Earth Engine implementation of the SSEBop model
Project description
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 or ETo). The SSEBop model does not solve all the energy balance terms explicitly; rather, it defines the limiting conditions based on “gray-sky” net radiation balance principles and an air temperature parameter. This approach predefines unique sets of “hot/dry” and “cold/wet” limiting values for each pixel, allowing an operational model setup and a relatively shorter compute time. More information on the GEE implementation of SSEBop is published in Senay2022 and Senay2023 with additional details and model assessment.
Basic SSEBop model implementation in Earth Engine:
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.
Input Collections
SSEBop ET can currently be computed for Landsat Collection 2 Level 2 (SR/ST) images from the following Earth Engine image collections:
LANDSAT/LC09/C02/T1_L2
LANDSAT/LC08/C02/T1_L2
LANDSAT/LE07/C02/T1_L2
LANDSAT/LT05/C02/T1_L2
Note: Users are encouraged to prioritize use of Collection 2 data where available. Collection 1 was produced by USGS until 2022-01-01, and maintained by Earth Engine until 2023-01-01. [More Information]
Landsat Collection 2 SR/ST Input Image
To instantiate the class for a Landsat Collection 2 SR/ST image, use the Image.from_landsat_c2_sr method.
The input Landsat image must have the following bands and properties:
SPACECRAFT_ID |
Band Names |
---|---|
LANDSAT_5 |
SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7, ST_B6, QA_PIXEL |
LANDSAT_7 |
SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7, ST_B6, QA_PIXEL |
LANDSAT_8 |
SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7, ST_B10, QA_PIXEL |
LANDSAT_9 |
SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7, ST_B10, QA_PIXEL |
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).
Example SSEBop ETa from Landsat:
Example
import openet.ssebop as ssebop
landsat_img = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044033_20170716')
et_fraction = ssebop.Image.from_landsat_c2_sr(landsat_img).et_fraction
et_reference = ee.Image('IDAHO_EPSCOR/GRIDMET/20170716').select('etr')
et_actual = et_fraction.multiply(et_reference)
Example Notebooks
Detailed Jupyter Notebooks of the various approaches for calling the OpenET SSEBop model are provided in the “examples” folder.
Ancillary Datasets
Land Surface Temperature (LST)
Land Surface Temperature is currently calculated in the SSEBop approach two ways:
Landsat Collection 2 Level-2 (ST band) images directly. More information can be found at: USGS Landsat Collection 2 Level-2 Science Products
Temperature Difference (dT)
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 Senay2018. The input dT is calculated under “gray-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_v7
Cold Boundary Temperature (Tcold)
In order to determine the theoretical LST corresponding to cold/wet limiting environmental conditions (Tcold), the SSEBop model uses a Forcing and Normalizing Operation (FANO) method, featuring a linear relation between a normalized land surface temperature difference and NDVI difference using the dT parameter and a proportionality constant.
More information on parameter design and model improvements using the FANO method can be found in Senay2023. Additional SSEBop model algorithm theoretical basis documentation can be found here.
model_obj = model.Image.from_landsat_c2_sr(
ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044033_20170716'),
)
The FANO parameterization allows the establishment of the cold boundary condition regardless of vegetation cover density, improving the performance and operational implementation of the SSEBop ET model in sparsely vegetated landscapes, dynamic growing seasons, and varying locations around the world.
Installation
The OpenET SSEBop python module can be installed via pip:
pip install openet-ssebop
Dependencies
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.
References
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