Machine learning and global vegetation: random  forests for downscaling and gap filling

Articles | Volume 28, issue 11

https://doi.org/10.5194/hess-28-2357-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-28-2357-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 28, issue 11

Research article

|

05 Jun 2024

Research article |

| 05 Jun 2024

Barry van Jaarsveld, Sandra M. Hauswirth, and Niko Wanders

Data sets

MOD13A2 MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid V006 K. Didan https://doi.org/10.5067/MODIS/MYD13A2.061

MCD12C1 v061 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05 Deg CMG USGS https://doi.org/10.5067/MODIS/MCD12C1.061

ERA5-Land hourly data from 1950 to present J. Muñoz Sabater https://doi.org/10.24381/cds.e2161bac

Hourly potential evapotranspiration (hPET) at 0.1degs grid resolution for the global land surface from 1981-present Michael Singer et al. https://doi.org/10.5523/bris.qb8ujazzda0s2aykkv0oq0ctp

Short summary

Drought often manifests itself in vegetation; however, obtaining high-resolution remote-sensing...