Improving HJ-1B IRS land surface temperature product using ASTER global emissivity database

Abstract:

Land surface temperature (LST) is a key parameter for hydrological, meteorological, climatological and environmental studies. Currently many operational LST products have been generated using European and American satellite data, i.e., the Advanced Very High Resolution Radiometer (AVHRR), Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS). However, few LST product has been produced using Chinese satellite data. Thus, the objective of this study is to generate reliable LST product using Chinese HJ-1B satellite data.

The HJ-1B satellite of China, were launched on September 6, 2008, which are used for disaster and environment monitoring. IRS (Infrared Scanner) is one of the key instruments onboard HJ-1B satellite, it can scan the earth every four days, has four spectral bands ranging from the near-infrared to thermal infrared bands (band 1 0.75 - 1.10μm, band 2 1.55-1.75μm, MIR band 3 3.50 - 3.90μm, band 4 10.5-12.5μm) with 720 km swath. It scans ±29° from nadir and the spatial resolution for band1-3 is 150m and 300m for band4.

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 Database (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 in an arid area of northwest China 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 developed HJ-1B IRS LST products demonstrate a good accuracy, with an average bias of 0.11 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 all the barren surface sites, this indicate that using ASTER GED can produce reliable LST products from HJ-1B IRS data, especially for the barren surfaces.