Articles | Volume 13, issue 7
https://doi.org/10.5194/gmd-13-3439-2020
https://doi.org/10.5194/gmd-13-3439-2020
Model description paper
 | 
31 Jul 2020
Model description paper |  | 31 Jul 2020

Efficient multi-scale Gaussian process regression for massive remote sensing data with satGP v0.1.2

Jouni Susiluoto, Alessio Spantini, Heikki Haario, Teemu Härkönen, and Youssef Marzouk

Download

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Jouni Susiluoto on behalf of the Authors (02 May 2020)  Author's response   Manuscript 
ED: Publish subject to minor revisions (review by editor) (03 Jun 2020) by Klaus Gierens
AR by Jouni Susiluoto on behalf of the Authors (13 Jun 2020)  Author's response   Manuscript 
ED: Publish as is (18 Jun 2020) by Klaus Gierens
AR by Jouni Susiluoto on behalf of the Authors (29 Jun 2020)  Manuscript 
Download
Short summary
We describe a new computer program that is able produce maps of carbon dioxide or other quantities based on data collected by satellites that orbit the Earth. When working with such data there is often too much data in one area and none in another. The program is able to describe the fields even when data is not available. To be able to do so, new computational methods were developed. The program is also able to describe how uncertain the estimated carbon dioxide or other fields are.