WARNING: This code is in development, is being provided without support, and is subject to change at any time without notification
This repository provides an Earth Engine Python API based implementation of the SIMS model for computing evapotranspiration (ET).
The SIMS model is currently implemented for the following Earth Engine image collections:
- Landsat SR
- LANDSAT/LC09/C02/T1_L2
- LANDSAT/LC08/C02/T1_L2
- LANDSAT/LE07/C02/T1_L2
- LANDSAT/LT05/C02/T1_L2
- LANDSAT/LT04/C02/T1_L2
The primary way of interact with the SIMS model are through the "Collection" and "Image" classes.
The Collection class should be used to generate image collections of ET (and and other model Variables). These collections can be for image "overpass" dates only or interpolated to daily, monthly, or annual time steps. The collections can be built for multiple input collections types, such as merging Landsat 8 and Sentinel 2.
The Collection class is built based on a list of input collections ID's, a date range, and a study area geometry.
- collections
- List of Earth Engine collection IDs (see Input Collections).
- start_date
- ISO format start date string (i.e. YYYY-MM-DD) that is passed directly to the collection .filterDate() calls.
- end_date
- ISO format end date string that is passed directly to .filterDate() calls. The end date must be exclusive (i.e. data will go up to this date but not include it).
- geometry
- ee.Geometry() that is passed to the collection .filterBounds() calls. All images with a footprint that intersects the geometry will be included.
- et_reference_source
- Reference ET source collection ID (see Reference ET Sources).
- et_reference_band
- Reference ET source band name.
- cloud_cover_max
- Maximum cloud cover percentage. The input collections will be filtered to images with a cloud cover percentage less than this value. Optional, the default is 70 (%).
- filter_args
- Custom filter arguments for teh input collections. This parameter is not yet fully implemented.
- model_args
- A dictionary of argument to pass through to the Image class initialization. This parameter is not yet fully implemented.
- variables
- List of variables to calculate/return.
- variables
- List of variables to calculate/return.
- t_interval
- Time interval over which to interpolate and aggregate values. Choices: 'daily', 'monthly', 'annual', 'custom' Optional, the default is 'custom'.
- interp_method
- Interpolation method. Choices: 'linear' Optional, the default is 'linear'.
- interp_days
- Number of extra days before the start date and after the end date to include in the interpolation calculation. Optional, the default is 32.
import openet.sims as model
overpass_coll = model.Collection(
collections=['LANDSAT/LC08/C02/T1_L2'],
start_date='2017-06-01',
end_date='2017-09-01',
geometry=ee.Geometry.Point(-121.5265, 38.7399),
et_reference_source='IDAHO_EPSCOR/GRIDMET',
et_reference_band='eto') \
.overpass(variables=['et', 'et_reference', 'et_fraction'])
monthly_coll = model.Collection(
collections=['LANDSAT/LC08/C02/T1_L2'],
start_date='2017-06-01',
end_date='2017-09-01',
geometry=ee.Geometry.Point(-121.5265, 38.7399),
et_reference_source='IDAHO_EPSCOR/GRIDMET',
et_reference_band='eto') \
.interpolate(variables=['et', 'et_reference', 'et_fraction']
t_interval='monthly')The Image class should be used to process a single image, an image collection with custom filtering, or to apply custom parameters to each image in a collection.
Typically the SIMS Image is initialized using one of the collection/sensor specific helper methods listed below (see below). These methods rename the bands to a common naming scheme, apply basic cloud masking, and .
Image collections can be built by mapping one of the helper methods over an image collection. Please see the Image Mapping example notebook for more details.
The Image class can also be initialized using any Earth Engine image with an 'ndvi' band and a 'system:time_start' property.
To instantiate the class for a Landsat Collection 2 SR image, use the Image.from_landsat_c2_sr() method.
The input Landsat image must have the following bands and properties:
| SATELLITE | Band Names |
|---|---|
| LANDSAT_4 | SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7, SR_B6, QA_PIXEL |
| LANDSAT_5 | SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7, SR_B6, QA_PIXEL |
| LANDSAT_7 | SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7, SR_B6, QA_PIXEL |
| LANDSAT_8 | SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7, SR_B10, QA_PIXEL |
| LANDSAT_9 | SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7, SR_B10, QA_PIXEL |
| Property | Description |
|---|---|
| system:index |
|
| system:time_start | Image datetime in milliseconds since 1970 |
| SPACECRAFT_ID |
|
import openet.sims as model
et_img = model.Image.from_landsat_c2_sr(
ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044033_20170716'),
et_reference_source='IDAHO_EPSCOR/GRIDMET',
et_reference_band='eto').etThe SIMS model can compute the following variables:
- ndvi
- Normalized difference vegetation index [unitless]
- et_fraction
- Fraction of reference ET [unitless]
- et_reference
- Reference ET [mm] (type will depend on Reference ET parameters)
- et
- Actual ET [mm]
There is also a more general "calculate" method that can be used to return a multiband image of multiple variables (see example...)
The reference ET data source is controlled using the "et_reference_source" and "et_reference_band" parameters.
The model is expecting a grass reference ET (ETo) and will not return valid results if an alfalfa reference ET (ETr) is used.
- GRIDMET
- Collection ID: IDAHO_EPSCOR/GRIDMETGrass reference ET band: eto
- Spatial CIMIS
- Collection ID: projects/openet/assets/reference_et/california/cimis/daily/v1Grass reference ET band: eto
Detailed Jupyter Notebooks of the various approaches for calling the OpenET SIMS model are provided in the "examples" folder.
The python OpenET SIMS module can be installed via pip:
pip install openet-simsEach 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.sims as modelPlease see the CONTRIBUTING.rst.
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