Articles | Volume 17, issue 20
https://doi.org/10.5194/amt-17-6025-2024
https://doi.org/10.5194/amt-17-6025-2024
Research article
 | 
17 Oct 2024
Research article |  | 17 Oct 2024

HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution

Giulia Roccetti, Luca Bugliaro, Felix Gödde, Claudia Emde, Ulrich Hamann, Mihail Manev, Michael Fritz Sterzik, and Cedric Wehrum

Viewed

Total article views: 1,329 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,116 173 40 1,329 25 21
  • HTML: 1,116
  • PDF: 173
  • XML: 40
  • Total: 1,329
  • BibTeX: 25
  • EndNote: 21
Views and downloads (calculated since 06 Mar 2024)
Cumulative views and downloads (calculated since 06 Mar 2024)

Viewed (geographical distribution)

Total article views: 1,329 (including HTML, PDF, and XML) Thereof 1,305 with geography defined and 24 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 05 Dec 2024
Download
Short summary
The amount of sunlight reflected by the Earth’s surface (albedo) is vital for the Earth's radiative system. While satellite instruments offer detailed spatial and temporal albedo maps, they only cover seven wavelength bands. We generate albedo maps that fully span the visible and near-infrared range using a machine learning algorithm. These maps reveal how the reflectivity of different land surfaces varies throughout the year. Our dataset enhances the understanding of the Earth's energy balance.