google earth engine metric (gem) application for remote
TRANSCRIPT
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
Ow
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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