Terrestrial vegetation dynamics and global climate controls
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Manual, on-the-ground observation can be accelerated by annotation of remote sensing data.
“This has traditionally been done via manual, on-the-ground observation, but the process can be accelerated by annotation of remote sensing data [91,92,513,637] (see also Section 6.1).”
From Tackling climate change with machine learning (2023) · cites this paper as [637]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Potter, C., Boriah, S., Steinbach, M., Kumar, V., & Klooster, S.
- Affiliations
- Ames Research Center (US)
- University of Minnesota (US)
- California State University, Monterey Bay (US)
- Corresponding author
- Potter, Christopher
- Venue
- Climate Dynamics
- Published
- 2007-11-27
- Volume
- 31
- Issue
- 1
- Pages
- 67-78
- ISSN
- 0930-7575 (ISSN-L)
- 1432-0894
- OpenAlex
- W2107502340
- Cited by
- 42
- References
- 49
- Citations by year
- 2024: 1 · 2023: 7 · 2022: 4 · 2021: 3 · 2020: 2 · 2019: 1 · 2018: 5 · 2016: 2 · 2015: 2 · 2014: 2 · 2013: 4 · 2012: 2
- FWCI
- 2.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Climate Dynamics (publishedVersion)
- Funders
- National Aeronautics and Space Administration
- Topics
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Global and Planetary Change › Plant Water Relations and Carbon Dynamics
- Keywords
- ENSO, MODIS, rainfall variability, drought, 1997-1998 El Niño, Southern Africa, Central Asia
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