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  • Metrics of dark ice extent and duration, and snowline retreat estimates, for the south-west ablation zone of the Greenland Ice Sheet, derived from MODIS satellite imagery. These metrics are provided on a ~613 m grid at annual resolution and cover the melt season, defined as June-July-August each year. All scripts used to generate the metrics are also provided, as well as the scripts which generate the plots found in the referenced publication. Funding was provided by the NERC grant NE/M021025/1.

  • This dataset consists of 75 destructive ice surface samples for which glacier algae cell counts were undertaken. The sample locations were distributed randomly over a 250 X 250 m area. Funding was provided by the NERC standard grant NE/M021025/1.

  • This dataset consists of (1) broadband albedo calculated using a narrowband-to-broadband approximation and (2) surface type classification into snow, clean ice, light algae, heavy algae, cryoconite and water, as determined by a supervised classification algorithm, as applied to Sentinel-2 overpasses of S6, K-transect, south-west Greenland on 20 and 21 July 2017. Funding was provided by the NERC standard grant NE/M021025/1.

  • The abundance, photophysiology, pigmentation, bio-optical properties, cellular energy balance and instantaneous radiative forcing of glacier algal assemblages from the surface of the Greenland Ice Sheet (GrIS) are quantified throughout the 2016 ablation season. The effects of assemblages on ice surface albedo are further derived using a newly developed model of glacier algal blooms for the GrIS, radiative transfer modelling using BioSNICAR-GO, and comparisons to MODIS broadband albedo observations over the same season. Data represent a composite of in-situ observations, in-situ incubations studies, laboratory analyses, modelling and remote sensing. All in -situ work was performed at site S6 of the K-Transect in the southwestern GrIS ablation zone as part of the Black and Bloom project. Funding was provided by the NERC ''Black and Bloom'' grant NE/M021025/1 and the Marie Sklodowska-Curie grant agreement No 675546.

  • This dataset consists of the unprocessed radiance measurements downloaded directly from the unmanned aerial system imaging platform used to image the ice sheet surface near UPE_U in the north-west of the Greenland Ice Sheet, along with captures of reflectance panels and sensor calibration parameters which enable these imagery to be transformed to reflectance measurements. Funding was provided by the NERC standard grant NE/M021025/1.

  • This dataset consists of the unprocessed radiance measurements downloaded directly from the unmanned aerial system imaging platform used to image the ice sheet surface at S6 on the south-west Greenland K-transect during July 2017, along with captures of reflectance panels and sensor calibration parameters which enable these imagery to be transformed to reflectance measurements. Funding was provided by the NERC standard grant NE/M021025/1.

  • This dataset consists of orthomosaics created from flights of an unmanned aerial system imaging platform at UPE_U in north-west Greenland on 24 July 2018. The Level-2 orthomosaics consist of (1) ground reflectance at 5 spectral bands, and (2) a digital elevation model. Level-3 orthomosaics consist of (1) broadband albedo calculated using a narrowband-to-broadband approximation and (2) surface type classification into snow, clean ice, light algae, heavy algae, cryoconite and water, as determined by a supervised classification algorithm which was trained on measurements collected at S6, K-transect, south-west Greenland. Funding was provided by the NERC standard grant NE/M021025/1.

  • This dataset consists of orthomosaics created from flights of an unmanned aerial system imaging platform at S6 on the south-west Greenland K-transect during July 2017. Level-2 orthomosaics consist of (1) ground reflectance at 5 spectral bands, and (2) digital elevation models (only for 2017-07-20 and 2017-07-21). Level-3 orthomosaics consist of (1) broadband albedo calculated using a narrowband-to-broadband approximation and (2) surface type classification into snow, clean ice, light algae, heavy algae, cryoconite and water, as determined by a supervised classification algorithm. Training data ingested by the classification algorithm are also provided. Funding was provided by the NERC standard grant NE/M021025/1.