Google Earth Engine METRIC (GEM) Application for Remote Sensing of Evapotranspiration

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1 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

2 Overview Brief summary of METRIC model Challenges of model comparison in the Sacramento-San Joaquin Bay Delta project Google Earth engine METRIC Application

3 A Brief Overview of the METRIC Model

4 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

5 METRIC Evapotranspiration Net Surface Radiation Flux Soil Heat Flux Air Sensible Heat Flux

6 METRIC Evapotranspiration Net Surface Radiation Flux Soil Heat Flux Air Sensible Heat Flux Evapotranspiration (mm/day) = Latent Heat Flux (W/m 2 ) Latent Heat of Vaporization (kj/kg)

Local CIMIS Station Weather Data Landsat Spectral

Evapotranspiration Calculations Land Use and Crop

Evapotranspiration Raster Image Daily Crop

7 Local CIMIS Station Weather Data Landsat Spectral and Thermal Image Digital Elevation Maps ASCE Evapotranspiration Calculations Land Use and Crop Type Maps METRIC Model Surface Energy Balance Daily Evapotranspiration Raster Image Daily Crop Coefficient Raster Image Instantaneous Vegetation Indices Raster Image

8 Challenges of Model Comparison in the Sacramento-San Joaquin Bay Delta Project

9 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

10 QAQC : CIMIS Stations

11 Common Inputs : Solar Radiation

12 Common Inputs : Solar Radiation

13 Common Inputs : Wind Speed

14 Common Inputs : Cloud Masking

15 FAO Oweis and Hachum (2004) Common Inputs : Aggregation and Interpolation

16 Challenges of Model Comparison How can we make our model more transparent? How can we make our model more flexible?

17 Google Earth Engine METRIC Application

18 Google Earth Engine METRIC Application Goal is to improve METRIC processing in terms of: Flexibility Transparency Efficiency Repeatability

19 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

20 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)

21 Google Earth Engine METRIC Application

22 Google Earth Engine METRIC Application

23 Google Earth Engine METRIC Application Scripts can be customized for California conditions Surface roughness equations can be easily customized for orchards and vineyards

24 Google Earth Engine METRIC Application

25 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)

26 Google Earth Engine METRIC Application

27 Google Earth Engine METRIC Application Instantaneous visual and statistical representation of the range of model output values based on the calibration parameters chosen

28 Google Earth Engine METRIC Application

29 Google Earth Engine METRIC Implementation and Goals

30 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

31 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

METRIC tm. Mapping Evapotranspiration at high Resolution with Internalized Calibration. Shifa Dinesh

METRIC tm. Mapping Evapotranspiration at high Resolution with Internalized Calibration. Shifa Dinesh METRIC tm Mapping Evapotranspiration at high Resolution with Internalized Calibration Shifa Dinesh Outline Introduction Background of METRIC tm Surface Energy Balance Image Processing Estimation of Energy