ALBERT

Description: Polar mesosphere summer echoes (PMSEs) are strong radar echoes observed in the polar mesopause during the local summer. Observations of layered PMSEs carried out by the European Incoherent Scatter Scientific Association very-high-frequency (EISCAT VHF) radar during 2004–2015 in the latest solar cycle are used to study the variations of the PMSE occurrence ratio (OR). Different seasonal behavior of PMSEs is found by analyzing the seasonal variation of PMSE mono-, double-, and tri-layer OR. A method was used to calculate the PMSE mono, double-, and tri-layer OR under a different electron density threshold. In addition, a method to analyze the correlation of the layered PMSE OR with the solar 10.7 cm flux index (F10.7) and geomagnetic K index is proposed. Based on it, the correlation of the layered PMSE OR with solar and geomagnetic activities is not expected to be affected by discontinuous PMSEs. It is found that PMSE mono-, double-, and tri-layer ORs are positively correlated with the K index. The correlation of the PMSE mono- and double-layer OR with F10.7 is weak, whereas the PMSE tri-layer OR shows a negative correlation with F10.7.

Description: Polar Mesosphere Summer Echoes (PMSE) are strong radar echoes observed in polar mesopause during local summer. Measurements of layered PMSE observed by the European Incoherent Scatter Scientific Association Very high frequency (EISCAT VHF) radar from 2004 to 2015 in the latest solar cycle, can be used to study the variations of PMSE occurrence ratio (OR). The seasonal variation of PMSE mono-, double- and tri-layer occurrence ratio was analyzed, and there is different seasonal behavior. A method was given to calculate the PMSE mono-, double- and tri-layer occurrence ratio under different electron density threshold conditions. In addition, the correlation between PMSE layered occurrence ratios and solar 10.7 cm flux index (F10.7), and the correlation between PMSE layered occurrence ratios and geomagnetic K index were analyzed respectively in this study. It can be obtained that PMSE mono-, double- and tri-layer OR are positively correlated with the K index. The correlation coefficient between PMSE mono- and double-layer OR and F10.7 is weak, and the PMSE tri-layer OR has a negative correlation with F10.7.

Description: 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.

Description: Fine-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 or moderate 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 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, temporal trends of phenology indicators (e.g., SOS) derived from Landsat and MODIS are consistent overall, but the Landsat derived results from 1985 have a longer temporal span compared to MODIS from 2001. 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 three decades. The derived phenology product in the US urban domains at the national level is of great use for urban ecology studies for its fine spatial resolution (30 m) and long temporal span (30 years). The data are available at https://doi.org/10.6084/m9.figshare.7685645.v2.