Articles | Volume 20, issue 2
Research article
25 Jan 2023
Research article |  | 25 Jan 2023

Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing

Matthew P. Dannenberg, Mallory L. Barnes, William K. Smith, Miriam R. Johnston, Susan K. Meerdink, Xian Wang, Russell L. Scott, and Joel A. Biederman


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2022-186', Andrew Feldman, 19 Oct 2022
    • AC1: 'Reply on RC1', Matthew Dannenberg, 02 Dec 2022
  • RC2: 'Comment on bg-2022-186', Anonymous Referee #2, 21 Oct 2022
    • AC2: 'Reply on RC2', Matthew Dannenberg, 02 Dec 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (09 Dec 2022) by Paul Stoy
AR by Matthew Dannenberg on behalf of the Authors (19 Dec 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (20 Dec 2022) by Paul Stoy
Short summary
Earth's drylands provide ecosystem services to many people and will likely be strongly affected by climate change, but it is quite challenging to monitor the productivity and water use of dryland plants with satellites. We developed and tested an approach for estimating dryland vegetation activity using machine learning to combine information from multiple satellite sensors. Our approach excelled at estimating photosynthesis and water use largely due to the inclusion of satellite soil moisture.
Final-revised paper