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Reducing the Discrepancy Between ASTER and MODIS Land Surface Temperature Products.

Yuanbo Liu1, Yasushi Yamaguchi2, Changqing Ke3

  • 1Graduate School of Environmental Studies, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan. yb218@yahoo.com.

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|September 15, 2017
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Summary
This summary is machine-generated.

Satellite monitoring of land surface temperature (LST) is crucial for global warming studies. This research compares three methods to correct discrepancies between Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and MODerate-resolution Imaging Spectroradiometer (MODIS) LST data, finding the generalized split window (GSW) algorithm most effective.

Keywords:
ASTERMODISland surface temperatureremote sensingretrieval algorithmscale effectssurface emissivityterrain effects

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Area of Science:

  • Earth Observation
  • Remote Sensing
  • Climate Science

Background:

  • Global warming necessitates accurate satellite monitoring of land surface temperature (LST).
  • MODerate-resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) provide complementary LST data but have discrepancies.
  • Existing LST studies often overlook sensor-specific errors, hindering joint analysis.

Purpose of the Study:

  • To evaluate and compare three distinct approaches for correcting ASTER-to-MODIS LST discrepancies.
  • To assess the effectiveness of these correction methods, particularly in semi-arid terrain.
  • To identify the optimal method for harmonizing LST data from ASTER and MODIS for improved scale-related studies.

Main Methods:

  • Comparison of three LST correction approaches: Wan et al.'s, refined Wan et al.'s, and generalized split window (GSW) algorithm based approach.
  • The Wan et al.'s approach uses MODIS 5-km LST to correct MODIS 1-km LST.
  • The refined approach incorporates ASTER emissivity and MODIS 5-km data, while the GSW approach uses only ASTER emissivity data.
  • Validation was performed over a semi-arid region of China's Loess Plateau, including terrain correction.

Main Results:

  • All tested approaches effectively reduced the ASTER-to-MODIS LST discrepancy after terrain correction.
  • The original LST difference of 2.7±1.28 K was reduced to -0.1±1.87 K (Wan et al.), 0.2±1.57 K (refined), and 0.1±1.33 K (GSW).
  • The GSW algorithm based approach demonstrated superior performance across all evaluated metrics: mean difference, standard deviation, root mean square error, and correlation coefficient.

Conclusions:

  • The generalized split window (GSW) algorithm based approach is the most effective method for correcting ASTER-to-MODIS LST discrepancies.
  • Accurate LST data harmonization is essential for reliable scale-related studies utilizing multiple satellite sensors.
  • This research provides a validated method for improving the consistency of LST data, crucial for global change research.