Abstract
Thermal infrared (TIR) satellite images are generally employed to retrieve land surface temperature (LST) data in remote sensing. LST data have been widely used in evapotranspiration (ET) estimation based on satellite observations over broad regions, as well as the surface dryness associated with vegetation index. Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) can provide LST data with a 30-m spatial resolution. However, rapid changes in environmental factors, such as temperature, humidity, wind speed, and soil moisture, will affect the dynamics of ET. Therefore, ET estimation needs a high temporal resolution as well as a high spatial resolution for daily, diurnal, or even hourly analysis. A challenge with satellite observations is that higher-spatial-resolution sensors have a lower temporal resolution, and vice versa. Previous studies solved this limitation by developing a spatial and temporal adaptive reflectance fusion model (STARFM) for visible images. In this study, with the primary mechanism (thermal emission) of TIRS, surface emissivity is used in the proposed spatial and temporal adaptive emissivity fusion model (STAEFM) as a modification of the original STARFM for fusing TIR images instead of reflectance. For high a temporal resolution, the advanced Himawari imager (AHI) onboard the Himawari-8 satellite is explored. Thus, Landsat-like TIR images with a 10-minute temporal resolution can be synthesized by fusing TIR images of Himawari-8 AHI and Landsat-8 TIRS. The performance of the STAEFM to retrieve LST was compared with the STARFM and enhanced STARFM (ESTARFM) based on the similarity to the observed Landsat image and differences with air temperature. The peak signal-to-noise ratio (PSNR) value of the STAEFM image is more than 42 dB, while the values for STARFM and ESTARFM images are around 31 and 38 dB, respectively. The differences of LST and air temperature data collected from five meteorological stations are 1.53 °C to 4.93 °C, which are smaller compared with STARFM’s and ESATRFM’s. The examination of the case study showed reasonable results of hourly LST, dryness index, and ET retrieval, indicating significant potential for the proposed STAEFM to provide very-high-spatiotemporal-resolution (30 m every 10 min) TIR images for surface dryness and ET monitoring.
Highlights
Soil moisture and vegetation are key parameters in land–atmosphere interactions [1,2]
The fused images of enhanced STARFM (ESTARFM) show differences in the spatial structure compared to Landsat Thermal infrared (TIR), which might be caused by significant differences in surface radiance between tR and tRA
Surface emissivity (LSE) plays a key role in the emission of energy as thermal radiation, which is essential to the procedure of TIR image fusion
Summary
Soil moisture and vegetation are key parameters in land–atmosphere interactions [1,2]. As the sum of soil evaporation and canopy transpiration, evapotranspiration (ET) plays a significant role in determining the exchanges of energy and mass between the hydrosphere, atmosphere, and biosphere [3]. Actual ET can be measured directly using lysimeters [5] and eddy covariance flux towers [6] at the individual plant and local scale, respectively. Since an in situ measurement station is limited in spatial coverage and considered relatively expensive, remote sensing data have been used for ET estimation on the local, regional, or even global scale. ET estimation using remote sensing data is important and has been increasingly studied in order to expand the information that an in situ measurement station cannot provide
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