Implementation of the EEFLux/METRIC Algorithms into the Google Earth Engine for Computation of Water Consumption

Affiliation(s) PI Project period Funded by
DHS Huntington, Justin L. 02/18/2013 - 02/28/2015 Google, Inc.

Project Description

We propose to implement an algorithm to be named EEFlux on the Google Earth Engine to estimate water consumption from vegetation at 30 m scale around the globe. "EEFlux" is an acronym representing 'Earth Engine Evapotranspiration Flux.' EEFlux is derived from the widely used surface energy balance model "METRIC" (Mapping Evapotranspiration at high Resolution with Internalized Calibration), where METRIC is a Landsat-image-based process currently employed in a number of US states to determine 'maps' of water consumption from vegetation, a process referred to as evapotranspiration (ET). The use of Landsat imagery enables production of ET maps having resolutions of 30 m, which is the scale of many human-impacted and human-interest activities, such as agricultural fields, forest clearcuts and vegetation systems along streams. This proposal 'gangs' support for three Ph.D. students from three institutions (University of Idaho, University of Nebraska-Lincoln and Desert Research Institute/University of Nevada-Reno). Each PI and supported Ph.D. student have complementary capabilities and will conduct coordinated work components to facilitate implementation. Submission of this proposal has been encouraged by Dr. David Thau, Developer Advocate, Google Earth Engine, Google. Institutional roles are detailed in the proposal body on the following page. The University of Idaho is a nation-leading developer of satellite image processing algorithms that produce high resolution, high accuracy spatial maps of water consumption by vegetation. The DRI group has been developing and testing innovative Python code for batching and automating a number of components of the METRIC process. The UNL group has developed new techniques for web delivery of evapotranspiration (ET) products and to manage spatially gridded weather data and soils data required for time-integration of ET between satellite image dates. The three institutions will work closely together to collaborate with distinct, complementary work tasks to produce a robust, final product.