Articles | Volume 17, issue 2
Research article
08 Feb 2023
Research article |  | 08 Feb 2023

Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets

Timbo Stillinger, Karl Rittger, Mark S. Raleigh, Alex Michell, Robert E. Davis, and Edward H. Bair


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-159', Roswitha Stolz, 11 Sep 2022
  • CC1: 'Comment on tc-2022-159', Xiongxin Xiao, 21 Sep 2022
  • RC2: 'Comment on tc-2022-159', Anonymous Referee #2, 21 Sep 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (24 Nov 2022) by Franziska Koch
AR by Timbo Stillinger on behalf of the Authors (23 Dec 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (27 Dec 2022) by Franziska Koch
Short summary
Understanding global snow cover is critical for comprehending climate change and its impacts on the lives of billions of people. Satellites are the best way to monitor global snow cover, yet snow varies at a finer spatial resolution than most satellite images. We assessed subpixel snow mapping methods across a spectrum of conditions using airborne lidar. Spectral-unmixing methods outperformed older operational methods and are ready to to advance snow cover mapping at the global scale.