Advancing of Land Surface Temperature Retrieval Using Extreme Learning Machine and Spatio-Temporal Adaptive Data Fusion Algorithm

Yang Bai,Kai Qin,Man Wong,Wen-Zhong Shi,Li-Xin Wu

doi:10.3390/rs70404424

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.

Highlights

In order to monitor the rapid and continual changes of the global environment, Land SurfaceTemperature (LST), as the prime and basic physical parameter of the earth’s surface, has been studied for over a decade
The developed fusion methodology was applied to three pairs of Landsat ETM+ and MODerate resolution Imaging Spectroradiometer (MODIS) images both in testing and simulated experiments (Figure 4)
The original multispectral images and Thermal Infra-Red (TIR) image of Landsat ETM+ were downscaled to 120 m using the pixel averaging method, the extreme learning machine (ELM) algorithm was employed to enhance the spatial resolution of the degraded TIR image from 120 m to 60 m, and the original TIR image at 60 m was used as the referenced data for validation

Summary

Introduction

In order to monitor the rapid and continual changes of the global environment, Land SurfaceTemperature (LST), as the prime and basic physical parameter of the earth’s surface, has been studied for over a decade. Compared with traditional methods using data from weather stations, remote sensing satellite images provide a more effective and efficient method to estimate LST and offer a synoptic view of the study area. Due to the limitations on both spatial and temporal resolution of the existing satellite sensors, there is no earth observation which can obtain Thermal. Infra-Red images (TIR) at detailed spatial- and temporal-resolution simultaneously. The applications of thermal infrared remote sensing in urban environment studies require heat-related information at high spatial resolution, as well as high temporal resolution [7]. In order to collect more reflected and emitted signal from the earth, large spatial coverage, e.g., lower spatial resolution from the earth observation is required.

Methods

Results

Discussion

Conclusion