Journal Article
Earth System Science Data, vol. 11, iss. 2, pp. 881-894, 2019
Authors
Xuecao Li, Yuyu Zhou, Lin Meng, Ghassem R. Asrar, Chaoqun Lu, Qiusheng Wu
Abstract
Abstract. Medium-resolution satellite observations show great
potential for characterizing seasonal and annual dynamics of vegetation
phenology in urban domains from local to regional and global scales.
However, most previous studies were conducted using coarse-resolution data,
which are inadequate for characterizing the spatiotemporal dynamics of
vegetation phenology in urban domains. In this study, we produced an annual
vegetation phenology dataset in urban ecosystems for the conterminous United
States (US), using all available Landsat images on the Google Earth Engine
(GEE) platform. First, we characterized the long-term mean seasonal pattern
of phenology indicators of the start of season (SOS) and the end of season
(EOS), using a double logistic model. Then, we identified the annual
variability of these two phenology indicators by measuring the difference of
dates when the vegetation index in a specific year reaches the same
magnitude as its long-term mean. The derived phenology indicators agree well
with in situ observations from the PhenoCam network and Harvard Forest. Comparing with
results derived from the moderate-resolution imaging spectroradiometer
(MODIS) data, our Landsat-derived phenology indicators can provide more
spatial details. Also, we found the temporal trends of phenology indicators
(e.g., SOS) derived from Landsat and MODIS are consistent overall, but the
Landsat-derived results from 1985 offer a longer temporal span compared to
MODIS from 2001 to present. In general, there is a spatially explicit
pattern of phenology indicators from the north to the south in cities in the
conterminous US, with an overall advanced SOS in the past 3 decades. The
derived phenology product in the US urban domains at the national level is
of great use for urban ecology studies for its medium spatial resolution (30 m) and long temporal span (30 years). The data are available at
https://doi.org/10.6084/m9.figshare.7685645.v5.
