Comparison of Nighttime Land Surface Temperature Retrieval Using Mid-Infrared and Thermal Infrared Remote Sensing Data Under Different Atmospheric Water Vapor Conditions

Thermal infrared (TIR) remote sensing is an important technological tool for observing large-scale land surface thermal radiance and can obtain the spatially continuous land surface temperature (LST), a critical land surface physical parameter of great interest in several fields. After decades of development, various LST retrieval algorithms have been proposed. However, current studies indicated that the commonly used retrieval algorithms show a decrease in the accuracy of the results under humid atmospheric conditions, and the theoretical analysis of the phenomenon needs to be developed. This study derives the LST error as a function of atmospheric parameters (transmittance, upward radiance, and downward radiance) directly based on the TIR radiative transfer equation. Compared with the TIR channel, the mid-infrared (MIR) channel has less water vapor absorption, is more insensitive to water vapor, and has a larger transmittance, which is expected to improve the accuracy of LST retrieval under humid atmospheric conditions. In this study, a typical simulation dataset under various atmospheric and land surface conditions is constructed based on the MIR channels of moderate resolution imaging spectroradiometer (MODIS) remote sensing data. Analysis of the retrieval results based on the simulation datasets shows that the MIR channels have more minor errors for the same level of atmospheric errors. With the growth of column water vapor (CWV), the error of the split-window (SW) algorithm constructed based on the TIR channel increases. In contrast, the accuracy of the algorithm developed by MIR channels is more stable, and the advantage of the accuracy in humid atmospheric conditions is more prominent. Two SW algorithms are applied to nighttime TIR and MIR remote sensing images observed by Aqua MODIS, and the validation results obtained based on surface radiation budget network (SURFRAD) ground sites also showed that the two MIR SW algorithms achieved the accuracy advantage of 0.425 K (SW1_TIR: 2.582 K/SW1_MIR: 2.157 K) and 0.525 K (SW2_TIR: 2.624 K/SW2_MIR: 2.099 K), which indicated that the MIR-SW algorithms can more accurately retrieve the LST under humid atmospheric conditions.