Land Surface Temperature and Emissivity Separation from Cross-Track Infrared Sounder Data with Atmospheric Reanalysis Data and ISSTES Algorithm

Yu-Ze Zhang,Zhao-Xia Liu,Hua Wu,Xiao-Guang Jiang,Cheng Huang,Ya-Zhen Jiang

doi:10.1155/2017/7398312

Abstract

The Cross-track Infrared Sounder (CrIS) is one of the most advanced hyperspectral instruments and has been used for various atmospheric applications such as atmospheric retrievals and weather forecast modeling. However, because of the specific design purpose of CrIS, little attention has been paid to retrieving land surface parameters from CrIS data. To take full advantage of the rich spectral information in CrIS data to improve the land surface retrievals, particularly the acquisition of a continuous Land Surface Emissivity (LSE) spectrum, this paper attempts to simultaneously retrieve a continuous LSE spectrum and the Land Surface Temperature (LST) from CrIS data with the atmospheric reanalysis data and the Iterative Spectrally Smooth Temperature and Emissivity Separation (ISSTES) algorithm. The results show that the accuracy of the retrieved LSEs and LST is comparable with the current land products. The overall differences of the LST and LSE retrievals are approximately 1.3 K and 1.48%, respectively. However, the LSEs in our study can be provided as a continuum spectrum instead of the single-channel values in traditional products. The retrieved LST and LSEs now can be better used to further analyze the surface properties or improve the retrieval of atmospheric parameters.

Highlights

Land Surface Temperature and Emissivity (LST and Land Surface Emissivity (LSE)) are two key parameters in quantitative remote sensing and have been widely used in many fields such as meteorological and climate models, lithological mapping, and resources exploration [1, 2].Over recent decades, great effort has been made to retrieve the LST and LSEs from multispectral thermal infrared (TIR) data, and some typical algorithms have been successfully used to determine the LST and LSEs from space measurements [3]
It should be noted that the calculated mean LST might be not representative if there are no sufficient valid VIIRS pixels with high quality
Compared with studies on the atmosphere, little attention has been paid to the retrievals of surface parameters using Cross-track Infrared Sounder (CrIS) data, which are critical for determining accurate climate variables

Summary

Introduction

Land Surface Temperature and Emissivity (LST and LSE) are two key parameters in quantitative remote sensing and have been widely used in many fields such as meteorological and climate models, lithological mapping, and resources exploration [1, 2].Over recent decades, great effort has been made to retrieve the LST and LSEs from multispectral thermal infrared (TIR) data, and some typical algorithms have been successfully used to determine the LST and LSEs from space measurements [3]. According to the current multispectral land products, the general accuracy of the LST can be approximately 1 K [7,8,9,10]. Advances in Meteorology needed to determine the LST, such as prior knowledge of the LSE in the Split Window (SW) algorithm and the assumption of a constant LSE during night and day in the Day/Night (D/N) algorithm [4, 5]. Unlike the LST product, the current LSE products are usually determined according to a classification map, which makes the accuracy of the LSE product highly dependent on prior knowledge [8, 10]. Due to the characteristics of multispectral instruments, only a few broadband LSEs can be provided for the users [11]

Methods

Results

Discussion

Conclusion