Satellite-based monitoring of forage quality in grasslands of the United Kingdom using sentinel-2 data and random forest regression
The calibrated models are suitable for forage systems with species composition and quality ranges similar to those represented in our dataset but should not be directly applied to other forage types without recalibration. Overall, this work demonstrates the potential of Sentinel-2 remote sensing combined with machine-learning approaches for tolerably accurate, large-scale forage quality monitoring. Such advancements could help improve grazing management, support nutritional planning, and contribute to efforts aimed at reducing methane emissions from livestock systems.
Authors
- Marcos Texeira (ORCID: https://orcid.org/0000-0002-6355-5113)
- J. Gonzalo N. Irisarri (ORCID: https://orcid.org/0000-0001-8610-3040)
- Keith G. Pembleton (ORCID: https://orcid.org/0000-0002-1896-4516)
- Paul Harris (ORCID: https://orcid.org/0000-0003-0259-4079)
Institutions
- University of Wyoming (US)
- University of Southern Queensland (AU)
- Rothamsted Research (GB)
- Agricultural Plant Physiology and Ecology Research Institute (AR)
Publication Details
- Journal
- Frontiers in Veterinary Science
- Published
- 2025-12-19
- DOI
- https://doi.org/10.3389/fvets.2025.1678123
- Citations
- 1
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 1.72
Funders
- National Aeronautics and Space Administration
- University of Wyoming
- UK Research and Innovation
- Nuclear Safety and Security Commission
- Directorate for Biological Sciences
- Biotechnology and Biological Sciences Research Council
- Office of Experimental Program to Stimulate Competitive Research