Evapotranspiration mapping and monitoring
Description
Mapping evapotranspiration (ET) with remote sensing is essential because it provides a consistent, large-scale view of how water is being used across landscapes. In agriculture, ET maps help farmers and water managers track crop water use, improve irrigation efficiency, and manage limited water resources more sustainably. Beyond farming, ET mapping supports drought monitoring, groundwater management, ecosystem health assessments, and fire fuel moisture assessments by showing how water and energy cycles vary over space and time. Without remote sensing, this kind of detailed, spatially explicit information would be impossible to obtain at regional or global scales. AI was used by USGS EROS to improve our ability to map ET, using multi-layer perceptrons and DNNs.
Detailed example
ET maps for the Western US, used as input to the OpenET project. Also on-demand generation of global ET.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
An improved ET product means a more informed farmer, allowing them to efficiently manage water resources and the crops that depend on them. The use of AI improves our ability to characterize surface temperature boundary conditions
Controls / human review
ATO: No; PIA: Not published
Data needed
Thermal data from the Landsat sensor is the primary data used in our SSEBop algorithm for mapping ET. For coarser scale applications, thermal data from MODIS or VIIRS are used. A reference ET variable required for mapping actual ET is dependent upon gridded meteorological data such as GRIDMET.