The results show that the use of different rural areas in the urban heat island (UHI) calculation influences the value of UHI and its seasonal variation. Daytime UHI shows a distinct seasonal variation, the maximum during summer being larger than 10°C, while conspicuous negative UHI occurs in winter and spring. Seasonal variation of nighttime UHI is much less. The contrast in thermal inertia between rural and urban areas, anthropogenic heat from the urban area and less latent heat flux over urban areas are the main factors influencing daytime UHI, whereas anthropogenic heat controls the nighttime UHI. Surface broadband emissivity derived from MODIS LST/emissivity for the urban area is nearly equal to the rural areas. Surface albedo over the urban area is 0.03-0.08 less than that of rural areas, but aerosols substantially reduce surface incoming solar radiation over the urban area, which results in the surface absorbed solar radiation being nearly equal for urban and rural areas during autumn. Diurnal variation of UHI demonstrates a distinctively seasonal variation. The accuracy of MODIS LST is investigated and it was found that the influence of satellite view angle on the calculated UHI is small enough to be ignored.

In the spring of 2005, a Sun photometer and a set of broadband pyranometers were installed in Liaozhong, a suburban region in northeastern China. Aerosol properties derived from Sun photometer measurements and aerosol‐induced changes in downwelling shortwave surface irradiances are analyzed in this paper. It is shown that the mean aerosol optical depth (AOD) at 500 nm is 0.63. The day‐to‐day variation of aerosol optical depth is dramatic, with a maximum daily AOD close to 2.0 and a minimum value close to the background level. Dust activities generally produce heavy aerosol loading characterized by larger particle sizes and less absorption than those observed under normal conditions. The reduction of instantaneous direct shortwave surface irradiance per unit of AOD is 404.5 W m−2. About 63.8% of this reduction is offset by an increase in diffuse irradiance; consequently, one unit increase in AOD leads to a decrease in global surface irradiance of 146.3 W m−2. The diurnal aerosol direct radiative forcing efficiency is about −47.4 W m−2. Overall, aerosols reduce about 30 W m−2 per day of surface net shortwave irradiance in this suburban region.

With the development of satellite networking technology, low-orbit satellites are gradually facing security threats caused by abnormal traffic. Since some types of attack traffic do not pass through ground stations, we are trying to migrate ground anomaly detection capabilities to satellites. In prior arts, many deep learning methods have been applied to network traffic anomaly detection. However, most of them have deep layers of neural networks and are unsuitable for satellites with limited energy and computing capacity. In this paper, we present SAT-NTAD, an effective low-orbit satellite network traffic anomaly detection method that combines shallow Convolutional Auto-encoder (CAE) and Bidirectional Simple Recurrent Unit (Bi-SRU) to extract the spatial and contextual features of traffic flows, and detects anomalies by utilizing dynamic threshold. Extensive experimental evaluations show that our method can stably maintain the high performance for abnormality detection, which outperforms state-of-the-art approaches, and effectively reduces the computational cost, which is more suitable for the low computing capacity environment of satellites.