The application of Very-High-Resolution satellite imagery for the purpose of studying wildlife, particularly in remote regions, has gained significant traction in recent years. With this there has been an exponential increase in the volume of data, which has fostered a shift towards the use of automated systems to increase processing efficiency. However, these systems require manually annotated data on which to be trained, which is lacking. This dataset describes a total of 819 annotated and classified whale Features of Interest (FOIs) from a multi-season survey of Wilhelmina Bay on the Western Antarctic Peninsula (WAP). These data are comprised of FOIs that have been annotated and classified based on existing protocols by seven individual observers who scanned ~1,900 km2 of WorldView-03 imagery acquired between 2018/2019 and 2021/2022. This work was supported by an Innovation Voucher from the British Antarctic Survey and grants from the World Wildlife Fund (GB107301) and NC-International NERC (NE/T012439/1).

This dataset provides daily, 8-day, and monthly Arctic melt pond fractions and binary classification, from 2021-05-01 to 2022-08-31. Level-2 MODerate resolution Imaging Spectroradiometer (MODIS) top-of-the-atmosphere (TOA) reflectances for bands 1-4 were obtained, to which two machine learning algorithms such as multi-layer neural networks and logistic regression were applied to map melt pond fraction and binary melt pond/ice classification. This work was funded by NERC standard grant NE/R017123/1.

This dataset contains model input and output data on emperor penguin population dynamics for a Bayesian analysis carried out on multivariate classification results. Model input data comprises multivariate classification analysis results derived from very-high resolution (VHR) satellite imagery pertaining to 16 emperor penguin colonies, spanning the Bellingshausen Sea to the Weddell Sea between 2009 to 2023. Model output data comprises population estimates for each year for each colony, global trends per year, global change for the dataset overall, global abundance pertaining to individual colonies, as well as statistical parameter estimates provided by the model. Data collection was carried out by personnel at BAS. Funding from WWF UK (GB095701), project NE/Y00115X/1 "Understanding emperor penguin populations in the Weddell Sea and Antarctic Peninsula" and previous WWF funding over the 15 year period.

This dataset provides the data produced as part of the work published in: Leeson, A. A., Foster, E., Rice, A., Gourmelen, N. and van Wessem, J. M.. 2019. ''Evolution of supraglacial lakes on the Larsen B ice shelf in the decades before it collapsed'' Geophysical Research Letters. It includes 1) shapefiles of supraglacial lakes mapped in both optical (Landsat) and SAR (ERS) satellite imagery, 2) rasters of lake depth, derived from Landsat TM and ETM+ images acquired in 1988 and 2000 and 3) shapefiles of the study area considered in the paper. Funding was provided by ERPSRC grant EP/R01860X/1.

This dataset provides daily, 8-day, and monthly Arctic melt pond fractions and binary classification, from 2000-06-01 to 2020-08-31. Level-2 MODerate resolution Imaging Spectroradiometer (MODIS) top-of-the-atmosphere (TOA) reflectances for bands 1-4 were obtained, to which two machine learning algorithms such as multi-layer neural networks and logistic regression were applied to map melt pond fraction and binary melt pond/ice classification. This work was funded by NERC standard grant NE/R017123/1.

This dataset provides supraglacial lake extents and depths as included in the paper by Arthur et al. (in review, Nature Comms.) entitled " Large interannual variability in supraglacial lakes around East Antarctica". Please cite this paper if using this data. This dataset consists of (1) shapefiles of supraglacial lake extents around the East Antarctic Ice Sheet derived from Landsat-8 imagery acquired between January 2014 and 2020 and (2) rasters of supraglacial lake depths derived from Landast-8 imagery acquired over the same period. The datasets presented here were used to analyse the spatial distribution and interannual variability in lake distributions and volume. Funding was provided by NERC DTP grant NE/L002590/1 and NERC grant NE/R000824/1.

Datasets from the Resolving subglacial properties, hydrological networks and dynamic evolution of ice flow on the Greenland Ice Sheet (RESPONDER) project as published in the paper by Chudley et al. entitled "Controls on water storage and drainage in crevasses on the Greenland Ice Sheet". This dataset consists of remotely sensed observations of water-filled crevasses across a marine-terminating sector of the west Greenland Ice Sheet between 2017 and 2019.The dataset presented here includes all data necessary to replicate the findings presented in the main paper, including UAV photogrammetry-derived raster data (producing a series of orthophotos and digital elevation models) and observations from satellite-derived data (Sentinel-2, ArcticDEM, and MEaSUREs Greenland velocity data) of crevasse presence, water presence, and estimates of surface stress. This research was funded by the European Research Council as part of the RESPONDER project under the European Union''s Horizon 2020 research and innovation program (Grant 683043). Tom Chudley was supported by a Natural Environment Research Council Doctoral Training Partnership Studentship (Grant NE/L002507/1).

Datasets from the Resolving subglacial properties, hydrological networks and dynamic evolution of ice flow on the Greenland Ice Sheet (RESPONDER) project as published in the paper by Chudley et al. entitled "Supraglacial lake drainage at a fast-flowing Greenlandic outlet glacier". Please cite this paper if using this data. This dataset consists of observations of the rapid drainage of a supraglacial lake on Store Glacier, a marine-terminating outlet glacier of the west Greenland Ice Sheet. ''Lake 028'', located 70.57degN, 50.08degW, drained on 2018-07-07 and was recorded using a variety of geophysical instrumentation. The dataset presented here includes all data necessary to replicate the findings presented in the main paper, including UAV photogrammetry-derived raster data (producing a series of orthophotos, digital elevation models, and velocity fields) and time-series records from in-situ geophysical instrumentation (GPS receiver, geophone, and water pressure sensor). Funding was provided by NERC DTP grant NE/L002507/1 and ERC Horizon 2020 grant 683043.

Meteorological variables (wind speed, air temperature and wind direction) were collected using two wind towers. Photogrammetric data were collected using a pole-mounted digital camera and DJI Phantom 3 UAV. Sites were Storglaciaren and Sydostra Kaskasatjakkaglaciaren, both in the Tarfala Valley in Arctic Sweden. Fieldwork was carried out between the 8th and 20th of July 2017, by Mark Smith, Duncan Quincey and Jonathan Carrivick. Wind towers recorded data continuously for the study period, and photogrammetric data were collected from each site on alternate days. Data from both sources were used to estimate glacier aerodynamic roughness (z0) for a method comparison. Funding was provided by NERC DTP grant NE/L002574/1