Remotely sensed terrestrial open water evaporation.

Area of Science:

• Earth and Environmental Sciences
• Hydrology
• Remote Sensing

Background:

• Terrestrial open water evaporation is crucial for water resource management but difficult to measure.
• Satellite-derived evapotranspiration (ET) data, including open water evaporation, are increasingly available but require robust validation.
• Existing open water evaporation algorithms differ from main ET algorithms and are often overlooked in evaluations.

Purpose of the Study:

• To evaluate the AquaSEBS algorithm for open water evaporation retrieval using satellite data.
• To validate AquaSEBS performance against extensive in situ measurements from diverse global sites.
• To assess the impact of high wind events and temporal integration on retrieval accuracy.

Main Methods:

• Validated the AquaSEBS algorithm against 19 in situ open water evaporation sites globally.
• Utilized MODIS and Landsat satellite data for remotely sensed evaporation retrieval.
• Compared AquaSEBS performance with 11 machine learning models and analyzed the influence of high wind conditions.

Main Results:

• Remotely sensed open water evaporation showed good agreement with in situ data (r²=0.71) when high wind events were accounted for.
• High wind events significantly impacted instantaneous accuracy (r²=0.47) by shifting evaporation from radiative to atmospheric control.
• Temporal integration minimized wind sensitivity, and machine learning models did not significantly outperform AquaSEBS.

Conclusions:

• Remotely sensed open water evaporation data provide valuable insights into hydrological processes, despite inherent uncertainties.
• The AquaSEBS algorithm demonstrates reliable performance, offering a foundation for future satellite-based evaporation monitoring.
• Further research should address in situ measurement uncertainties, forcing data, and scaling mismatches for enhanced accuracy.