Comparison of Three Methods for Estimating Land Surface Temperature from Landsat 8-TIRS Sensor Data

Abstract

After Landsat 8 was launched in 2013, it was observed that for Thermal Infrared sensor (TIRS) bands, radiance from outside of an instrument’s field-of-view produced a non-uniform ghost signal across the focal plane that varied depending on the out-of-scene content (i.e., the stray light effect). A new stray light correction algorithm (SLCA) is currently operational and has been implemented into the United States Geological Survey (USGS) ground system since February 2017. The SLCA has also been applied to reprocess historical Landsat 8 scenes. After approximately two years of SLCA implementation, more land surface temperature (LST) validation studies are required to check the effect of correction in the estimation of LST from different retrieval algorithms. For this purpose, three different LST estimation method algorithms (i.e., the radiative transfer equation (RTE), single-channel algorithm (SCA), and split-window algorithm (SWA)) have been assessed. The study site is located on the campus of the University of Balearic Islands on the island of Mallorca (Spain) in the western Mediterranean Sea. The site is considered a heterogeneous area that is composed of different types of surfaces, such as buildings, asphalt roads, farming areas, sloped terrains, orange fields, almond trees, lawns, and some natural vegetation regions. Data from 21 scenes, which were acquired by the Landsat 8-TIRS sensor and extracted from a 100 × 100 m2 pixel, were used to retrieve the LST with different algorithms; then, they were compared with in situ LST measurements from a broadband thermal infrared radiometer located on the same Landsat 8 pixel. The results show good performances of the three methods, with the SWA showing the lowest observed RMSE (within 1.6–2 K), whereas the SCA applied to the TIRS band 10 (10 µm) was also appropriate, with a RMSE ranging within 2.0–2.3 K. The LST estimates using the RTE algorithm display the highest observed RMSE values (within 2.0–3.6 K) of all of the compared methods, but with an almost unbiased value of −0.1 K for the case of techniques applied to band 10 data. The SWAs are the preferred method to estimate the LST in our study area. However, further validation studies around the world are required.

Mass Loss From Calving in Himalayan Proglacial Lakes

“…The measured positive temperature bias of satellite vs. in situ measurements is consistent with previous calibrations/validations of ASTER kinetic surface temperatures (Tonooka and Palluconi, 2005) and our own ASTER thermal validations at an iceberg-choked lake and on melting snow and glacier clean-ice fields (Ramachandran et al, 2014). Validations of Landsat 8 temperatures have involved radiometrically very different types of surfaces (urban buildings, asphalt and orchards for instance) but have also shown biases and RMSEs similar to those of ASTER (García-Santos et al, 2018) and have revealed algorithm-dependent and humidity-dependent biases. Li and Jiang (2018) found a negative bias of about −0.49 K between Landsat 8 temperatures (cooler) compared to MODIS MOD11, which is roughly consistent with the Landsat 8/ASTER bias since ASTER temperatures were partly calibrated using MODIS (Arai, 1996).…”

“…The warmest mean lake temperatures observed with ASTER and Landsat thermal imagery were 10.7 and 10.8 • C for Thulagi Lake on 29 September 2015 and 30 September 2016; 4.2 and 7.2 • C at Lower Barun Lake on 9 October 2004 and 30 September 2015 and 8.6 and 9.6 • C for Imja Lake on 9 October 2004. When interpreting Figure 4, we should recall the RMSE evaluated for Landsat 8 surface temperatures of + −1.6-2 K (García-Santos et al, 2018) and ASTER (about + −0.8 K, Ramachandran et al, 2014), and the temperature bias of + 0.8 K for ASTER (Ramachandran et al, 2014). Considering these errors, it may be seen that Thulagi Lake well exceeds the 4 • C density-maximum temperature of water, but for Lower Barun, it did not clearly ever exceed that temperature.…”

The Spatial Optimization and Evaluation of the Economic, Ecological, and Social Value of Urban Green Space in Shenzhen

“…In this study, the radiative transfer equation was used for Landsat 8 TIRS-10/11 to retrieve LSTs for Shenzhen. The main processes were defined with reference to Garcia-Santos [50]. The retrieved mean LSTs of Shenzhen on 1 and 23 October 2017 are displayed in Figure 3.…”

“…where P is housing rental prices or house prices, and 1 , 2 , …, are the factors that impact P. In Equation (1), f is the relationship between and P, where the linear model, semi-log model, and double-log model were in existing studies. The linear model was used [35,[49][50][51] in this study to evaluate the economic value of green spaces in Shenzhen.…”

Tree Water Status in Apple Orchards Measured by Means of Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from Landsat 8 Satellite Images

“…After analyzing the relevant sources of errors, the author showed that the split window method could be adopted with an accuracy of 3 • C. Among numerous applications of LST retrieval calculations, its application is still followed in estimating water status in the field [21]. In this regard, many studies have been carried out for developing and improving the LST retrieval methods [22][23][24][25][26] to estimate evapotranspiration. Moreover, the RS has been utilized for estimating vegetation indices (VI), such as the normalized difference vegetation index (NDVI) [27].…”