Study of land surface temperature and spectral emissivity using multi-sensor satellite data

Abstract

In this study, an attempt has been made to estimate land surface temperatures (LST) and spectral emissivities over a hard rock terrain using multi-sensor satellite data. The study area, of about 6000 km 2 , is a part of Singhbhum-Orissa craton situated in the eastern part of India. TIR data from ASTER, MODIS and Landsat ETM+ have been used in the present study. Telatemp Model AG-42D Portable Infrared Thermometer was used for ground measurements to validate the results derived from satellite (MODIS/ASTER) data. LSTs derived using Landsat ETM+ data of two different dates have been compared with the satellite data (ASTER and MODIS) of those two dates. Various techniques, viz., temperature and emissivity separation (TES) algorithm, gray body adjustment approach in TES algorithm, Split-Window algorithms and Single Channel algorithm along with NDVI based emissivity approach have been used. LSTs derived from bands 31 and 32 of MODIS data using Split-Window algorithms with higher viewing angle (50 • ) (LST1 and LST2) are found to have closer agreement with ground temperature measurements (ground LST) over waterbody, Dalma forest and Simlipal forest, than that derived from ASTER data (TES with AST 13). However, over agriculture land, there is some uncertainty and difference between the measured and the estimated LSTs for both validation dates for all the derived LSTs. LST obtained using Single Channel algorithm with NDVI based emissivity method in channel 13 of ASTER data has yielded closer agreement with ground measurements recorded over vegetation and mixed lands of low spectral contrast. LST results obtained with TIR band 6 of Landsat ETM+ using Single Channel algorithm show close agreement over Dalma forest, Simlipal forest and waterbody with LSTs obtained using MODIS and ASTER data for a different date. Comparison of LSTs shows good agreement with ground measurements in thermally homogeneous area. However, results in agriculture area with less homogeneity show difference of LST up to 2 • C. The results of the present study indicate that continuous monitoring of LST and emissivity can be undertaken with the aid of multi-sensor satellite data over a thermally homogeneous region.

A Software Tool for Atmospheric Correction and Surface Temperature Estimation of Landsat Infrared Thermal Data

“…An offset of the same order of magnitude must be associated with the French datasets, whether between one quarter and one third of the mean bias. For their comparison between ASTER, MODIS, Landsat 7, and ground measurements, Srivastava et al [47] established a similar mean error with a value of 2.3 • C. They also determined an RMSE of around 3 • C for the differences between Landsat and the two other datasets. Zhou et al [46] compared ground measurements with data from ASTER bands 13 and 14, which were corrected separately using several algorithms.…”

“…In contrast, the ground of the Tunisian site was mainly composed of sand that remained untouched over time and was planted with slow-growing olive trees. Srivastava [47] has shown that a good match of ground LST and satellite data can be obtained for homogeneous soil, whereas an error of ±2 • C was found for less homogeneous surfaces such as are often encountered in agricultural areas. …”

Thermal Infrared Remote Sensing of Hydrocarbon in Lagos- Southern Nigeria: Application of the Thermographic Model

“…Due to some anomalies reported for emissivity estimations by some researchers [10,14,15], surface emissivity is influenced by local climatic conditions, land cover, seasons e.t.c. We assume that the ≈ so as to accommodate the anomalies majorly influenced via .…”

Land Surface Temperature Extracts for Peri-Urban Heat and Rural Cool Troughs in Ghana

“…The reflectance and emission properties of land surface features also determine the albedo that defines the percentage reflectance of solar energy from the earth surface (Ahrens, 2005). As a proxy for calculating the degree of hotness or coldness of the land surface, many researchers have used thermal infrared (TIR) satellite remote sensing to estimate land surface reflectance properties to extract surface temperature and moisture for climatic analysis (Rajeshwari et al, 2014;Liu andZhang, 2011 andSrivastava et al, 2010). Land use and land cover (LULC) dynamics influence the ability of land surfaces to absorb or reflect solar radiation in varying proportions.…”

“…In furtherance of handling LULC dynamics, previous studies have indicated that depending on the type of LULC obtained, it could be possible to extract surface moisture and temperature characteristics from LULC maps generated from Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER), Landsat, MODIS and others (Ahrens, 2005;Liu andZhang, 2011 andSrivastava et al, 2010). Accordingly, the energy emitted by the LULC mosaic as indicated by its surface energy fluxes and atmospheric conditions, aids in determining the varying energy fluxes of land surfaces (Vlassova et al, 2014).…”