Open AccessLand surface phenology from optical satellite measurement and CO 2 eddy covariance technique
Author(s) -
Gonsamo Alemu,
Chen Jing M.,
Price David T.,
Kurz Werner A.,
Wu Chaoyang
Publication year - 2012
Publication title -
journal of geophysical research: biogeosciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2012jg002070
Subject(s) - eddy covariance , environmental science , normalized difference vegetation index , phenology , enhanced vegetation index , remote sensing , atmospheric sciences , flux (metallurgy) , satellite , vegetation (pathology) , snow , boreal , taiga , leaf area index , climatology , ecosystem , meteorology , geography , vegetation index , ecology , geology , forestry , medicine , materials science , archaeology , pathology , metallurgy , biology , aerospace engineering , engineering
Land surface phenology (LSP) is an integrative indicator of vegetation dynamics under a changing environment. Increasing amounts of remote sensing measurements and CO 2 flux observations offer unprecedented opportunities to quantify LSP phases at landscape scale. LSP start of season (SOS) and end of season (EOS) estimates are often based on the use of a single‐purpose vegetation index derived from optical satellite data, characterized by poor performances in decoupling soil and snow cover dynamics from LSP cycles, as well as contrasting responses of the needleleaf and broadleaf forests in boreal ecosystems. We propose a new remote‐sensing‐based phenology index (PI) which combines the merits of normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) by taking the difference of squared greenness and wetness to remove the soil and snow cover dynamics from key vegetation LSP cycles. We have cross‐validated the remote‐sensing‐based LSP dates with those of CO 2 flux observations from 11 selected tower sites across Canada and the United States consisting of needleleaf forests, broadleaf forests, and croplands. The results indicate that PI estimates the SOS and EOS dates better than NDVI when compared to the LSP dates from CO 2 flux measurements (reduced RMSE, bias and dispersions, and higher correlation). PI‐based SOS and EOS estimates are in good agreement with those derived from CO 2 flux measurements with mean bias comparable to the temporal resolution of the high‐quality, 8‐day composite satellite measurements. Finally, PI also shows a smoother time series compared to NDVI and NDII.