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In this study, we employed the non-uniform rational B-splines surface technique to develop a provisional horizontal wind model of the mesosphere and lower thermosphere at altitudes of 87, 97, and 250 km over Kelan (38.7°N, 111.6°E) in China using Fabry–Perot interferometer (FPI) observations for the period between January 2012 and May 2014. We compared the meridional and zonal winds derived from the Kelan FPI wind model with those based on Meteor Radar wind observations at Beijing (39.92°N, 116.39°E), and those calculated from horizontal wind models, i.e., HWM93 and HWM07, at Kelan. Our analysis showed the following. (1) At 87 and 97 km, the Kelan FPI wind patterns exhibited strong annual variation in the meridional wind and semiannual variation in the zonal wind, as well as diurnal variation, which was dominated by a semidiurnal tidal variation in the meridional and zonal winds during May and September with a diurnal phase difference. The Kelan FPI wind patterns agreed well with the Meteor Radar winds and the HWM07 model results, but less well with the HWM93 model. (2) At 250 km, the FPI wind patterns agreed fairly well with the HWM93 results, where both indicated annual/diurnal variation in the meridional and zonal winds, and weak semidiurnal tidal variation. There were large discrepancies between the Kelan FPI winds and the HWM07 model results, which were mainly due to differences in the annual variation in the nighttime meridional wind. (3) The magnitude of the FPI winds was comparable to the HWM93 results but they were nearly 40–50% lower than the other winds, which is attributable to the inherent limitations of airglow measurement. Thus, this local empirical wind model could be used to investigate the climatological features of local ionospheric spatial–temporal variations. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) plays a crucial role in characterizing land surface processes and the energy balance. Accurate monitoring of spatial–temporal variations in LST holds great significance for global and regional climate change research. However, with the increasing of multi-source remote sensing data, it has become a challenging task to construct an LST model that reduces dependence on auxiliary data (such as land surface emissivity (LSE) and atmospheric water vapor content (WVC) synchronized with satellite observations. This study proposed an LST retrieval model based on random forest model (RFLSTR). The results of 10-fold cross-validation showed that the RFLSTR model had high modelling accuracy, the root mean square error (RMSE) and mean absolute error (MAE) were lower than 2 K. Landsat 8 images from the Beijing area and South Korea, were utilized to assess the spatial–temporal migration performance of the RFLSTR model. Additionally, Landsat 9 images from South Korea were employed to analyse the sensor migration performance of the RFLSTR model. In conclusion, the findings highlight that the proposed RFLSTR model achieve high retrieval accuracy without relying on auxiliary data (such as LSE and WVC) synchronized with satellite observations. Moreover, the RFLSTR model exhibits robust spatial–temporal capabilities, enhancing the efficiency of LST retrieval and facilitating the utilization of satellite data. [ABSTRACT FROM AUTHOR]