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

Xinyu Lan,Zhao-Liang Li,Enyu Zhao,Jelila Labed,Francoise Nerry

doi:10.1109/access.2020.3040780

Xinyu Lan, Zhao-Liang Li + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.3040780

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Abstract

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.

Highlights

The main objective in thermal infrared remote sensing is the accurate determination of the land surface temperature (LST) at scales that vary from the regional through to the global scale
To evaluate the accuracy and practicability of the LST retrieval, the infrared atmospheric sounding interferometer (IASI) brightness temperature and Advanced Very High Resolution Radiometer (AVHRR)/MetOp Daily LST products were established as the training and test datasets
For the research area training dataset, 90% of the dataset was adopted as training data, and 10% of the dataset was utilized as the test dataset for the deep mixture model

Summary

Introduction

The main objective in thermal infrared remote sensing is the accurate determination of the land surface temperature (LST) at scales that vary from the regional through to the global scale. 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. Some methods, such as the linear spectral emissivity constraint method [4], [12] and wavelet transform method [13], have adopted data dimensionality reduction to reduce the number of LSEs in order to solve underdetermined problems

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Conclusion