A national dataset of annual urban extent (1985-2015)

Li, X., Y. Zhou*, Z. Zhu & W. Cao (2020) A national dataset of 30m annual urban extent dynamics (1985–2015) in the conterminous United States. Earth Syst. Sci. Data, 12, 357-371.

Li, X., Y. Zhou*, Z. Zhu, L. Liang, B. Yu, and W. Cao (2018), Mapping annual urban dynamics (1985–2015) using time series of Landsat data. Remote Sensing of Environment 216: 674-683.

We mapped urban dynamics from Landsat time series data using the Google Earth Engine (GEE) platform and developed a national dataset of annual urban extent (1985–2015) at a fine spatial resolution (30 m) in the conterminous United States (US).

Spatial Resolution: 30m;

Temporal Resolution: Annual;

Temporal Coverage: 1985-2015

A dataset of 30 m annual vegetation phenology indicators (1985–2015)

Li, X., Y. Zhou*, L. Meng, G. R. Asrar, C. Lu & Q. Wu (2019). A dataset of 30-meter annual vegetation phenology indicators (1985-2015) in urban areas of the conterminous United States. Earth Syst. Sci. Data, 2019. 11(2): 881-894.

Li, X., Y. Zhou*, G. R. Asrar and L. Meng (2017). Characterizing spatiotemporal dynamics in phenology of urban ecosystems based on Landsat data. Science of the Total Environment 605-606: 721-734.

In this study, we produced an annual vegetation phenology dataset (phenology indicators of the start of season (SOS) and the end of season (EOS)) in urban ecosystems for the conterminous United States (US), using all available Landsat images on the Google Earth Engine (GEE) platform.

Spatial Resolution: 30m;

Temporal Resolution: Annual;

Temporal Coverage: 1985-2015

Global Calibrated Nighttime light at a 1km spatial resolution (1992-2013)

Li, X.; Zhou, Y. A Stepwise Calibration of Global DMSP/OLS Stable Nighttime Light Data (1992–2013). Remote Sens. 2017, 9, 637.

We developed a temporally consistent NTL dataset using a stepwise approach to calibrate the NTL time series. Each step was designed to calibrate particular satellites (or years) with systematic over- or under-estimation. Our calibrated NTL observations show a temporally more consistent pattern of NTL time series, as well as a good agreement with socioeconomic data, such as GDP and electricity consumption.

Spatial Resolution: 1km; Temporal Resolution: Annual; Temporal Coverage: 1992-2013