Abstract
Characterizing vegetation phenology is a highly
significant problem, due to its importance in regulating ecosystem
carbon cycling, interacting with climate changes, and
decision-making of croplands managements. While ground
based sensors, such as the AmeriFlux sensors, can provide
measurements at high temporal resolution (every hour) and can
be used to accurately calculate vegetation phenology indices,
they are limited to only a few sites. Remote sensing data, such as
the Normalized Difference Vegetation Index (NDVI), collected
using the MODerate Resolution Imaging Spectroradiometer
(MODIS), can provide global coverage, though at a much
coarser temporal resolution (16 days). In this study we use
data mining based time series segmentation methods to derive
phenology indices from NDVI data, and compare it with the
phenology indices derived from the AmeriFlux data using a
widely used model fitting approach. Results show a significant
correlation (as high as 0.60) between the indices derived from
these two different data sources. This study demonstrates
that data driven methods can be effectively employed to
provide realistic estimates of vegetation phenology indices using
periodic time series data and has the potential to be used at
large spatial scales and for long-term remote sensing data.
