An Analytic Solution Method for Retrieving Land Surface Temperature from Remotely Sensed Thermal Infrared Imagery
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RESEARCH ARTICLE
An Analytic Solution Method for Retrieving Land Surface Temperature from Remotely Sensed Thermal Infrared Imagery Hongrui Zhao & Hui Ren & Gang Fu
Received: 26 June 2013 / Accepted: 29 September 2014 # Indian Society of Remote Sensing 2015
Abstract This paper investigates land surface temperature (LST) retrieval method based on multiband remotely sensed thermal infrared data. In search of quantitative remote sensing retrieval methods for the inversion model of the radiance transfer equation, a quantitative retrieval analytical solution method is proposed, which adds a constraint of the difference between the LST and the effective mean atmospheric temperature based on prior knowledge, and introduces prior knowledge for the second time in deriving an analytical solution of the retrieved LST. The verification tests with both simulated data and real MODIS data show that this analytical solution method is relatively superior to both the split-window algorithm and the iterative retrieval method for the radiance transfer equation in terms of retrieval precision, stability and efficiency; moreover, the new method reveals more general applicability theoretically, and it may be applied to comprehensive processing of thermal infrared imagery recorded by different sensors. Keywords Land surface temperature . Thermal infrared remote sensing . Retrieval
Introduction Land surface temperature (LST) is not only one of the most important parameters in research fields such as climate change Supported by The National Natural Science Fund (40771135), The Special Research Project for the Commonwealth of the Ministry of Water Resources of the People’s Republic of China (201201092), and The Western Light Fund (2009y236). H. Zhao (*) : H. Ren : G. Fu Institute of Geomatics, Department of Civil Engineering, Tsinghua University, Beijing, China e-mail: [email protected] H. Ren e-mail: [email protected]
and ecological environment evolution, but also the basis of acquiring other geophysical parameters like land surface moisture and evapotranspiration. Remote sensing provides a means of repeatable measurement in a wide geometrical range (Eduardo et al. 2012), and LST retrieval has been a focus and also obstacle of quantitative remote sensing retrieval (Luo et al. 2012; Merlin et al. 2012). Data, inversion models and retrieval methods are the critical components of quantitative remote sensing retrieval. Shortage of information has been a problem in LST retrieval; nowadays, more information is available from multi-band, multi-sensor remote sensing data. We expect that we could get more accurate retrieval results by making full use of these information. The inversion models are physical or statistical models. The physical inversion models based on Planck’s law are the most common approaches because they are considered to be superior to statistical models. Retrieval methods are required to be oriented to practical applications, convenient and rapid to implement, stable in the course of processing and accurate in the gen
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