Zhang, XD; Zhou, J; Liang, SL; Chai, LN; Wang, DD; Liu, J (2020). Estimation of 1-km all-weather remotely sensed land surface temperature based on reconstructed spatial-seamless satellite passive microwave brightness temperature and thermal infrared data. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 167, 321-344.

All-weather remotely sensed land surface temperature (LST) with a 1-km resolution from combined satellite passive microwave (MW) and thermal infrared (TIR) remote sensing data has been urgently needed during the past decades. However, due to considerable temporal gap between AMSR-E and AMSR2 observation from November 2011 to May 2012, current MODIS-AMSR-E/2 integrated LST is not really all-weather available. Therefore, an AMSR-E/2-like brightness temperature (BT) without the temporal gap for 2011-2012 is highly desirable. Despite the Chinese Fengyun-3B MWRI BT is qualified to reconstruct such a BT, the swath gap in its BT has to be effectively filled as the gap not only greatly decreases the spatiotemporal coverage of the TIR-MW integrated LST but also limits the application of satellite MW BT data. However, the gap issue has not been effectively addressed by previous research. In this context, this study proposes an novel method to (i) reconstruct a spatial-seamless (i.e. without the two gaps) AMSR-E/2-like MW BT based on MWRI data for 2011-2012 over the Tibetan Plateau and (ii) estimate a realistic 1-km all-weather LST by integrating reconstructed MW BT with Aqua-MODIS data. Results show that the reconstructed MW BT is spatiotemporally continuous and has a high accuracy with a root-mean-square error (RMSE) of 0.89-2.61 K compared to original AMSR-E/2 BT. This exhibits the method's potential to greatly extend the spatiotemporal coverage of currently available MW BT-based remote sensing data. In addition, the estimated LST has an RMSE of 1.45-3.36 K when validated against the ground measurements, which outperforms current TIR-MW integrated LST products. Therefore, this study would be valuable for facilitating satellite MW data and generating a realistic and reliable 1-km all-weather remotely sensed LST at large scales.