Articles | Volume 20, issue 2
https://doi.org/10.5194/bg-20-383-2023
https://doi.org/10.5194/bg-20-383-2023
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

Viewed

Total article views: 1,980 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,415 524 41 1,980 131 33 24
  • HTML: 1,415
  • PDF: 524
  • XML: 41
  • Total: 1,980
  • Supplement: 131
  • BibTeX: 33
  • EndNote: 24
Views and downloads (calculated since 30 Sep 2022)
Cumulative views and downloads (calculated since 30 Sep 2022)

Viewed (geographical distribution)

Total article views: 1,980 (including HTML, PDF, and XML) Thereof 1,927 with geography defined and 53 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 16 Jul 2024
Download
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.
Altmetrics
Final-revised paper
Preprint