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This study proposes an improved temperature and emissivity separation (TES) algorithm for simultaneously retrieving the land surface temperature and emissivity (LST&E) from the Advanced Himawari Imager (AHI) data, including a modified water vapor scaling (WVS) method and a calibrated empirical relationship over vegetated surfaces. The modified WVS algorithm is comparable to the original WVS algorithm in deriving the LST&E but expands the application scope of the original WVS algorithm. The calibrated empirical relationship improved the LST&E and retrieval accuracy over vegetated surfaces by up to 0.165 K and 0.004, respectively. Comprehensive validation and evaluation are conducted in this study. In situ measurements from three networks are collected for the temperature-based validation. The bias and RMSE are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. Radiance-based LST validation shows that the bias and RMSE are 0.25 and 1.88 K, respectively. In addition, the AHI LST is evaluated using the MYD11 LST over large inland lakes, and the bias and RMSE are 0.25 and 1.12 K, respectively. The AHI LST is also compared to the MYD21 LST. The spatial distributions of the two LSTs are similar, and the LST differences are mostly within 4 K. The bias of the AHI LST ranges from −0.57 to 0.36 K, and the RMSE ranges from 1.7 to 2.64 K. The retrieved AHI LSE is compared with the latest MYD21 LSE. The biases and RMSEs are smaller than 0.005 and 0.014, respectively, for the three AHI bands. The improved TES algorithm is proven to be capable of obtaining accurate LST and LSE from AHI data.