Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs |
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| Institution: | 1. Indian Institute of Remote Sensing, Kalidas Road, Dehradun 248001, India;2. Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India;1. Department of Earth Physics and Thermodynamics, Faculty of Physics, University of Valencia., 50 Dr. Moliner, E-46100 Burjassot, Spain;2. Instituto Universitario Centro de Estudios Ambientales del Mediterráneo – CEAM-UMH, 14 Charles Darwin, E-46980 Paterna, Spain;1. School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou, China;2. Faculty of Geo-information Science and Earth Observation (ITC), The University of Twente, Enschede, The Netherlands;3. Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China;1. Department of Natural Resources, TERI University, New Delhi 110 070, India;2. Department of Civil Engineering, Indian Institute of Technology, Roorkee 247667, India;1. Department of Arid Lands Management, Faculty of Natural Resources and Desert Studies, Yazd University, Yazd, Iran;2. Department of Geography, Yazd University, Yazd, Iran |
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| Abstract: | The present study proposes land surface temperature (LST) retrieval from satellite-based thermal IR data by single channel radiative transfer algorithm using atmospheric correction parameters derived from satellite-based and in-situ data and land surface emissivity (LSE) derived by a hybrid LSE model. For example, atmospheric transmittance (τ) was derived from Terra MODIS spectral radiance in atmospheric window and absorption bands, whereas the atmospheric path radiance and sky radiance were estimated using satellite- and ground-based in-situ solar radiation, geographic location and observation conditions. The hybrid LSE model which is coupled with ground-based emissivity measurements is more versatile than the previous LSE models and yields improved emissivity values by knowledge-based approach. It uses NDVI-based and NDVI Threshold method (NDVITHM) based algorithms and field-measured emissivity values. The model is applicable for dense vegetation cover, mixed vegetation cover, bare earth including coal mining related land surface classes. The study was conducted in a coalfield of India badly affected by coal fire for decades. In a coal fire affected coalfield, LST would provide precise temperature difference between thermally anomalous coal fire pixels and background pixels to facilitate coal fire detection and monitoring. The derived LST products of the present study were compared with radiant temperature images across some of the prominent coal fire locations in the study area by graphical means and by some standard mathematical dispersion coefficients such as coefficient of variation, coefficient of quartile deviation, coefficient of quartile deviation for 3rd quartile vs. maximum temperature, coefficient of mean deviation (about median) indicating significant increase in the temperature difference among the pixels. The average temperature slope between adjacent pixels, which increases the potential of coal fire pixel detection from background pixels, is significantly larger in the derived LST products than the corresponding radiant temperature images. |
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| Keywords: | Land surface temperature (LST) Land surface emissivity (LSE) Jharia Coalfield India |
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