Abstract
The successful launch of the Landsat 8 satellite with two thermal infrared bands on February 11, 2013, for continuous Earth observation provided another opportunity for remote sensing of land surface temperature (LST). However, calibration notices issued by the United States Geological Survey (USGS) indicated that data from the Landsat 8 Thermal Infrared Sensor (TIRS) Band 11 have large uncertainty and suggested using TIRS Band 10 data as a single spectral band for LST estimation. In this study, we presented an improved mono-window (IMW) algorithm for LST retrieval from the Landsat 8 TIRS Band 10 data. Three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature) were required for the IMW algorithm to retrieve LST. A new method was proposed to estimate the parameter of effective mean atmospheric temperature from local meteorological data. The other two essential parameters could be both estimated through the so-called land cover approach. Sensitivity analysis conducted for the IMW algorithm revealed that the possible error in estimating the required atmospheric water vapor content has the most significant impact on the probable LST estimation error. Under moderate errors in both water vapor content and ground emissivity, the algorithm had an accuracy of ~1.4 K for LST retrieval. Validation of the IMW algorithm using the simulated datasets for various situations indicated that the LST difference between the retrieved and the simulated ones was 0.67 K on average, with an RMSE of 0.43 K. Comparison of our IMW algorithm with the single-channel (SC) algorithm for three main atmosphere profiles indicated that the average error and RMSE of the IMW algorithm were −0.05 K and 0.84 K, respectively, which were less than the −2.86 K and 1.05 K of the SC algorithm. Application of the IMW algorithm to Nanjing and its vicinity in east China resulted in a reasonable LST estimation for the region. Spatial variation of the extremely hot weather, a frequently-occurring phenomenon of an abnormal heat flux process in summer along the Yangtze River Basin, had been thoroughly analyzed. This successful application suggested that the IMW algorithm presented in the study could be used as an efficient method for LST retrieval from the Landsat 8 TIRS Band 10 data.
Highlights
Land surface temperature (LST) is a very important parameter in the surface process of land-air interaction [1,2]
Where Ts is the LST in Kelvin degrees; Ta is the effective mean atmospheric temperature; T6 is the brightness temperature of Landsat 5 Thematic Mapper (TM) Band 6; a6 and b6 are the coefficients used to approximate the derivative of the Planck radiance function for the thermal band; and C6 and D6 are the internal parameters for the algorithm based on the atmospheric parameters and ground emissivity
Where Ts is the LST retrieved from the Landsat 8 Thermal Infrared Sensor (TIRS) Band 10 data; Ta is the effective mean atmospheric temperature; T10 is the brightness temperature of Landsat 8 TIRS Band 10; a10 and b10 are the constants used to approximate the derivative of the Planck radiance function for the TIRS Band 10 given in Table 1; C10 and D10 are the internal parameters for the algorithm, given as follows: C10 = τ10ε10 (4)
Summary
Land surface temperature (LST) is a very important parameter in the surface process of land-air interaction [1,2]. Qin et al [16] requires three essential parameters for LST retrieval from the one TIR band data of Landsat TM/ETM+: ground emissivity, atmospheric transmittance and effective mean atmospheric temperature. The SC algorithm had been updated and extended to Landsat 4 and Landsat 7 band data in Jiménez-Muñoz et al [19], in which the feasibility had been analyzed through look-up tables (LUTs) to avoid the problem relevant to uncertainties introduced from fitting the atmospheric parameters to water vapor content for their estimation. Both the mono-window algorithm and single-channel algorithm require atmospheric profiles to model the radiative transfer process. We present an example of applying the algorithm to Nanjing and its vicinity in east China for LST retrieval
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