Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17515
Title: Advancing of land surface temperature retrieval using extreme learning machine and spatio-temporal adaptive data fusion algorithm
Authors: Bai, Y
Wong, MS 
Shi, WZ 
Wu, LX
Qin, K
Keywords: Extreme learning machine
Land surface temperature
Landsat
Modis
Spatial-temporal fusion
Thermal infrared images
Issue Date: 2015
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Remote sensing, 2015, v. 7, no. 4, p. 4424-4441 How to cite?
Journal: Remote sensing 
Abstract: As a critical variable to characterize the biophysical processes in ecological environment, and as a key indicator in the surface energy balance, evapotranspiration and urban heat islands, Land Surface Temperature (LST) retrieved from Thermal Infra-Red (TIR) images at both high temporal and spatial resolution is in urgent need. However, due to the limitations of the existing satellite sensors, there is no earth observation which can obtain TIR at detailed spatial- and temporal-resolution simultaneously. Thus, several attempts of image fusion by blending the TIR data from high temporal resolution sensor with data from high spatial resolution sensor have been studied. This paper presents a novel data fusion method by integrating image fusion and spatio-temporal fusion techniques, for deriving LST datasets at 30 m spatial resolution from daily MODIS image and Landsat ETM+ images. The Landsat ETM+ TIR data were firstly enhanced based on extreme learning machine (ELM) algorithm using neural network regression model, from 60 m to 30 m resolution. Then, the MODIS LST and enhanced Landsat ETM+ TIR data were fused by Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT) in order to derive high resolution synthetic data. The synthetic images were evaluated for both testing and simulated satellite images. The average difference (AD) and absolute average difference (AAD) are smaller than 1.7 K, where the correlation coefficient (CC) and root-mean-square error (RMSE) are 0.755 and 1.824, respectively, showing that the proposed method enhances the spatial resolution of the predicted LST images and preserves the spectral information at the same time.
URI: http://hdl.handle.net/10397/17515
EISSN: 2072-4292
DOI: 10.3390/rs70404424
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