Detecting global irrigated areas by using satellite and reanalysis products

Area of Science:

• Earth and Environmental Sciences
• Hydrology
• Remote Sensing

Background:

• Global irrigation is vital for food production but its precise extent is unknown.
• Accurate global irrigated area data is essential for water and energy cycle studies.
• Existing irrigation maps have limitations in accuracy and coverage.

Purpose of the Study:

• To develop and validate a novel method for detecting global irrigated areas.
• To leverage satellite observations and reanalysis data for near real-time irrigation mapping.
• To improve the accuracy of land surface models by reducing uncertainties in irrigation parameterization.

Main Methods:

• Utilized a combination of satellite and reanalysis datasets.
• Developed a method based on detecting irrigation's aftereffects on soil moisture (SM), land surface temperature (LST), and surface albedo (A_L).
• Compared derived irrigation maps against established global irrigation datasets.

Main Results:

• The proposed method, combining SM, LST, and A_L signals, showed ~70% agreement with reference irrigated maps.
• Individual detection methods showed discrepancies in certain regions, highlighting the benefit of combining variables.
• The approach offers a near real-time estimation of irrigated areas.

Conclusions:

• The proposed method provides a reliable approach for mapping global irrigated areas.
• This technique can be used independently or to enhance existing irrigation datasets.
• Improved irrigated area data will benefit water and energy balance studies and land surface modeling.