Paper Info
Title
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.
Year
DOI
Venue
2016
10.1109/IGARSS.2016.7729687
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
Keywords
Field
DocType
ASTER GED, HJ-1B, land surface temperature, land surface emissivity
Atmospheric correction,Aster (genus),Parametric model,Drainage basin,Computer science,Remote sensing,Atmospheric model,Watershed,Radiometry,Emissivity
Conference
ISSN
Citations 
PageRank 
2153-6996
0
0.34
References 
Authors
4
6
Name
Order
Citations
PageRank
hua li1269.11
Tian Hu222.07
Xiangchen Meng382.54
Yongming Du47219.60
Biao Cao5142.43
Qinhuo Liu628085.97