Land surface temperature retrieval techniques and applications

Kerr, Yann H.; Lagouarde, J.-P.; Nerry, Françoise; Ottlé, Catherine; Quattrochi, Dale A.; Luvall, Jeff

doi:10.1201/9780203502174-c3

Cited by 51 publications

(46 citation statements)

References 78 publications

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“…지표면온도의 큰 시·공간적 변동성 을 고려할 때 정지궤도 기상위성으로부터 현업 차원 에서의 지표면온도의 산출이 가능해짐에 따라 지표면 온도의 활용에 새로운 전기가 다가오고 있다 (Prata and Cechet 1999;Sun and Pinker, 2003;Han et al, 2004;Sobrino and Romaguera 2004;Inamdar and French 2009;Pinker et al, 2009;Suh 2013b, Gottsche et al, 2016). 정확한 이해 및 고려가 필요하다 (Wan and Dozier, 1996;Goita et al, 1997;Kerr et al, 2004;Dash, 2005 (Gallo et al, 1996;Dai et al, 1999;Suh et al, 2009). 2) 연구방법 Cho et al(2015)가 개선한 기존의 알고리즘(CSW_v2.0)은 다양한 대기 조건에 대하여 휘도온도차, 지표면 방출율, Frey et al, 2012, Cho andSuh, 2013b;Li et al, 2013).…”

Section: 서 론unclassified

Improvement of COMS land surface temperature retrieval algorithm by considering diurnal variation of air temperature

Choi¹,

Suh²

2016

Korean Journal of Remote Sensing

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: Land Surface Temperature (LST) has been operationally retrieved from the Communication, Ocean, and Meteorological Satellite (COMS) data by the spilt-window method (CSW_v2.0) developed by Cho et al. (2015). Although the CSW_v2.0 retrieved the LST with a reasonable quality compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) LST data, it showed a relatively poor performance for the strong inversion and lapse rate conditions. To solve this problem, the LST retrieval algorithm (CSW_v2.0) was updated using the simulation results of radiative transfer model (MODTRAN 4.0) by considering the diurnal variations of air temperature. In general, the upgraded version, CSW_v3.0 showed a similar correlation coefficient between the prescribed LSTs and retrieved LSTs (0.99), the relatively smaller bias (from -0.03 K to-0.012 K) and the Root Mean Square Error (RMSE) (from 1.39 K to 1.138 K). Particularly, CSW_v3.0 improved the systematic problems of CSW_v2.0 that were encountered when temperature differences between LST and air temperature are very large and/or small (inversion layers and superadiabatic lapse rates), and when the brightness temperature differences and surface emissivity differences were large. The bias and RMSE of CSW_v2.0 were reduced by 10-30% in CSW_v3.0. The indirect validation results using the MODIS LST data showed that CSW_3.0 improved the retrieval accuracy of LST in terms of bias (from -0.629 K to -0.049 K) and RMSE (from 2.537 K to 2.502 K) compared to the CSW_v2.0.

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Section: 서 론unclassified

Improvement of COMS land surface temperature retrieval algorithm by considering diurnal variation of air temperature

Choi¹,

Suh²

2016

Korean Journal of Remote Sensing

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“…This Split Window method is mainly used for sea surface temperature (SST), but in LST retrieval it has more conflicts (Kerr, et al, 2005). The main reason that causes less accuracy in estimating LST is the variability of land surface emissivity.…”

Section: Avhrr Algorithmmentioning

confidence: 99%

“…The first one is based on a relationship between emissivity and shortwave bands (0.5~1.1 µm). This method is almost empirical and the coefficients are surface dependent (Kerr, et al, 2005). The second method calculates emissivity by solving the radiometric equation at the surface in the thermal infrared.…”

Section: Avhrr Algorithmmentioning

confidence: 99%

“…Because in channel 3 the daytime radiance is a combination of emitted radiance by the surface and reflected radiance due to Sun illumination, but the night-time radiance is just because of the emitted radiance. Therefore, the use of day-time and night-time images makes it possible to estimate the contribution of the reflected radiance and then emissivity (Kerr, et al, 2005 …”

Section: Avhrr Algorithmmentioning

confidence: 99%

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Trend Assessment of Spatio-Temporal Change of Tehran Heat Island Using Satellite Images

Saradjian

Sherafati

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Numerous investigations on Urban Heat Island (UHI) show that land cover change is the main factor of increasing Land Surface Temperature (LST) in urban areas, especially conversion of vegetation and bare soil to concrete, asphalt and other man-made structures. On the other hand, other human activities like those which cause to burning fossil fuels, that increase the amount of carbon dioxide, may raise temperature in global scale in comparison with small scales (urban areas). In this study, multiple satellite images with different spatial and temporal resolutions have been used to determine Land Surface Temperature (LST) variability in Tehran metropolitan area. High temporal resolution of AVHRR images have been used as the main data source when investigating temperature variability in the urban area. The analysis shows that UHI appears more significant at afternoon and night hours. But the urban class temperature is almost equal to its surrounding vegetation and bare soil classes at around noon. It also reveals that there is no specific difference in UHI intense during the days throughout the year. However, it can be concluded that in the process of city expansion in years, UHI has been grown both spatially and in magnitude. In order to locate land-cover types and relate them to LST, Thematic Mapper (TM) images have been exploited. The influence of elevation on the LST has also been studied, using digital elevation model derived from SRTM database.

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“…Methods to retrieve the LST that compensate for the emissivity effects can be classified into the following three categories: Single-channel methods, two-channel or split-window methods, and two-angle methods [44][45][46]. The single-channel methods rely on accurate atmospheric profiles [47], which are unavailable for our study site and image acquisition dates.…”

Section: Land Surface Temperature and Land Surface Emissivity Retrievalmentioning

confidence: 99%

Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Using ASTER Thermal Infrared Data, Part I: Methodology and Decadal Change Detection

Cao

Mishra

et al. 2015

Remote Sensing

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Coal fires that are induced by natural spontaneous combustion or result from human activities occurring on the surface and in underground coal seams destroy coal resources and cause serious environmental degradation. Thermal infrared image data, which directly measure surface temperature, can be an important tool to map coal fires over large areas. As the first of two parts introducing our coal fire detection method, this paper proposes a self-adaptive threshold-based approach for coal fire detection using ASTER thermal infrared data: the self-adaptive gradient-based thresholding method (SAGBT). This method is based on an assumption that the attenuation of temperature along the coal fire's boundaries generates considerable numbers of spots with extremely high gradient values. The SAGBT method applied mathematical morphology thinning to skeletonize the potential high gradient buffers into the extremely high gradient lines, which provides a self-adaptive mechanism to generate thresholds according to the thermal spatial patterns of the images. The final threshold was defined as an average temperature value OPEN ACCESSRemote Sens. 2015, 7 6577 reading from the high temperature buffers (segmented by 1.0 σ from the mean) and along a sequence of extremely high gradient lines (thinned from the potential high gradient buffers and segmented within the lower bounds, ranging from 0.5 σ to 1.5 σ and with an upper bound of 3.2 σ, where σ is the standard deviation), marking the coal fire areas. The SAGBT method used the basic outer boundary of the coal-bearing strata to simply exclude false alarms. The intermediate thresholds reduced the coupling with the temperature and converged by changing the potential high gradient buffers. This simple approach can be economical and accurate in identifying coal fire areas. In addition, it allows for the identification of thresholds using multiple ASTER TIR scenes in a consistent and uniform manner, and supports long-term coal fire change analyses using historical images in local areas. This paper focuses on the introduction of the methodology. Furthermore, an improvement to SAGBT is proposed. In a subsequent paper, subtitled "Part 2, Validation and Sensitivity Analysis," we address satellite-field simultaneous observations and report comparisons between the retrieved thermal anomalies and field measurements in different aspects to prove that the coal fires are separable by the SAGBT method. These comparisons allowed us to estimate the accuracy and biases of the SAGBT method. As an application of the SAGBT, a relationship between coal fires' decadal variation and coal production was also examined. Our work documented a total area increase in the beginning of 2003, which correlates with increased mining activities and the rapid increase of energy consumption in China during the decade (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011). Additionally, a decrease in the total coal fire area is consistent with the nationally sponsored fire suppression efforts during [2...

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Land surface temperature retrieval techniques and applications

Cited by 51 publications

References 78 publications

Improvement of COMS land surface temperature retrieval algorithm by considering diurnal variation of air temperature

Improvement of COMS land surface temperature retrieval algorithm by considering diurnal variation of air temperature

Trend Assessment of Spatio-Temporal Change of Tehran Heat Island Using Satellite Images

Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Using ASTER Thermal Infrared Data, Part I: Methodology and Decadal Change Detection

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