Peichl, Matthias
- Department of Forest Ecology and Management, Swedish University of Agricultural Sciences
Abstract
Phenology is an important indicator of annual plant growth and is also widely incorporated in ecosystem models to simulate interannual variability of ecosystem productivity under climate change. A comprehensive understanding of the potentials of current algorithms to detect the start and end for growing season (SOS and EOS) from remote sensing is still lacking. This is particularly true when considering the diverse interactions between phenology and climate change among plant functional types as well as potential influences from different sensors. Using data from 60 flux tower sites (376 site-years in total) from the global FLUXNET database, we applied four algorithms to extract plant phenology from time series of normalized difference vegetation index (NDVI) from both MODIS and SPOT-VGT sensors. Results showed that NDVI-simulated phenology had overall low correlation (R-2
Keywords
Phenology; SOS/EOS; Remote sensing; NDVI; MODIS; SPOT-VGT; Forest
Published in
Agricultural and Forest Meteorology
2017, volume: 233, pages: 171-182
SLU Authors
Global goals (SDG)
UKÄ Subject classification
Forest Science
Publication identifier
- DOI: https://doi.org/10.1016/j.agrformet.2016.11.193
Permanent link to this page (URI)
https://res.slu.se/id/publ/78792