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

Abstract v), were generally the most important variables contributing to model skill. However, daytime and nighttime land surface temperatures and SMAP soil moisture and soil temperature also contributed to model skill, with SMAP especially improving model predictions of shrubland, grassland, and savanna fluxes and land surface temperatures improving predictions in evergreen needleleaf forests. Our results show that a combination of optical vegetation indices and thermal infrared and microwave observations can substantially improve estimates of carbon and water fluxes in drylands, potentially providing the means to better monitor vegetation function and ecosystem services in these important regions that are undergoing rapid hydroclimatic change.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing ; volume:20 ; number:2 ; year:2023 ; pages:383-404 ; extent:22 ; Biogeosciences ; 20, Heft 2 (2023), 383-404 (gesamt 22)

Contributors, Places and Time

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

Further information

• DOI: 10.5194/bg-20-383-2023
• URN: urn:nbn:de:101:1-2023033008042965244867
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 11:03 AM CEST