Land surface temperature and emissivity estimation based on the two-temperature method: sensitivity analysis using simulated MSG/SEVIRI data

Peres, Leonardo F.; DaCamara, Carlos C.

doi:10.1016/j.rse.2004.03.011

Cited by 97 publications

(68 citation statements)

References 21 publications

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“…Emissivity may differ according to the viewing angle, surface moisture, and roughness as well as with vegetation [16,27]. Notwithstanding the fact that there are many algorithms that have been proposed for the estimation of LSE [28][29][30][31], in this study, the estimation of LSE with prior known LSE has been used. This is because the algorithm is more practical and has a reasonable accuracy [16,32,33].…”

Section: Estimation Of Land Surface Emissivity (Lse)mentioning

confidence: 99%

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

Ndossi

Avdan

2016

Remote Sensing

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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.

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Section: Estimation Of Land Surface Emissivity (Lse)mentioning

confidence: 99%

“…SURFRAD LST can be derived from upwelling wave flux measurements during the satellite overpass time using Equation (28).…”

Section: Derival Of Surfrad Land Surface Temperature (Lst)mentioning

confidence: 99%

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

Ndossi

Avdan

2016

Remote Sensing

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Section: Ji-hyun Kim and Myoung-seok Suhmentioning

confidence: 99%

“…Corresponding Author: Myoung-Seok Suh (sms416@kongju.ac.kr) surface variables to observe regularly due to the strong spatio-temporal variations. At present, the only available cost-effective operational systems capable of observing the LST at spatial and temporal resolutions appropriate to the various applications are the satellite sensors working in the thermal infrared and/or microwaves (Becker and Li, 1995;Peres and DaCamara, 2004).After Becker and LI (1990) has retrieved the LST from meteorological satellite data by using splitwindow method, number of works have been performed to retrieve the LST from the satellite data, especially polar orbit satellites (Kerr et al, 1992; Ulivieri et al, 1994; Wan and Dozier, 1996; Sobrino and Romaguera, 2004; Suh et al, 2008; Hong et al, 2009a, b). However, operational retrieval of LST from the meteorological satellite data is very limited due to the poor quality of retrieved LST, especially by using geostationary satellite data over East Asia (Hong et al, 2009a).…”

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confidence: 99%

Development of Land Surface Temperature Retrieval Algorithm from the MTSAT-2 Data

Kim¹,

Suh²

2011

Korean Journal of Remote Sensing

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In general, land-atmosphere interactions are mainly controlled by the biophysical parameters of land surface, such as the land surface temperature (LST), soil moisture, leaf area index, and roughness length.So, the LST is one of the key environmental variables which can be used in a wide range of applications, such as weather, climate modeling, hydrology, surface urban heat island and ecology (Prata and Cechet, 1999; Voogt and Oke, 2003;Hong et al., 2009a). However, LST is one of the most difficult -653- Development of Land Surface Temperature RetrievalAlgorithm from the MTSAT-2 Data Ji-Hyun Kim and Myoung-Seok SuhDepartment of Atmospheric Science, Kongju National University, 182 Shinkwan-dong, Gongju-city 314-701, ChungCheongnam-do, KoreaAbstract : Land surface temperature (LST) is a one of the key variables of land surface which can be estimated from geostationary meteorological satellite. In this study, we have developed the three sets of LST retrieval algorithm from MTSAT-2 data through the radiative transfer simulations under various atmospheric profiles (TIGR data), satellite zenith angle, spectral emissivity, and surface lapse rate conditions using MODTRAN 4. The three LST algorithms are daytime, nighttime and total LST algorithms. The weighting method based on the solar zenith angle is developed for the consistent retrieval of LST at the early morning and evening time. The spectral emissivity of two thermal infrared channels is estimated by using vegetation coverage method with land cover map and 15-day normalized vegetation index data. In general, the three LST algorithms well estimated the LST without regard to the satellite zenith angle, water vapour amount, and surface lapse rate. However, the daytime LST algorithm shows a large bias especially for the warm LST (> 300 K) at day time conditions. The night LST algorithm shows a relatively large error for the LST (260 2 80K) at the night time conditions. The sensitivity analysis showed that the performance of weighting method is clearly improved regardless of the impacting conditions although the improvements of the weighted LST compared to the total LST are quite different according to the atmospheric and surface lapse rate conditions. The validation results of daytime (nighttime) LST with MODIS LST showed that the correlation coefficients, bias and RMSE are about 0.62~0.93 (0.44~0.83), -1.47~1.53 (-1.80~0.17), and 2.25~4.77 (2.15~ 4.27), respectively. However, the performance of daytime/nighttime LST algorithms is slightly degraded compared to that of the total LST algorithm. Received November 7, 2011; Revised November 30, 2011, Revised December 10, 2011 Accepted December 11, 2011. Corresponding Author: Myoung-Seok Suh (sms416@kongju.ac.kr) surface variables to observe regularly due to the strong spatio-temporal variations. At present, the only available cost-effective operational systems capable of observing the LST at spatial and temporal resolutions appropriate to the various applications are the satellite sensors working in the thermal infrare...

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“…In terms of TIR data, the spectrum is generally dominated by temperature, which results in TIR radiance containing both temperature and emissivity information. The algorithms to separate temperature and emissivity are generally complex and problematic because of the nonlinearity in the relative contributions of temperature and emissivity effects [12][13][14]. The extracted emissivity products have a relatively high uncertainty and low contrast, which may subdue spectral features and limit the discrimination accuracy [15,16].…”

Section: Introductionmentioning

confidence: 99%

Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

2016

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Abstract:Texture as a measure of spatial features has been useful as supplementary information to improve image classification in many areas of research fields. This study focuses on assessing the ability of different textural vectors and their combinations to aid spectral features in the classification of silicate rocks. Texture images were calculated from Landsat 8 imagery using a fractal dimension method. Different combinations of texture images, fused with all seven spectral bands, were examined using the Jeffries-Matusita (J-M) distance to select the optimal input feature vectors for image classification. Then, a support vector machine (SVM) fusing textural and spectral features was applied for image classification. The results showed that the fused SVM classifier achieved an overall classification accuracy of 83.73%. Compared to the conventional classification method, which is based only on spectral features, the accuracy achieved by the fused SVM classifier is noticeably improved, especially for granite and quartzose rock, which shows an increase of 38.84% and 7.03%, respectively. We conclude that the integration of textural and spectral features is promising for lithological classification when an appropriate method is selected to derive texture images and an effective technique is applied to select the optimal feature vectors for image classification.

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Land surface temperature and emissivity estimation based on the two-temperature method: sensitivity analysis using simulated MSG/SEVIRI data

Cited by 97 publications

References 21 publications

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

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

Development of Land Surface Temperature Retrieval Algorithm from the MTSAT-2 Data

Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

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