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

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 OPEN ACCESSRemote Sens. 2015, 7 4425 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.

An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping

“…Figure 3 and Table 5 show that the retrieved LEs agreed well with local measurements, with an overall low root mean square error (RMSE) of 40.9 W/m 2 at all sites and the average retrieved LEs were slightly lower than the average observations; thus, the retrieved instantaneous ET is reliable as the input parameter for data fusion. , 1 1 1 (24) where T10 represents the brightness temperature at Landsat 8 band 10. The surface emissivity (ɛ) is estimated following an NDVI threshold method [52].…”

“…Data fusion methods use two or more images to obtain fine spatial resolution images; thus, they can simultaneously improve the spatial resolution and temporal coverage. To date, several data fusion methodologies, which were originally developed to fuse land surface reflectance and spectral index data, have been utilized to attempt the estimation of ET at a fine spatio-temporal resolution from ET at a high spatial resolution and high temporal resolution [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. These data fusion methods can be divided into two categories: (1) fuse intermediate variables to estimate ET and (2) fuse ET data.…”

Combining ad hoc spectral indices based on LANDSAT-8 OLI/TIRS sensor data for the detection of plastic cover vineyard

“…Satellite thermal infrared (TIR) data are commonly processed for retrieving land surface temperature (LST) (Bai et al 2015) and sea surface temperature (Tarantino 2012). Generally in-situ thermal data are necessary to study and assess growing conditions, air flow and evapotranspiration within greenhouses and to define experiment conditions (Villarreal-Guerrero et al 2012).…”

Combining Linear Pixel Unmixing and STARFM for Spatiotemporal Fusion of Gaofen-1 Wide Field of View Imagery and MODIS Imagery

“…The fusion of multi-source remote sensing data can result in more abundant and more accurate information than any single piece of remote sensing data. Studies have applied multi-sensor data fusion of medium-and high-resolution imagery for applications such as phenology analysis, management of wetlands, vegetation dynamics monitoring, land cover classification, and land surface temperature retrieval [2][3][4][5][6][7][8][9][10][11]. The first satellite of China's high-resolution Earth observation system, GaoFen-1 (GF-1), was successfully launched on 26 April 2013.…”