Articles | Volume 17, issue 3
Research article
10 Mar 2023
Research article |  | 10 Mar 2023

High-resolution debris-cover mapping using UAV-derived thermal imagery: limits and opportunities

Deniz Tobias Gök, Dirk Scherler, and Leif Stefan Anderson


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-113', Sam Herreid, 21 Aug 2022
    • AC1: 'Reply on RC1', Deniz Gök, 07 Oct 2022
  • RC2: 'Comment on tc-2022-113', Anonymous Referee #2, 07 Sep 2022
    • AC2: 'Reply on RC2', Deniz Gök, 07 Oct 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (19 Oct 2022) by Kang Yang
AR by Deniz Gök on behalf of the Authors (01 Nov 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (11 Nov 2022) by Kang Yang
RR by Levan Tielidze (11 Feb 2023)
ED: Publish subject to technical corrections (16 Feb 2023) by Kang Yang
AR by Deniz Gök on behalf of the Authors (20 Feb 2023)  Manuscript 
Short summary
We performed high-resolution debris-thickness mapping using land surface temperature (LST) measured from an unpiloted aerial vehicle (UAV) at various times of the day. LSTs from UAVs require calibration that varies in time. We test two approaches to quantify supraglacial debris cover, and we find that the non-linearity of the relationship between LST and debris thickness increases with LST. Choosing the best model to predict debris thickness depends on the time of the day and the terrain aspect.