Inversion and Validation of FY-4A Official Land Surface Temperature Product

The thermal infrared data of Fengyun 4A (FY-4A) geostationary meteorological satellite can be used to retrieve hourly land surface temperature (LST). In this paper, seven candidate algorithms are compared and evaluated. The Ulivieri (1985) algorithm is determined to be optimal for the algorithm of FY-4A LST official products. The refined algorithm coefficients for distinguishing dry and moist atmosphere were established for daytime and nighttime, respectively. Then, FY-4A LST official products under clear-sky conditions are produced. The validation results show that: (1) Compared with in-situ measured LST data at the HeBi crop measurement network, the root mean square errors (RMSE) were 2.139 and 2.447 K. Compared with in-situ measured LST data at Naqu alpine meadow site of Tibet plateau, the RMSE was 2.86 K. (2) When compared with the MODIS LST product, the RMSE was 1.64, 2.17, 2.6, and 1.73 K in March, July, October, and December, respectively. By the bias long-time change at a single site, RMSE of the XLHT (city) and GZH (desert) sites were 2.735 and 2.97 K, respectively. Overall, the preferred algorithm exhibits good accuracy and meets the required accuracy of the FY-4A mission.

Automated summary

This paper utilizes the thermal infrared data from the FY-4A geostationary meteorological satellite to retrieve hourly land surface temperature (LST) and evaluates seven different algorithms. The Ulivieri (1985) algorithm is determined to be the most optimal for the FY-4A LST official products. Refined coefficients for distinguishing between dry and moist atmospheres are established, and the official LST products are successfully produced under clear-sky conditions. Validation results demonstrate that the preferred algorithm exhibits good accuracy and meets the required precision for the FY-4A mission.