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Updated: Sep 20, 2025
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Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
Published on: December 12, 2013
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Application of a Machine Learning Algorithm in Generating an Evapotranspiration Data Product From Coupled Thermal
Li Fang1,2, Xiwu Zhan2, Satya Kalluri2
1Earth System Science Interdisciplinary Center, Cooperate Institute of Satellite Earth System Studies (CISESS), University of Maryland, College Park, MD, United States.
Frontiers in Big Data
|June 7, 2022
Summary
This study introduces an improved method for estimating land surface evapotranspiration (ET) using satellite data, overcoming cloud interference to provide daily, all-weather ET maps for better water cycle monitoring.
Area of Science:
- Hydro-climatology
- Remote Sensing
- Big Data Science
Background:
- Land surface evapotranspiration (ET) is crucial for atmospheric dynamics and water cycles.
- Accurate ET estimation is a key research area in hydro-climatology.
- Existing satellite methods are limited by cloud cover, impacting data availability.
Purpose of the Study:
- To develop an all-weather system for estimating land surface evapotranspiration (ET).
- To improve the Geostationary satellite Evapotranspiration and Drought (GET-D) product system.
- To integrate multiple satellite data sources and land surface models for enhanced ET estimation.
Main Methods:
- Utilized a machine learning regression tree algorithm.
- Combined thermal infrared (TIR) and microwave (MW) satellite observations.
- Integrated GOES TIR data, passive microwave data, and land surface model (LSM) simulations for all-weather land surface temperature (LST).
Main Results:
- Developed an all-weather LST estimation method using a regression tree approach.
- Successfully generated daily ET estimates under all weather conditions.
- Evaluated LST and ET estimates against ground measurements, showing satisfactory accuracy.
Conclusions:
- The regression tree machine learning method is effective for all-weather ET estimation.
- The upgraded GET-D system provides a feasible solution for operational, all-weather ET mapping.
- This advancement enhances the utility of satellite observations for water cycle research.
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