Cloud tolerance of remote-sensing technologies to measure land surface
temperature

Holmes, Thomas R. H.; Hain, Christopher R.; Anderson, Martha C.; Crow, Wade T.

doi:https://doi.org/10.5194/hess-20-3263-2016

Articles | Volume 20, issue 8

https://doi.org/10.5194/hess-20-3263-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-20-3263-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 20, issue 8

Research article

|

11 Aug 2016

Research article |

| 11 Aug 2016

Cloud tolerance of remote-sensing technologies to measure land surface temperature

Thomas R. H. Holmes, Christopher R. Hain, Martha C. Anderson, and Wade T. Crow

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Final revised paper (published on 11 Aug 2016)
Preprint (discussion started on 21 Apr 2016)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'review', Catherine Prigent, 19 May 2016
- AC1: 'Author Comment to Referee #1', Thomas Holmes, 10 Jun 2016
RC2: 'Comments', Anonymous Referee #2, 23 May 2016
- AC2: 'Author Comment to Referee #2', Thomas Holmes, 10 Jun 2016

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Reconsider after major revisions (27 Jun 2016) by Alexander Loew (deceased)

AR by Thomas Holmes on behalf of the Authors (01 Jul 2016) Author's response Manuscript

ED: Referee Nomination & Report Request started (12 Jul 2016) by Alexander Loew (deceased)

RR by Anonymous Referee #2 (12 Jul 2016)

ED: Publish as is (21 Jul 2016) by Alexander Loew (deceased)

AR by Thomas Holmes on behalf of the Authors (22 Jul 2016)

Short summary

We test the cloud tolerance of two technologies to estimate land surface temperature (LST) from space: microwave (MW) and thermal infrared (TIR). Although TIR has slightly lower errors than MW with ground data under clear-sky conditions, it suffers increasing negative bias as cloud cover increases. In contrast, we find no direct impact of clouds on the accuracy and bias of MW-LST. MW-LST can therefore be used to improve TIR cloud screening and increase sampling in clouded regions.