Modeling landscape variability in grasslands productivity with DayCent and remote-sensing imagery across a precipitation gradient
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.
Authors
- David Sharp (ORCID: https://orcid.org/0000-0002-8364-1705)
- Johny Arteaga (ORCID: https://orcid.org/0009-0003-4028-5063)
- Melannie D. Hartman (ORCID: https://orcid.org/0000-0002-0675-2292)
- Jerry D. Volesky (ORCID: https://orcid.org/0000-0002-4487-6325)
- Maosi Chen
- Jonathan Straube
- Mitchell Stephenson (ORCID: https://orcid.org/0000-0002-1094-2777)
- William J. Parton (ORCID: https://orcid.org/0000-0003-4151-7869)
- Wei Gao (ORCID: https://orcid.org/0000-0001-9502-722X)
Institutions
- University of Nebraska–Lincoln (US)
- Colorado State University (US)
Publication Details
- Published
- 2025-09-18
- DOI
- https://doi.org/10.1117/12.3064678
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 0.00