openet-sims 0.2.5

Earth Engine implementation of the SIMS model

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).

Input Collections

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

Model Structure

The primary way of interact with the SIMS model are through the “Collection” and “Image” classes.

Collection

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.

Required Inputs

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.

Optional Inputs

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.

Overpass Method

variables

List of variables to calculate/return.

Interpolate Method

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.

Collection Examples

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')

Image

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.

Landsat Collection 2 Surface Reflectance (SR) Input Image

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	Landsat Scene ID Must be in the Earth Engine format (e.g. LC08_044033_20170716)
system:time_start	Image datetime in milliseconds since 1970
SPACECRAFT_ID	Used to determine which Landsat type (for band renaming) Must be: LANDSAT_4, LANDSAT_5, LANDSAT_7, LANDSAT_8, or LANDSAT_9

Image Example

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').et

Variables

The 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…)

Reference ET

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.

Reference ET Sources

GRIDMET

Collection ID: IDAHO_EPSCOR/GRIDMET

http://www.climatologylab.org/gridmet.html

Grass reference ET band: eto

Spatial CIMIS

Collection ID: projects/openet/assets/reference_et/california/cimis/daily/v1

https://cimis.water.ca.gov/SpatialData.aspx

Grass reference ET band: eto

Example Notebooks

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

• Computing daily ET for a single Landsat image

• Computing a collection of “overpass” ET images

• Computing a collection of interpolated monthly ET images

Installation

The python OpenET SIMS module can be installed via pip:

pip install openet-sims

Dependencies

• earthengine-api

• openet-core

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.sims as model

Development and Testing

Please see the CONTRIBUTING.rst.

References

[Melton2012]

Melton, F., Johnson, L., Lund, C., Pierce, L., Michaelis, A., Hiatt, S., Guzman, A., Adhikari, D., Purdy, A., Rosevelt, C. and Votava, P. (2012). Satellite irrigation management support with the terrestrial observation and prediction system: A framework for integration of satellite and surface observations to support improvements in agricultural water resource management. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(6), pp.1709-1721.

https://doi.org/10.1109/JSTARS.2012.2214474

[Pereira2020]

Pereira L., Paredes P., Melton F., Johnson L., Lopez-Urrea R., Cancela J., Allen R. (2020). Prediction of basal crop coefficients from fraction of ground cover and height. Agricultural Water Management, Special Issue on Updates to the FAO56 Crop Water Requirements Method.

https://doi.org/10.1016/j.agwat.2020.106197