Thermometer, AVHRR source=ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfinder_AVHRR, Navy_AVHRR, NOAA_ACSP sourceUrl=https://www.ncei.noaa.gov/thredds/dodsC/ncFC/fc-oisst-daily-avhrr-only-dly/OISST_Daily_AVHRR-only_Feature_Collection_best.ncd Southernmost_Northing=-89.875 standard_name_vocabulary=CF Standard Name Table (v40, 25 January 2017) time_coverage_end=2025-06-11T12:00:00Z time_coverage_start=2020-02-28T12:00:00Z Westernmost_Easting=0.125

This dataset summarizes work done to predict the presence of perennial firn aquifers (PFA) and buried lakes in and on the Greenland ice sheet using satellite-based remote sensing observations (specifically, differences in two wavelengths of passive microwave data observed by Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E)). This work was done by Ms. Margeaux Carter in partial fulfillment of a Master’s degree in Hydrology (2017) at the New Mexico Institute of Mining and Technology, under the research supervision of Dr. David B. Reusch. A copy of this thesis has been archived for personal or classroom use only. While every reasonable effort has been made to provide “production” quality files, certain aspects of files in this project tend to be more “research” (or “development”) grade. For example, while metadata on Network Common Data Form (netCDF) variables will usually be sufficient, there tends to be a lack of file history metadata. The main components of this project are (1) modeling microwave brightness temperature (Tb) of ice and snow using Microwave Emission Model of Layered Snowpacks (MEMLS) and (2) predicting occurrence of subsurface liquid melt using an algorithm known as “Polarization Difference” and AMSR-E passive microwave observations. 1. Modeling microwave emissions of ice and snow using MEMLS MEMLS (Weismann and Matzler, 1999) was used to test the feasibility of using passive low frequency microwave (LFM) satellite observations to identify subsurface water (surface lakes, aquifers) in the Greenland Ice Sheet (GIS). A model snowpack was used to test whether the characteristics (frequency, polarization) of the available LFM data could be used to identify subsurface water layers. The representative snowpack is 25 m deep and has twelve layers with thickness, temperature, and density chosen to reflect density profiles of ice cores obtained from snowpacks with perennial firn aquifers (Koenig et. al., 2014), and average winter snow temperature profiles from the Greenland Climate Network observation station at Swiss Camp (Steffen et. al., 1996). The tested range of liquid water content in the snowpack was chosen to reflect those observed by Koenig et. al. (2014). The official archive for MEMLS is at github.com/akasurak/memls_TVC. Custom files (primarily MATLAB scripts) are archived in the file memls_TVC-custom.tgz. 2. AMSR-E-based prediction Prediction of subsurface liquid water was done using AMSR-E passive microwave data at two frequencies, 6.9 and 10.7 Gigahertz (GHz), and vertical (V) and horizontal (H) polarizations. A netCDF version of the original binary files has been archived. These files were used with an algorithm called “Polarization Difference” (PD) that looks at differences between V and H polarizations at each wavelength to predict the presence of subsurface liquid water. A 10-year (2002-2011) daily resolution dataset from the PD algorithm has been archived. These data were further analyzed to classify the ice sheet into four categories: probable firn aquifer, probable buried lake, “overlap” where subsurface liquid water is likely present but type cannot be classified, and “not in range”. Details on development of the prediction algorithm may be found in the master of science (MSc) thesis. PD-based predictions were tested against a number of independent datasets. Direct verification included observations of PFAs (Forster et al., 2014) and buried surface lakes (Koenig et al., 2015). These files are not archived here, please contact those authors. Additional testing against aspects of modeled monthly meteorology was done with a subset of the Arctic System Reanalysis (ASR; Bromwich et. al., 2012). These data have been archived here after customizations for our analysis. Observations of surface melt occurrence from the “MEaSUREs Greenland Surface Melt Daily 25 kilometers (km) Equal Area Scalable Earth (EASE)-Grids 2.0, Version 1” (Mote 2014; https://nsidc.org/data/NSIDC-0533/versions/1), also a part of testing the PD algorithm, were also archived in modified format (spatially subset and spatially summed). Other In addition to observational and model datasets, we have also archived a set of NCL (National Center for Atmospheric Research (NCAR) Command Language) scripts related to file processing and model development and verification. References Bromwich, D., L. Bai, K. Hines, S. Wang, Z. Liu, H. Lin, Y. Kuo, and M. Barlage (2012), Arctic System Reanalysis (ASR) Project. https://doi.org/10.5065/D6K072B5, Nat. Cent. Atmos. Res., Comp. Inf. Sys. Lab. Boulder, Colo., Accessed 01 Mar 2016. Forster, R., J. Box, M. van den Broeke, C. Miège, E. Burgess, J. van Angelen, J. Lenaerts, L. Koenig, J. Paden, C. Lewis, S. Gogineni, C. Leuschen, and J. McConnel (2014), Extensive liquid meltwater storage in firn within the Greenland ice sheet, Nat. Geo... Visit https://dataone.org/datasets/doi%3A10.18739%2FA2VM42Z3G for complete metadata about this dataset.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Hydrogeology And Remote Sensing. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Hydrogeology And Remote Sensing. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Doctor Of Philosophy - Application Of Remote Sensing & Gis In Chennai Harbor Area Management. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Doctor Of Philosophy - Application Of Remote Sensing & Gis In Chennai Harbor Area Management. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Physical Oceanography And Remote Sensing. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Physical Oceanography And Remote Sensing. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Ecology, Wresearch Applying Advanced Remote Sensing To Model Landscape Change. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Ecology, Wresearch Applying Advanced Remote Sensing To Model Landscape Change. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during one flight aboard a DOE B-200 aircraft over California and Nevada, U.S., on 2004-08-18 to 2004-08-29. Objectives of this deployment included mapping geological faults in southern California. 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.