Retrievals of all-weather daytime land surface temperature from FengYun-2D data

Land surface temperature (LST) is a key parameter in the interaction of the land-atmosphere system. Nevertheless, on the regional scale, the actual weather is cloudy for half a year in most regions. Therefore, receiving all-weather LST from thermal-infrared remote sensing is necessary and urgent. In this paper, an approach with multi-temporal and spatial neighboring-pixels in combination with diurnal solar radiation and surface temperature evolution is proposed to estimate daytime all-weather LST using FY-2D data. Evaluation of the accuracy of the algorithm is performed against the simulated data and the in situ measurements. The root mean square error (RMSE) between the actual and estimated LSTs under cloud-free conditions is approximately 1.84 K for the simulated data, while the RMSE of LST under cloud-free conditions varies from 3.42 to 5.1 K for the in situ measurement, and RMSE of LST under cloudy sky is approximately 7 K. The results indicate that the new algorithm is practical for retrieving the daytime all-weather LST at high-temporal resolution without any auxiliary field measurement, although some uncertainties exist.