An operational split-window algorithm for retrieving land surface temperature from FengYun-4A AGRI data

The National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Enterprise algorithm with explicit path length correction (named as the adapted enterprise algorithm) was employed to retrieve land surface temperature (LST) from FengYun-4A (FY-4A)/Advanced Geostationary Radiation Imager (AGRI) thermal infrared (TIR) data. Three land surface emissivity (LSE) datasets, i.e., the daily LSE composited from the Essential thermaL Infrared remoTe sEnsing (ELITE) hourly emissivity product, the LSE retrieved by the vegetation cover method (VCM) and the AGRI official LSE, were used in the adapted enterprise algorithm. Validation results show that the accuracy of the retrieved AGRI LST using the ELITE LSE is better than that using the VCM-retrieved LSE and official LSE, with an overall bias, mean absolute error (MAE), and root mean square error (RMSE) of 0.06, 1.94, and 2.55 K, respectively. This study demonstrates that the split-window algorithm with explicit path length correction can improve the accuracy of LST retrieval.

Automated summary

The adapted enterprise algorithm was used to retrieve land surface temperature from FY-4A thermal infrared data, and it was found that using the daily composite of emissivity data resulted in better accuracy. This study demonstrates the effectiveness of the algorithm.