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

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 emis...

Full description

Saved in:
Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2017-12, Vol.9 (12), p.1247
Main Authors: Meng, Xiangchen, Cheng, Jie, Liang, Shunlin
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Atmospheric correction
Atmospheric effects
Broadband
Emissivity
Empirical analysis
Energy balance
FY-3C/MERSI
GLASS
Hydrology
Land surface emissivity
Land surface temperature
Meteorology
Radiative transfer
Remote sensing
Spatial discrimination
Surface energy
Surface properties
Surface temperature
Temperature
Water vapor
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs9121247