Articles | Volume 20, issue 2
https://doi.org/10.5194/bg-20-383-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-20-383-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Jan 2023
Research article |
| 25 Jan 2023
| 25 Jan 2023
Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing
Matthew P. Dannenberg
CORRESPONDING AUTHOR
Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA 52245, USA
Mallory L. Barnes
O'Neill School of Public and Environmental Affairs, Indiana University, Bloomington, IN 47405, USA
William K. Smith
School of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721, USA
Miriam R. Johnston
Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA 52245, USA
Susan K. Meerdink
Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA 52245, USA
Xian Wang
O'Neill School of Public and Environmental Affairs, Indiana University, Bloomington, IN 47405, USA
School of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721, USA
Russell L. Scott
Southwest Watershed Research Center, Agricultural Research Service, U.S. Department of Agriculture, Tucson, AZ 85719, USA
Joel A. Biederman
Southwest Watershed Research Center, Agricultural Research Service, U.S. Department of Agriculture, Tucson, AZ 85719, USA
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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.
Earth's drylands provide ecosystem services to many people and will likely be strongly affected...
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