An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

Abstract

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 OPEN ACCESSRemote Sens. 2015, 7 4269 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.

Analysis of Relationship Between Urban Heat Island Effect and Land Use/Cover Type Using Landsat 7 Etm+ and Landsat 8 Oli Images

“…Landsat satellite series are the data that are most widely used for these studies. However, Landsat 8 OLI/TIRS satellite was launched in 2013 and therefore, there are limited number of UHI studies in the literature that use this satellite images (Jimenez-Munoz et al, 2014;Jin et al, 2015;Rozenstein et al, 2014;Sekertekin et al, 2016;Wang et al, 2015;Yu et al, 2014). In the study conducted by Yu et al (2014), three different methods were compared for LST extraction from Landsat 8 OLI/TIRS thermal bands which are the single channel (SC) method, the split window (SW) algorithm and the radiative transfer equationbased method.…”

Temperature Vegetation Dryness Index Estimation of Soil Moisture under Different Tree Species

“…Many researchers have validated the algorithm and have shown that the algorithm provides an RMSD value of 0.9-1.1 K [28][29][30]. Thus, this study uses this algorithm to obtain the Ts.…”

Urban Built-Up Area Boundary Extraction and Spatial-Temporal Characteristics Based on Land Surface Temperature Retrieval

“…For Landsat 8 images (TIRS), the spectral radiance computed from the DN value has the following relation with its DN value [22]:…”

“…Combined with the algorithm proposed by Wang et al [22], the parameter values were assigned using the equation proposed by Sobrino et al [38]. Thus, for water bodies, ε w = 0.991; for bare soil, ε s = 0.966.…”