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Land surface temperature and emissivity separation from GF-5 visual and infrared multispectral imager data

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    College of Geomatics, Shandong University of Science and Technology, QingDao 266590, China

  • Email：yangykun_rs@163.com
  • Introduction：杨以坤，1992年生，男，硕士研究生，研究方向为地表温度和发射率反演，气溶胶反演及沙尘检测。E-mail：yangykun_rs@163.com
YANG Yikun12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

  • Email：lihua@radi.ac.cn
  • Introduction：历华，1982年生，男，副研究员，研究方向为热红外辐射传输建模、地表温度与发射率的遥感反演及验证。E-mail：lihua@radi.ac.cn
LI Hua1*,  
  • Affiliation：

    College of Geomatics, Shandong University of Science and Technology, QingDao 266590, China

SUN Lin2,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

DU Yongming1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

CAO Biao1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

LIU Qinhuo1,  
  • Affiliation：

    College of Geomatics, Shandong University of Science and Technology, QingDao 266590, China

ZHU Jinshan2

Resümee

Land Surface Temperature (LST), as a key parameter of surface physical processes at regional and global scales, is an important indicator of energy balance and climate change on the land surface. Land Surface Emissivity (LSE) is an intrinsic property of natural materials and is a key parameter of surface energy balance and mineral mapping. LSE is also an important input variable for LST retrieval. LST rapidly changes in space and time given the heterogeneity of land surface, and remote sensing in thermal infrared (TIR) provides a unique means of obtaining LST information at regional and global scales. GF-5 satellite, which was launched in 2018, is the fifth satellite in the national high-resolution Earth observation project of China. Visual and Infrared Multispectral Imager (VIMI) is a sensor onboard GF-5, which has four TIR channels centered at 8.20, 8.63, 10.80, and 11.95 μm with a spatial resolution of 40 m. In this study, we present a physics-based Temperature and Emissivity Separation (TES) algorithm (denoted as WVSTES algorithm) to retrieve LST and emissivity (LST&E) simultaneously from GF-5 VIMI data. The TES algorithm uses full radiative transfer simulations to isolate surface-emitted radiance and an emissivity calibration curve based on the variability in the surface radiance data to retrieve LST and spectral emissivity dynamically. Furthermore, an improved Water Vapor Scaling (WVS) model is adopted to improve the accuracy and stability of atmospheric correction for conditions with high atmospheric water vapor content. First, Seebor V5.0 atmospheric profile database and 81 emissivity spectra extracted from the ASTER spectral library were used to simulate WVS coefficients. Then, reanalysis data of the Modern Era Retrospective-analysis for Research and Applications (MERRA) and fast radiative transfer model RTTOV were used to perform the atmospheric correction of GF-5 TIR data. Second, atmospheric parameters, such as atmospheric transmittance, upwelling, and downwelling radiance, were adjusted through the WVS method. Finally, the LST&E were retrieved using the TES algorithm. Two methods were used to evaluate the accuracy of the proposed algorithm. The first method was used to evaluate the algorithm using simulated data constructed on Seebor profile database and MODTRAN 5.2 model. The second method was applied to validate the algorithm using 11 daytime simulated images from ASTER data acquired in the Heihe River Basin with the concurrent in situ LST&E measurements. First, the GF-5 at-sensor radiances were simulated using the MODTRAN 5.2 model with 9136 atmospheric profiles and 81 ASTER emissivity spectra to evaluate the simulated data. Second, the errors in atmospheric correction were simulated in terms of the total atmospheric water vapor content uncertainties, and the atmospheric profile was adjusted with a scaling factor of 1.2 in MODTRAN to simulate the errors. Finally, the standard TES and WVSTES algorithms were evaluated using the simulated data. Validation results using the simulated data show that LST RMSE reduces from 2.59 K to 1.54 K, and LSE RMSEs in the four bands reduce from 0.122, 0.12, 0.10, and 0.037 to 0.042, 0.040, 0.028, and 0.026, respectively, when the WVS model is applied. The validation results using the simulated GF-5 images show that the LST retrieved using the WVSTES algorithm agrees well with in situ LST data. The LST average bias reduces from 1.08 K to 0.47 K, and the RMSE reduces from 2.17 K to 1.70 K. The error between the retrieved emissivity and ground measured data is basically less than 1%. The abovementioned results indicate that the proposed WVSTES algorithm can retrieve accurate LST&E results, which can be used to obtain the LST&E with high accuracy and spatial resolution from the GF-5 VIMI data.

Schlüsselwort

remote sensing;land surface temperature;land surface emissivity;GF-5;WVS;TES

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