Improving HJ-1B IRS land surface temperature product using ASTER Global Emissivity Dataset

Abstract

In this study, a single-channel parametric model (SC-PM) algorithm were used to produce 300m LST product from HJ-1B IRS data. The NCEP atmospheric profiles and a parametric model were used for atmospheric correction. In order to improve the accuracy of the land surface emissivity (LSE), the 1km ASTER Global Emissivity Dataset (GED) and self-developed 5-day 1km vegetation cover product were used for estimating the LSE based on the Vegetation Cover Method. Two years of HJ-1B IRS LST product in Heihe River basin (Gansu province, China) from June 2012 to June 2014 were generated. The LST products were evaluated against ground observations collected during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment. Four barren surface sites and ten vegetated sites were chosen for the evaluation. The results show that the produced HJ-1B IRS LST products demonstrate a good accuracy, with an average bias of 0.10 K and an average root mean square error (RMSE) of 2.43 K for all the sites during daytime. In addition, the biases are within 1K for the four barren surface sites. This indicate that using ASTER GED can produce reliable LST products from HJ-1B IRS data, especially for the barren surfaces.