openet-ssebop 0.4.3

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:

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)

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'])
    .set({
        'system:index': 'LC08_044033_20170716',
        'system:time_start': ee.Date.fromYMD(2017, 7, 16).millis()})
)
et_fraction = ssebop.Image(input_img).et_fraction

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. Support for source options includes CIMIS, GRIDMET, DAYMET, and other custom Image Collections. See the model Image class docstrings for more information.

Default Asset ID: projects/usgs-ssebop/tmax/daymet_v4_mean_1981_2010 (Daily median from 1981-2010)

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_v6

Temperature Correction (c factor)

In order to correspond the maximum air temperature with cold/wet limiting environmental conditions, the SSEBop model uses a temperature correction coefficient (c factor, sometimes labeled interchangeably as Tcorr) uniquely calculated for each Landsat scene. This temperature correction component is uniquely developed for SSEBop using 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.

Note: Tcorr refers to the pixel-based ratio of LST_cold and Tmax while c factor is a statistical value that represents a region such as a 5-km grid size (or larger) value.

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.

The ‘FANO’ parameter (default) can be implemented dynamically for each Landsat scene within the SSEBop Image object using the following Tcorr source:

model_obj = model.Image.from_landsat_c2_sr(
    ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044033_20170716'),
    tcorr_source='FANO',
    )

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.