Anthropogenic heat (AH) generated by human activities is an important factor affecting the urban climate. Thus, refined AH parameterization of a large area can provide data support for regional meteorological research. In this study, we developed a refined anthropogenic heat flux (RAHF) parameterization scheme to estimate the gridded anthropogenic heat flux (AHF). Firstly, the annual total AH emissions and annual mean AHF of Beijing municipality in the year 2015 were estimated using a top-down, energy-consumption inventory method, which was derived based on socioeconomic statistics and energy consumption data. The heat released from industry, transportation, buildings (including both commercial and residential buildings), and human metabolism were taken into account. Then, the county-scale AHF estimation model was constructed based on multi-source remote sensing data, such as Suomi national polar-orbiting partnership (Suomi-NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light (NTL) data and moderate resolution imaging spectroradiometer (MODIS) data. This model was applied to estimate the annual mean AHF of the counties in the Beijing–Tianjin–Hebei region. Finally, the gridded AHF data with 500-m resolution was obtained using a RAHF parameterization scheme. The results indicate that the annual total AH emissions of Beijing municipality in the year 2015 was approximately 1.704 × 10¹⁸ J. Of this, the buildings contribute about 34.5%, followed by transportation and industry with about 30.5% and 30.1%, respectively, and human metabolism with only about 4.9%. The annual mean AHF value of the Beijing–Tianjin–Hebei region is about 6.07 W·m⁻², and the AHF in urban areas is about in the range of 20 W·m⁻² and 130 W·m⁻². The maximum AHF value is approximately 130.84 W·m⁻², mostly in airports, railway stations, central business districts, and other densely-populated areas. The error analysis of the county-scale AHF results showed that the residual between the model estimation and energy consumption statistics is less than 1%. In addition, the spatial distribution of RAHF results is generally centered on urban area and gradually decreases towards suburbs. The spatial pattern of the RAHF results within urban areas corresponds well to the distribution of population density, building density, and the industrial district. The spatial heterogeneity of AHF within urban areas is well-reflected through the RAHF results. The RAHF results can be used in meteorological and environmental modeling for the Beijing–Tianjin–Hebei region. The results of this study also highlight the superiority of Suomi-NPP VIIRS NTL data for AHF estimation.

Land surface temperature (LST) is a crucial parameter in the interaction between the ground and the atmosphere. The Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) provides global daily coverage of day and night observation in the wavelength range of 0.55 to 12.0 μm. LST retrieved from SLSTR is expected to be widely used in different fields of earth surface monitoring. This study aimed to develop a split-window (SW) algorithm to estimate LST from two-channel thermal infrared (TIR) and one-channel middle infrared (MIR) images of SLSTR observation. On the basis of the conventional SW algorithm, using two TIR channels for the daytime observation, the MIR data, with a higher atmospheric transmittance and a lower sensitivity to land surface emissivity, were further used to develop a modified SW algorithm for the nighttime observation. To improve the retrieval accuracy, the algorithm coefficients were obtained in different subranges, according to the view zenith angle, column water vapor, and brightness temperature. The proposed algorithm can theoretically estimate LST with an error lower than 1 K on average. The algorithm was applied to northern China and southern UK, and the retrieved LST captured the surface features for both daytime and nighttime. Finally, ground validation was conducted over seven sites (four in the USA and three in China). Results showed that LST could be estimated with an error mostly within 1.5 to 2.5 K from the algorithm, and the error of the nighttime algorithm involved with MIR data was about 0.5 K lower than the daytime algorithm.

Anthropogenic heat (AH) generated by human activities has a major impact on urban and regional climate. Accurately estimating anthropogenic heat is of great significance for studies on urban thermal environment and climate change. In this study, a gridded anthropogenic heat flux (AHF) estimation scheme was constructed based on socio-economic data, energy-consumption data, and multi-source remote sensing data using a partition modeling method, which takes into account the regional characteristics of AH emission caused by the differences in regional development levels. The refined AHF mapping in China was realized with a high resolution of 500 m. The results show that the spatial distribution of AHF has obvious regional characteristics in China. Compared with the AHF in provinces, the AHF in Shanghai is the highest which reaches 12.56 W·m⁻², followed by Tianjin, Beijing, and Jiangsu. The AHF values are 5.92 W·m⁻², 3.35 W·m⁻², and 3.10 W·m⁻², respectively. As can be seen from the mapping results of refined AHF, the high-value AHF aggregation areas are mainly distributed in north China, east China, and south China. The high-value AHF in urban areas is concentrated in 50–200 W·m⁻², and maximum AHF in Shenzhen urban center reaches 267 W·m⁻². Further, compared with other high resolution AHF products, it can be found that the AHF results in this study have higher spatial heterogeneity, which can better characterize the emission characteristics of AHF in the region. The spatial pattern of the AHF estimation results correspond to the distribution of building density, population, and industry zone. The high-value AHF areas are mainly distributed in airports, railway stations, industry areas, and commercial centers. It can thus be seen that the AHF estimation models constructed by the partition modeling method can well realize the estimation of large-scale AHF and the results can effectively express the detailed spatial distribution of AHF in local areas. These results can provide technical ideas and data support for studies on surface energy balance and urban climate change.

On the basis of the spectral shape of thermal infrared (TIR) emissivity for sandy land, a remote sensing sand index called the sand differential emissivity index (SDEI) is proposed in this article to simply and conveniently detect sandy land over large areas. The SDEI is evaluated on ground, airborne, and spaceborne thermal emissivity data, and it shows good characterization of sandy land and performs better in sandy land identification than two previous indices. The SDEI was also evaluated in the transition zones of China's four mega-sandy lands and was applied to long-term land surface emissivity to obtain the spatial distribution and variation in China's sandy land from 2000 to 2016. The findings showed that a mean accuracy of 96% and a mean kappa coefficient of 0.83 were obtained in the transition zones, and the sandy land in the transition zone exhibited a decreasing trend over the past 17 years and a significant decline in the Mu Us sandy land. Meanwhile, the sandy land area in China decreased by 3.6 x 10⁴ km² (1.53%) by the end of 2016 compared with that in early 2000.