This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based on atmospheric water vapor sub-ranges, which were obtained through a modified split-window covariance-variance ratio method. The channel emissivities were acquired from newly released global land cover products at 30 m and from a fraction of the vegetation cover calculated from visible and near-infrared images aboard Landsat 8. Simulation results showed that the new algorithm can obtain LST with an accuracy of better than 1.0 K. The model consistency to the noise of the brightness temperature, emissivity and water vapor was conducted, which indicated the robustness of the new algorithm in LST retrieval. Furthermore, based on comparisons, the new algorithm performed better than the existing algorithms in retrieving LST from TIRS data. Finally, the SW algorithm was proven to be reliable through application in different regions. To further confirm the credibility of the SW algorithm, the LST will be validated in the future.
Atmospheric water vapor (wv) is required for the accurate retrieval of the land surface temperature from remote sensing data and other applications. This work aims to estimate wv from Landsat 8 Thermal InfraRed Sensor (TIRS) images using a new modified split‐window covariance‐variance ratio (MSWCVR) method on the basis of the brightness temperatures of two thermal infrared bands. Results show that the MSWCVR method can theoretically retrieve wv with an accuracy better than 0.3 g/cm2 for dry atmosphere (wv <2 g/cm2) conditions and better than 0.5 g/cm2 for wet atmosphere conditions. The method was applied at different locations with dry and moist atmospheres and was validated at 42 ground sites using AERONET (Aerosol Robotic Network) ground‐measured data and MODIS (Moderate Resolution Imaging Spectroradiometer) products. The results show that the retrieved wv from the TIRS data is highly correlated with the wv of AERONET and MODIS but is generally larger. This difference was probably attributed to the uncertainty of radiometric calibration and stray light coming outside from field of view of TIRS instrument in the current images. Consequently, the data quality and radiometric calibration of the TIRS data should be improved in the future.
Land surface emissivity is a crucial parameter for obtaining the land surface temperature and estimating the land surface energy budget from remote sensing data. The current emissivity products always have a coarser spatial resolution than the products from the visible and near‐infrared data. This study focused on the generation of an emissivity product at a spatial resolution of 30 m using a new global land cover product called Finer‐Resolution Observation and Monitoring of Global Land Cover and Landsat images. Summer‐average emissivity products in four narrowbands (Landsat 5/Thematic Mapper Band 6, Landsat 7/Enhanced Thematic Mapper Plus Band 6, and Landsat 8 Thermal Infrared Sensor bands 1 and 2) and two broadbands (3–14 μm and 8–13.5 μm) were produced in China. Results illustrated that the narrowband emissivities ranged from 0.95 to 0.99, whereas the broadband emissivities ranged from 0.93 to 0.99 in the study area. Intercomparisons in different places showed that the new emissivity was close to Advanced Spaceborne Thermal Emission and Reflection Radiometer emissivity with a difference of about 0.015 for narrowband emissivity and about 0.02 for broadband emissivity on a regional scale. For application purposes, the emissivities were released in the Worldwide Reference System 2 and geographic coordinate systems with several spatial resolutions resampled from its original scale of 30 m.
This study performed an on-orbit evaluation of noise level for the Operational Land Imager (OLI) onboard Landsat 8 using early images over ground homogeneous sites. The signal-to-noise ratios (SNR) were higher than 160 of OLI nine bands at typical radiance level, while the noise equivalent radiance difference (NE∆L) and the noise equivalent reflectance difference (NE∆ρ) were respectively lower than 0.8 W/m(2)/µm/sr and 0.002. Compared to pre-launch predictions, the on-orbit low noise and high SNR almost satisfied requirements for OLI bands, and can provide a prior knowledge for uncertainty analysis of OLI images in monitoring land surface, oceanic, and atmospheric status.
Abstract:The radiometric performance of remotely-sensed images is important for the applications of such data in monitoring land surface, ocean and atmospheric status. One requirement placed on the Thermal Infrared Sensor (TIRS) onboard Landsat 8 was that the noise-equivalent change in temperature (NEΔT) should be ≤0.4 K at 300 K for its two thermal infrared bands. In order to optimize the use of TIRS data, this study investigated the on-orbit NEΔT of the TIRS two bands from a scene-based method using clear-sky images over uniform ground surfaces, including lake, deep ocean, snow, desert and Gobi, as well as dense vegetation. Results showed that the NEΔTs of the two bands were 0.051 and 0.06 K at 300 K, which exceeded the design specification by an order of magnitude. The effect of NEΔT on the land surface temperature (LST) retrieval using a split window algorithm was discussed, and the estimated NEΔT could contribute only 3.5% to the final LST error in
OPEN ACCESSRemote Sens. 2014, 6 12777 theory, whereas the required NEΔT could contribute up to 26.4%. Low NEΔT could improve the application of TIRS images. However, efforts are needed in the future to remove the effects of unwanted stray light that appears in the current TIRS images.
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