Retrieval of cloudy land surface temperature from AMSR2 brightness temperature, precipitable water vapour and cloud liquid water

Abstract

ABSTRACT Land surface temperature (LST) is an important parameter in many studies including hydrology, meteorology, and ecology. In addition to thermal infrared remote sensing, passive microwave remote sensing is an effective alternative for LST retrieval, particularly under cloudy conditions. In this study, we analysed the impact of cloud on the transfer of microwave radiation in simulated database and presented a parameterization method to compensate the impact. On this basis, we put forward an improved method for LST retrieval from Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature at the 18.7 and 23.8 GHz channels with precipitable water vapour (PWV) and cloud liquid water (CLW) as supplementary input. The method is built on the basis of microwave radiative transfer equation and the assumption that 1) surface emissivity at two adjacent channels is linear and 2) the main factor causing brightness temperature difference at two channels is the absorption difference of PWV and CLW. The LST retrieval method was tested in simulated database, with root mean square error (RMSE) of 3.1 K. The method was then applied to AMSR2 brightness temperature and GFS reanalysis data to estimate global LST in 2019–2020, and the accuracy was validated using in situ LST measurements collected from 52 sites. The RMSE is approximately 4.7 K and 3.7 K for daytime and night-time, respectively. The improved LST retrieval method is promising in long-temporal and large-area applications.