Modeling landscape variability in grasslands productivity with DayCent and remote-sensing imagery across a precipitation gradient

  • Johny Arteaga*
  • , Melannie D. Hartman
  • , William J. Parton
  • , Mitchell B. Stephenson
  • , Jerry Volesky
  • , Maosi Chen
  • , Darrin Sharp
  • , Jonathan Straube
  • , Wei Gao
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We introduced improvements to the DayCent-UV ecosystem model to better capture landscape variability in historical ANPP observations across three contrasting grassland ecosystems along a precipitation gradient: the shortgrass steppe, mixed-grass prairie, and tallgrass prairie. Additionally, we evaluated the ability of satellite remote sensing to detect this landscape-level variation in productivity.

Original languageEnglish
Title of host publicationRemote Sensing and Modeling of Ecosystems for Sustainability XVI
EditorsWei Gao, Jinnian Wang
PublisherSPIE
ISBN (Electronic)9781510691407
DOIs
StatePublished - 19 Sep 2025
Externally publishedYes
Event16th Remote Sensing and Modeling of Ecosystems for Sustainability - San Diego, United States
Duration: 6 Aug 20256 Aug 2025

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume13616
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference16th Remote Sensing and Modeling of Ecosystems for Sustainability
Country/TerritoryUnited States
CitySan Diego
Period6/08/256/08/25

Keywords

  • DayCent
  • Grasslands
  • net primary productivity
  • remote sensing
  • vegetation indices

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