Preliminary applications of a land surface temperature retrieval method to IASI and AIRS data

Ouyang, Xiaoying; Kang, Guoting; Zeng, Funian; Qi, Enyun; Li, Zhao-Liang

doi:10.1080/01431161.2012.716916

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…The articles selected to appear in this special issue cover a very wide range of quantitative aspects of the use of remote sensing. The dominant areas include land surface temperature (Freitas et al 2013;Göttsche, Olesen, and Bork-Unkelbach 2013;Li et al 2013;Ouyang et al 2013;Qian, Li, and Nerry 2013;Raissouni et al 2013;Smith et al 2013;Sobrino et al 2013; Wu, Tang, and Li 2013). Other topics attracted some interest on atmospheric and meteorological studies (Boussetta et al 2013;Bravo-Aranda et al 2013;Campo et al 2013;Castellanos et al 2013;Pérez-Ramírez et al 2013;Segura et al 2013).…”

Section: Preface Third International Symposium On Recent Advances In mentioning

confidence: 97%

Third International Symposium on Recent Advances in Quantitative Remote Sensing

Cracknell¹

2013

International Journal of Remote Sensing

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Section: Preface Third International Symposium On Recent Advances In mentioning

confidence: 97%

Third International Symposium on Recent Advances in Quantitative Remote Sensing

Cracknell¹

2013

International Journal of Remote Sensing

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“…Many methods have been developed to acquire LST information from the at-sensor radiance after accurate atmospheric correction, temperature-emissivity separation methods add physical constraints to obtain the accurate LST or reduce the number of unknowns to make the underdetermined problem solvable using data dimensionality reduction technology such as the iterative spectrally smooth temperature emissivity separation method [12]- [15], downwelling radiance residual index [16], [17], correlation-based temperature and emissivity separation algorithm [18], stepwise refining temperature and emissivity separation algorithm [19], linear spectral emissivity constraint method [11], [20], emissivity eigenvector method [21], and wavelet transform method [22].…”

Section: Introductionmentioning

confidence: 99%

Alternative Physical Method for Retrieving Land Surface Temperatures from Hyperspectral Thermal Infrared Data: Application to IASI Observations

Lan

Zhao

Leng

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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A physical method was developed to retrieve the land surface temperature (LST) from infrared atmospheric sounding interferometer (IASI) observations. It is a simple two-step physical retrieval method, which can be used to relinearize the radiative transfer equation (RTE) by the tangents around the initial estimates of the LST, land surface emissivity (LSE), atmospheric equivalent temperature (Ta), and water vapor content (q) without considering the complex vertical structure of the atmospheric profile. Principal component analysis was utilized to reduce the number of unknown Ta and LSE. The Tikhonov regularization method and discrepancy principle iteration algorithm were employed to stabilize the ill-posed problem and obtain the final maximum likelihood solution of the LST. A new channel selection scheme was proposed for this physical method to obtain an accurate LST estimation. This physical algorithm was tested on both simulated and real data obtained from the IASI. The root-mean-square error (RMSE) of the simulated LST is ~1 K based on an initial LST estimate with an RMSE of 2 K (1.9 K). The sensitivity analysis shows that the LST retrieval accuracy is ~1 K based on an LST with a random error of 3 K, constant initial LSE (0.97), 10% Ta error, and 40% q error. Within the given error range of the initial values of the simulated dataset, the LST retrieval accuracy is insignificantly affected by the initial estimates of the unknown variables. Finally, compared with the Advanced Very High Resolution Radiometer onboard Metop (AVHRR/Metop) LST product, the physical method based on artificial neural network improves the LST retrieval accuracy to 1.5 and 1 K for real daytime and nighttime IASI data obtained in the study area. Based on the new method,

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“…For example, the iterative spectrally smooth (ISSTES) method [5], [6], [7], [8] defines a smoothness index because inaccurate LST will cause the calculated LSE to retain characteristics such as atmospheric emission lines. The downwelling radiance residual index (DRRI) [9], [10] describes the direction and magnitude of the downwelling radiance residual feature associated with some commonlyused channel groups. In addition, the correlation-based temperature and emissivity separation (CBTES) algorithm [11] describes the relationship between the surface emissivity and atmospheric downward radiance to optimize surface temperature.…”

Section: Introductionmentioning

confidence: 99%

Deep Mixture Model-Based Land Surface Temperature Retrieval for Hyperspectral Thermal IASI Sensor

Lan

Zhao

et al. 2020

IEEE Access

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A deep mixture model was developed to retrieve land surface temperatures (LSTs) from infrared atmospheric sounding interferometer (IASI) observations. The IASI brightness temperature (Tb) data and the Advanced Very High Resolution Radiometer onboard MetOp (AVHRR/MetOp) LST data were randomly divided into training and test datasets, and a deep mixture model was constructed to simulate radiation transmission in order to invert the LST. The constructed model could evaluate dataset characteristics that included global features, local features, and time-domain predictions, covering most of the features of the satellite dataset. For the test datasets, the root mean square error (RMSE) indicated that the LST in Algeria and South Africa could be retrieved with an error of less than 2 K and 2.5 K, respectively. Compared with the AVHRR/MetOp LST product in March and December 2019 for Algeria and South Africa, the LST could be retrieved with the maximum RMSE of 2.5 K. The LST retrievals at nighttime had an RMSE of less than 2.0 K, which was superior to those retrieved during daytime for Algeria. This deep mixture model can be applied to time-series temperature prediction. INDEX TERMS Hyperspectral thermal infrared; land surface temperature retrieval; IASI; deep learning

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Preliminary applications of a land surface temperature retrieval method to IASI and AIRS data

Cited by 9 publications

References 13 publications

Third International Symposium on Recent Advances in Quantitative Remote Sensing

Third International Symposium on Recent Advances in Quantitative Remote Sensing

Alternative Physical Method for Retrieving Land Surface Temperatures from Hyperspectral Thermal Infrared Data: Application to IASI Observations

Deep Mixture Model-Based Land Surface Temperature Retrieval for Hyperspectral Thermal IASI Sensor

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