Google Earth Engine METRIC (GEM) Application

for Remote Sensing of Evapotranspiration

Nadya Alexander Sanchez, Quinn Hart, Justin Merz, and Nick Santos

Center for Watershed Sciences

University of California, Davis

2017

Overview

• Brief summary of METRIC model

• Challenges of model comparison in the Sacramento-San Joaquin Bay Delta project

• Google Earth engine METRIC Application

A Brief Overview of the METRIC Model

METRIC

• Remote image processing model that uses a surface energy balance to estimate crop evapotranspiration

• Developed by Drs. Richard Allen, Ricardo Trezza, Masahiro Tasumi, and Jeppe Kjaersgaard at the University of Idaho beginning around 2000

Evapotranspiration ≈ Net Surface Radiation Flux – Soil Heat Flux – Air Sensible Heat Flux

METRIC

Evapotranspiration (mm/day) =

Latent Heat Flux (W/m2)

Latent Heat of Vaporization (kJ/kg)

METRIC Evapotranspiration ≈

Net Surface Radiation Flux – Soil Heat Flux – Air Sensible Heat Flux

Instantaneous Vegetation Indices

Raster Image

Daily Evapotranspiration

Raster Image

Daily Crop Coefficient

Raster Image

Local CIMIS Station Weather

Data

Digital Elevation

Maps

ASCE Evapotranspiration

Calculations

Land Use and Crop Type Maps

Landsat Spectral and

Thermal Image

METRIC Model

Surface Energy Balance

Challenges of Model Comparison in the

Sacramento-San Joaquin Bay Delta Project

Challenges of Model Comparison

• Wide range of models compared

• Large variability in results across models

• How to separate the variability by factor? • Inputs

• Algorithm

• Aggregation

• Gap filling

• Difficult to standardize inputs when models have different data types and strong precedents

QAQC : CIMIS Stations

Common Inputs : Solar Radiation

Common Inputs : Solar Radiation

Common Inputs : Wind Speed

Common Inputs : Cloud Masking

Common Inputs : Aggregation and Interpolation

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FAO

Challenges of Model Comparison

• How can we make our model more transparent?

• How can we make our model more flexible?

Google Earth Engine METRIC Application

Google Earth Engine METRIC Application

Goal is to improve METRIC processing in terms of:

• Flexibility

• Transparency

• Efficiency

• Repeatability

Google Earth Engine METRIC Application

• No need to gather most input data • Landsat data available via Google Earth Engine

• Elevation available via Google Earth Engine

• CIMIS data added to GEM code

• Spatial CIMIS ETo added to GEM code

• ETo and ETr calculations added to GEM code (Penmann-Monteith)

• Land use data must be uploaded

Google Earth Engine METRIC Application

• Automation of most, but not all processes • METRIC processing is fully automated EXCEPT core calibration (selection of

hot and cold anchor pixels)

Google Earth Engine METRIC Application

Google Earth Engine METRIC Application

Google Earth Engine METRIC Application

• Scripts can be customized for California conditions • Surface roughness equations can be easily customized for orchards and

vineyards

Google Earth Engine METRIC Application

Google Earth Engine METRIC Application

• Scripts can be customized for California conditions • Surface roughness equations can be easily customized for orchards and

vineyards

• NDVI equations can be adjusted using presets for flooded rice

• Thermal sharpening can be modified based on three presets: • Standard

• Desert-adjacent

• Water-adjacent (such as the Sacramento-San Joaquin Bay Delta)

Google Earth Engine METRIC Application

Google Earth Engine METRIC Application

• Instantaneous visual and statistical representation of the range of model output values based on the calibration parameters chosen

Google Earth Engine METRIC Application

Google Earth Engine METRIC Implementation and Goals

Google Earth Engine METRIC Implementation and Goals

• Target audience is water models at research institutions, state agencies, and in the private sector

• Interface designed for ease-of-use while still retaining statistical robusticity required for research

• Parameters are transparent and retained in model records to improve repeatability of results and increase confidence

• Huge reduction in processing time allows for more thorough sensitivity analysis of model runs

Thank You.

Thanks to the UC Davis Center for Watershed Sciences, the Office of the

Delta Watermaster, the California Department of Food and Agriculture

for their support, and Drs Rick Allen and Ricardo Trezza for their

feedback