Global spatiotemporally continuous MODIS land surface temperature dataset.
Pei Yu1,2, Tianjie Zhao3, Jiancheng Shi4
1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, China.
Scientific Data
|April 2, 2022
Summary
This study reconstructs missing land surface temperature (LST) data from satellite observations. The developed method effectively fills gaps caused by clouds, ensuring more accurate global LST datasets.
Area of Science:
- Earth Science
- Remote Sensing
- Climate Science
Background:
- Land surface temperature (LST) is crucial for understanding land surface processes.
- Satellite-derived LST products suffer from data gaps due to cloud cover.
- Accurate LST data is essential for various environmental and climate studies.
Purpose of the Study:
- To develop a robust method for reconstructing missing LST data in cloud-affected areas.
- To produce a spatiotemporally continuous global LST dataset.
- To improve the reliability of satellite-based LST observations.
Main Methods:
- Data Interpolating Empirical Orthogonal Functions (DINEOF) for initial LST reconstruction.
- Cumulative Distribution Function (CDF) matching for correcting reconstructed LSTs.
- Generation of a 0.05° gridded MODIS LST dataset.
Main Results:
- Effective reconstruction of missing LST data with high accuracy.
- Root Mean Square Errors (RMSEs) of 1-2 K and R values of 0.820-0.996 under clear-sky conditions.
- RMSEs of 4-7 K and R values of 0.811-0.933 under all weather conditions.
Conclusions:
- The proposed method successfully reconstructs missing LST data, ensuring data continuity.
- The generated MODIS LST dataset offers improved accuracy and reliability for global applications.
- This approach addresses a key limitation in satellite LST data, enhancing its utility for scientific research.
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