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


Total article views: 872 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
613 241 18 872 59 9 7
  • HTML: 613
  • PDF: 241
  • XML: 18
  • Total: 872
  • Supplement: 59
  • BibTeX: 9
  • EndNote: 7
Views and downloads (calculated since 30 Sep 2022)
Cumulative views and downloads (calculated since 30 Sep 2022)

Viewed (geographical distribution)

Total article views: 872 (including HTML, PDF, and XML) Thereof 818 with geography defined and 54 with unknown origin.
Country # Views %
  • 1
Latest update: 19 Mar 2023
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