DOI · DOI-0067 · record R2686

Evapotranspiration mapping and monitoring

Department of the Interior · USGS

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2025-04-30 (day precision)Source: 4/30/2025
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

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

System outputs

ET maps for the Western US, used as input to the OpenET project. Also on-demand generation of global ET.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: No
Public source code: Not reported

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: No
Demographic features: k) None of the above
Training and evaluation data: 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.
Federal Data Catalog: www.usgs.gov ↗

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported