Estimating Land Surface Temperature from Feng Yun-3C/MERSI Data Using a New Land Surface Emissivity Scheme

Xiangchen Meng,Shunlin Liang,Jie Cheng

doi:10.3390/rs9121247

Abstract

Land surface temperature (LST) is a key parameter for a wide number of applications, including hydrology, meteorology and surface energy balance. In this study, we first proposed a new land surface emissivity (LSE) scheme, including a lookup table-based method to determine the vegetated surface emissivity and an empirical method to derive the bare soil emissivity from the Global LAnd Surface Satellite (GLASS) broadband emissivity (BBE) product. Then, the Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis data and the Feng Yun-3C/Medium Resolution Spectral Imager (FY-3C/MERSI) precipitable water vapor product were used to correct the atmospheric effects. After resolving the land surface emissivity and atmospheric effects, the LST was derived in a straightforward manner from the FY-3C/MERSI data by the radiative transfer equation algorithm and the generalized single-channel algorithm. The mean difference between the derived LSE and field-measured LSE over seven stations is approximately 0.002. Validation of the LST retrieved with the LSE determined by the new scheme can achieve an acceptable accuracy. The absolute biases are less than 1 K and the STDs (RMSEs) are less than 1.95 K (2.2 K) for both the 1000 m and 250 m spatial resolutions. The LST accuracy is superior to that retrieved with the LSE determined by the commonly used Normalized Difference Vegetation Index (NDVI) threshold method. Thus, the new emissivity scheme can be used to improve the accuracy of the LSE and further the LST for sensors with broad spectral ranges such as FY-3C/MERSI.

Highlights

Land surface temperature (LST) is one of the key parameters in the land surface physical processes at regional and global scales, integrating the interactions between the surface and atmosphere and all energy exchanges between the atmosphere and the land [1,2]
Comparing the land surface emissivity (LSE) derived by the Normalized Difference Vegetation Index (NDVI) threshold method with the LSE derived by the new scheme, the two methods have nearly similar LSTs, because the soil emissivity used here is the same as the new scheme, rather than the constant assumption adopted by the original NDVI threshold
According to the Moderate Resolution Imaging Spectroradiometer (MODIS) precipitable water product (MOD05), most of the total water vapor content of the validation stations in 2014 was less than 2.0 g/cm2, and so the Radiative Transfer Equation (RTE) and Generalized Single-Channel (GSC) algorithms all have obtained a high level of accuracy

Summary

Introduction

Land surface temperature (LST) is one of the key parameters in the land surface physical processes at regional and global scales, integrating the interactions between the surface and atmosphere and all energy exchanges between the atmosphere and the land [1,2]. Three kinds of algorithms have been proposed in the past decades to derive the LST from satellite data [4,5], i.e., the single-channel algorithm [6,7,8], the split-window (SW) algorithm [1,9,10,11] and the multi-channel algorithm [12,13]. Imaging Radiometer Suite (VIIRS) [10,17], Geostationary Operational Environmental Satellites (GOES) [18,19] and Spinning Enhanced Visible and Infrared Imager (SEVIRI) [20] Those LST products have been widely used for monitoring urban heat islands [21,22] and volcanoes [23,24,25], detecting forest fires [26,27], and so on

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