Inversion of Land Surface Temperature (LST) Using Terra ASTER Data: A Comparison of Three Algorithms

Abstract

Land Surface Temperature (LST) is an important measurement in studies related to the Earth surface's processes. The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) instrument onboard the Terra spacecraft is the currently available Thermal Infrared (TIR) imaging sensor with the highest spatial resolution. This study involves the comparison of LSTs inverted from the sensor using the Split Window Algorithm (SWA), the Single Channel Algorithm (SCA) and the Planck function. This study has used the National Oceanic and Atmospheric Administration's (NOAA) data to model and compare the results from the three algorithms. The data from the sensor have been processed by the Python programming language in a free and open source software package (QGIS) to enable users to make use of the algorithms. The study revealed that the three algorithms are suitable for LST inversion, whereby the Planck function showed the highest level of accuracy, the SWA had moderate level of accuracy and the SCA had the least accuracy. The algorithms produced results with Root Mean Square Errors (RMSE) of 2.29 K, 3.77 K and 2.88 K for the Planck function, the SCA and SWA respectively.

Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

“…In previous studies on SURFRAD stations [53,56,65], the broadband emissivity was computed as reported in [104,105] by regression from narrowband emissivity of MODIS thermal bands, which are available through the MODIS monthly emissivity data set. The results in [104,105] proved that the longwave broadband emissivity for the SURFRAD sites could be considered 0.97, as also assumed in [64] and [53].…”

“…Besides, ancillary observations include direct and diffuse solar radiation, ultraviolet-B radiation, and meteorological parameters. Since SURFRAD stations provide unique in-situ LST information over rural sites, many researchers have used these data to validate satellite-based LST retrievals [15,46,52,53,56,[64][65][66][67]. In this study, five SURFRAD stations were considered as ground-based stations, and Table 1 reports information regarding the SURFRAD experimental sites.…”

Surface Urban Heat Island in Middle City: Spatial and Temporal Characteristics

“…The calculation of the SUHI was performed in six steps ( Figure 2), as follows: (i) was to convert the digital numbers into radiance; (ii) to calculate brightness temperature; (iii) to calculate the NDVI (Normalized Difference Vegetation Index); (iv) to estimate the surface emissivity using the values obtained by the NDVI; (v) after estimating emissivity, to determine the corrected surface temperature, which was later transformed into SUHI in the sixth step, as suggested by Ndossi and Avdan [47,48].…”

“…[46]. LANDSAT satellite sensors have been monitoring the Earth for over four decades, providing data continuity during this period [47,48]. The first series of this satellite was launched in 1972, named LANDSAT 1.…”

A Split Window Algorithm for Retrieving Land Surface Temperature from FY-3D MERSI-2 Data

“…Specifically, the satellite has 15 visible and NIR bands (0.402-0.965 µm), four shortwave infrared bands (1.02/1.23-2.155 µm), two medium-wave infrared bands (3.71-4.1275 µm), one water vapor band (6.95-7.45 µm) and three longwave infrared bands (8.4-12.5 µm). In terms of product applications, channels 1-7 are mainly used for land and cloud boundaries; channels 8-15 are used for ocean color, plankton and biogeochemical remote sensing; channels 16,17,18,22, and 23 are used for atmospheric water vapor remote sensing; channel 19 is a cloud band; and channels 20, 21, 24, and 25 are used for land, water, and cloud temperature sensing. Table 1 shows the continuity and evolution of the MERSI to MERSI-2 bands and the comparison of MERSI-2 with MODIS, MERSI, and VIIRS.…”