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The characteristics of convective clouds on infrared brightness temperature (BTIR) and brightness temperature difference (BTD) image were analyzed using successive Infrared and Visible Spin-Scan Radiometer (VISSR) data of FY-2, and an integrated detection method of convective clouds using infrared multi-thresholds in combination with tracking techniques was implemented. In this method, BT and BTD thresholds are used to detect severe convection and uncertain clouds, then the tracking technique including overlap ratio, minimum BT change and cross-correlation coefficient is used to detect convection activities in uncertain clouds. The Application test results show that our integrated detection method can effectively detect convective clouds in different life periods, which show a better performance than any single step in it. The statistical results show that the α-type clouds are mostly large-scale systems, and the β- and γ-type clouds have the highest proportion of general type. However, the proportion of weak convective cloud is higher than that of severe ones in γ-type cloud, and an opposite result is found in the β-type.

In order to monitor nighttime particular matter (PM) air quality in urban area, a back propagation neural network (BP neural network) inversion model is established, using low-light radiation data from the day/night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. The study focuses on the moonless and cloudless nights in Beijing during March–May 2015. A test is carried out by selecting surface PM2.5 data from 12 PM2.5 automatic monitoring stations and the corresponding night city light intensity from DNB. As indicated by the results, the linear correlation coefficient (R) between the results and the corresponding measured surface PM2.5 concentration is 0.91, and the root-mean-square error (RMSE) is 14.02 μg/m3 with the average of 59.39 μg/m3. Furthermore, the BP neural network model shows better accuracy when air relative humility ranges from 40% to 80% and surface PM2.5 concentration exceeds 40 μg/m3. The study provides a superiority approach for monitoring PM2.5 air quality from space with visible light remote sensing data at night.

In order to monitor nighttime particular matter (PM) air quality in urban area, a back propagation neural network (BP neural network) inversion model is established, using low-light radiation data from the day/night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. The study focuses on the moonless and cloudless nights in Beijing during March-May 2015. A test is carried out by selecting surface PM2.5 data from 12 PM2.5 automatic monitoring stations and the corresponding night city light intensity from DNB. As indicated by the results, the linear correlation coefficient (R) between the results and the corresponding measured surface PM2.5 concentration is 0.91, and the root-mean-square error (RMSE) is 14.02 μg/m³ with the average of 59.39 μg/m³. Furthermore, the BP neural network model shows better accuracy when air relative humility ranges from 40% to 80% and surface PM2.5 concentration exceeds 40 μg/m³. The study provides a superiority approach for monitoring PM2.5 air quality from space with visible light remote sensing data at night. [ABSTRACT FROM AUTHOR]