The massive lockdown of human socioeconomic activities and vehicle movements due to the COVID-19 pandemic in 2020 has resulted in an unprecedented reduction in pollutant gases such as Nitrogen Dioxide(NO_(2))and Carbo...The massive lockdown of human socioeconomic activities and vehicle movements due to the COVID-19 pandemic in 2020 has resulted in an unprecedented reduction in pollutant gases such as Nitrogen Dioxide(NO_(2))and Carbon Monoxide(CO)as well as Land Surface Temperature(LST)in Amman as well as all countries around the globe.In this study,the spatial and temporal variability/stability of NO_(2),CO,and LST throughout the lockdown period over Amman city have been analyzed.The NO_(2) and CO column density values were acquired from Sentinel-5p while the LST data were obtained from MODIS satellite during the lockdown period from 20 March to 24 April in 2019,2020,and 2021.The statistical analysis showed an overall reduction in NO_(2) in 2020 by around 27% and 48% compared to 2019 and 2021,respectively.However,an increase of 7% in 2021 compared to 2019 was observed because almost all anthropogenic activities were allowed during the daytime.The temporal persistence showed almost constant NO2 values in 2020 over the study area throughout the lockdown period.In addition,a slight decrease in CO(around 1%)was recorded in 2020 and 2021 compared to the same period in 2019.Restrictions on human activities resulted in an evident drop in LST in 2020 by around 13%and 18% less than the 5-year average and 2021 respectively.The study concludes that due to the restrictions imposed on industrial activities and automobile movements in Amman city,an unprecedented reduction in NO_(2),CO,and LST was recorded.展开更多
This paper proposes an applicable approach for snow information abstraction in northern Xinjiang Basin using MODIS data. Linear spectral mixture analysis （LSMA） was used to calculate snow cover fractions （SF） with...This paper proposes an applicable approach for snow information abstraction in northern Xinjiang Basin using MODIS data. Linear spectral mixture analysis （LSMA） was used to calculate snow cover fractions （SF） within a pixel, which was used to establish a regression function with NDSI. In addition, 80 snow depths samples were collected in the study region. The correlation between image spectra reflectance and snow depth as well as the comparison between measured snow spectra and image spectra was analyzed. An algorithm was developed for snow depth inversion on the basis of the correlation between snow depth and snow spectra in the region. The results indicated that the model of SF had a high accuracy with the mean absolute error 0.06 tested by 26 true measured values and the validation for snow depth model using another dataset with 50 sampling sites showed an RMSE of 1.63. Our study showed that MODIS data provide an alternative method for snow information abstraction through development of algorithms suitable for local application.展开更多
文摘The massive lockdown of human socioeconomic activities and vehicle movements due to the COVID-19 pandemic in 2020 has resulted in an unprecedented reduction in pollutant gases such as Nitrogen Dioxide(NO_(2))and Carbon Monoxide(CO)as well as Land Surface Temperature(LST)in Amman as well as all countries around the globe.In this study,the spatial and temporal variability/stability of NO_(2),CO,and LST throughout the lockdown period over Amman city have been analyzed.The NO_(2) and CO column density values were acquired from Sentinel-5p while the LST data were obtained from MODIS satellite during the lockdown period from 20 March to 24 April in 2019,2020,and 2021.The statistical analysis showed an overall reduction in NO_(2) in 2020 by around 27% and 48% compared to 2019 and 2021,respectively.However,an increase of 7% in 2021 compared to 2019 was observed because almost all anthropogenic activities were allowed during the daytime.The temporal persistence showed almost constant NO2 values in 2020 over the study area throughout the lockdown period.In addition,a slight decrease in CO(around 1%)was recorded in 2020 and 2021 compared to the same period in 2019.Restrictions on human activities resulted in an evident drop in LST in 2020 by around 13%and 18% less than the 5-year average and 2021 respectively.The study concludes that due to the restrictions imposed on industrial activities and automobile movements in Amman city,an unprecedented reduction in NO_(2),CO,and LST was recorded.
基金Supported by the National Natural Science Foundation of China （No.70361001）.
文摘This paper proposes an applicable approach for snow information abstraction in northern Xinjiang Basin using MODIS data. Linear spectral mixture analysis （LSMA） was used to calculate snow cover fractions （SF） within a pixel, which was used to establish a regression function with NDSI. In addition, 80 snow depths samples were collected in the study region. The correlation between image spectra reflectance and snow depth as well as the comparison between measured snow spectra and image spectra was analyzed. An algorithm was developed for snow depth inversion on the basis of the correlation between snow depth and snow spectra in the region. The results indicated that the model of SF had a high accuracy with the mean absolute error 0.06 tested by 26 true measured values and the validation for snow depth model using another dataset with 50 sampling sites showed an RMSE of 1.63. Our study showed that MODIS data provide an alternative method for snow information abstraction through development of algorithms suitable for local application.
