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Earth Engine based SSEBop Model

Project description

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WARNING: This code is in development, is being provided without support, and is subject to change at any time without notification

This repository provides Google Earth Engine Python API based implementation of the SSEBop ET model.

The Operational Simplified Surface Energy Balance (SSEBop) model computes daily total actual evapotranspiration (ETa) using land surface temperature (Ts), maximum air temperature (Ta) and reference ET (ETr). The SSEBop model does not solve all the energy balance terms explicitly; rather, it defines the limiting conditions based on clear-sky net radiation balance principles. This approach predefines unique sets of “hot/dry” and “cold/wet” limiting values for each pixel and is designed to reduce model operator errors when estimating ET routinely.

Basic SSEBop model architecture in Earth Engine:


Input Collections

SSEBop ET can currently only be computed for Landsat Collection 1 TOA image from the following Earth Engine image collections:


Note that scene specific Tcorr values have only been computed for Landsat images covering the contiguous United States (CONUS). SSEBop estimates for Landsat images outside the CONUS will use the default c-factor value of 0.978 (see the Tcorr (C-factor) section for more details).

Model Design

The primary component of the SSEBop model is the Image() class. The Image class can be used to compute a single fraction of reference ET (ETf) image from a single input image. The Image class should generally be instantiated from an Earth Engine Landsat image using the collection specific methods listed below. ET image collections can be built by computing ET in a function that is mapped over a collection of input images. Please see the Example Notebooks for more details.

Landsat Collection 1 TOA Input Image

To instantiate the class for a Landsat Collection 1 TOA image, use the Image.from_landsat_c1_toa() method.

The input Landsat image must have the following bands and properties:

LANDSAT_5 B1, B2, B3, B4, B5, B7, B6, BQA
LANDSAT_7 B1, B2, B3, B4, B5, B7, B6_VCID_1, BQA
LANDSAT_8 B2, B3, B4, B5, B6, B7, B10, BQA
Property Description
  • Landsat Scene ID
  • Must be in the Earth Engine format (e.g. LC08_044033_20170716)
  • Used to lookup the scene specific c-factor
system:time_start Image datetime in milliseconds since 1970
  • Used to determine which Landsat type
  • Must be: LANDSAT_5, LANDSAT_7, or LANDSAT_8

Model Output

The primary output of the SSEBop model is the fraction of reference ET (ETf). The actual ET (ETa) can then be computed by multiplying the Landsat-based ETf image with the reference ET (e.g. ETr from GRIDMET).


import openet.ssebop as ssebop

landsat_img = ee.Image('LANDSAT/LC08/C01/T1_RT_TOA/LC08_044033_20170716')
et_fraction = ssebop.Image.from_landsat_c1_toa(landsat_img).et_fraction
et_reference = ee.Image('IDAHO_EPSCOR/GRIDMET/20170716').select('eto')
et_actual = et_fraction.multiply(et_reference)

Custom Input Image

SSEBop images can also be built manually by instantiating the class with an ee.Image with the following bands: ‘lst’ (land surface temperature [K]) and ‘ndvi’ (normalized difference vegetation index). The input image must have ‘system:index’ and ‘system:time_start’ properties (described above).

import openet.ssebop as ssebop

input_img = ee.Image([ee.Image(lst), ee.Image(ndvi)]) \
    .rename(['lst', 'ndvi']) \
        'system:index': 'LC08_044033_20170716',
        'system:time_start': ee.Date.fromYMD(2017, 7, 16).millis()})
et_fraction = ssebop.Image(input_img).et_fraction

Example Notebooks

Detailed Jupyter Notebooks of the various approaches for calling the OpenET SSEBop model are provided in the “examples” folder.

Ancillary Datasets

Maximum Daily Air Temperature (Tmax)

The daily maximum air temperature (Tmax) is essential for establishing the maximum ET limit (cold boundary) as explained in Senay2017.

Default Asset ID: projects/usgs-ssebop/tmax/daymet_median_v2

Land Surface Temperature

Land Surface Temperature (LST) is currently calculated in the SSEBop approach from Landsat Top-of-Atmosphere images by including commonly used calibration steps and atmospheric correction techniques. These include calculations for: (1) spectral radiance conversion to the at-sensor brightness temperature; (2) atmospheric absorption and re-emission value; and (3) surface emissivity. For additional information, users can refer to section 3.2 of the Methodology in Senay2016.


The SSEBop ET model uses dT as a predefined temperature difference between Thot and Tcold for each pixel. In SSEBop formulation, hot and cold limits are defined on the same pixel; therefore, dT actually represents the vertical temperature difference between the surface temperature of a theoretical bare/dry condition of a given pixel and the air temperature at the canopy level of the same pixel as explained in Senay2013. The input dT is calculated under average-sky conditions and assumed not to change from year to year, but is unique for each day and location.

Default Asset ID: projects/usgs-ssebop/dt/daymet_median_v0


The default elevation dataset is the USGS SRTM global image asset.

Default Asset ID: USGS/SRTMGL1_003

The elevation parameter will accept any Earth Engine image.

Tcorr (C-factor)

In order to correspond the maximum air temperature with cold/wet limiting environmental conditions, the SSEBop model uses a correction coefficient (C-factor) uniquely calculated for each Landsat scene from well-watered/vegetated pixels. This temperature correction component is based on a ratio of Tmax and Land Surface Temperature (LST) that has passed through several conditions such as NDVI limits.


The Tcorr value is read from precomputed Earth Engine feature/image collections based on the Landsat scene ID (from the system:index property). If the target Landsat scene ID is not found in the Tcorr collection, a median monthly value for the WRS2 path/row is used. If median monthly values have not been computed for the target path/row, a default value of 0.978 will be used.

The Tcorr is a function of the maximum air temperature dataset, so separate Tcorr collections have been generated for each of the following air temperature datasets: CIMIS, DAYMET, GRIDMET, TopoWX. The data source of the Tcorr collection needs to match the data source of the air temperature.

The Tcorr collections were last updated through 2018 but will eventually be updated daily.

Default Asset IDs

Scene ID: projects/usgs-ssebop/tcorr_scene/daymet_median_v2_scene

Monthly ID: projects/usgs-ssebop/tcorr_scene/daymet_median_v2_monthly


The OpenET SSEBop python module can be installed via pip:

pip install openet-ssebop

OpenET Namespace Package

Each OpenET model is stored in the “openet” folder (namespace). The model can then be imported as a “dot” submodule of the main openet module.

import openet.ssebop as ssebop

Development and Testing

Please see the CONTRIBUTING.rst.


Senay, G., Bohms, S., Singh, R., Gowda, P., Velpuri, N., Alemu, H., Verdin, J. (2013). Operational Evapotranspiration Mapping Using Remote Sensing and Weather Datasets: A New Parameterization for the SSEB Approach. Journal of the American Water Resources Association, 49(3).
Senay, G., Friedrichs, M., Singh, R., Velpui, N. (2016). Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin. Remote Sensing of Environment, 185.
Senay, G., Schauer, M., Friedrichs, M., Manohar, V., Singh, R. (2017). Satellite-based water use dynamics using historical Landsat data (1984-2014) in the southwestern United States. Remote Sensing of Environment, 202.
Senay, G. (2018). Satellite Psychrometric Formulation of the Operational Simplified Surface Energy Balance (SSEBop) Model for Quantifying and Mapping Evapotranspiration. Applied Engineering in Agriculture, 34(3).
Senay, G., Schauer, M., Velpuri, N.M., Singh, R.K., Kagone, S., Friedrichs, M., Litvak, M.E., Douglas-Mankin, K.R. (2019). Long-Term (1986–2015) Crop Water Use Characterization over the Upper Rio Grande Basin of United States and Mexico Using Landsat-Based Evapotranspiration. Remote Sensing, 11(13):1587.
Schauer, M.,Senay, G. (2019). Characterizing Crop Water Use Dynamics in the Central Valley of California Using Landsat-Derived Evapotranspiration. Remote Sensing, 11(15):1782.

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