Metadata-Version: 2.1
Name: openet-ssebop
Version: 0.1.6
Summary: Earth Engine based SSEBop Model
Home-page: https://github.com/Open-ET/openet-ssebop-beta
Author: Charles Morton
Author-email: charles.morton@dri.edu
License: Apache
Download-URL: https://github.com/Open-ET/openet-ssebop-beta/archive/v0.1.6.tar.gz
Description: ===============
        OpenET - SSEBop
        ===============
        
        |version| |build| |codecov|
        
        **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 <https://earthengine.google.com/>`__ 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 average-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 implementation in Earth Engine:
        
        .. image:: docs/SSEBop_GEE_diagram.jpg
        
        Input Collections
        =================
        
        SSEBop ET can currently be computed for Landsat Collection 2 SR/ST images (where available) and Landsat Collection 1 TOA images from the following Earth Engine image collections:
        
         * LANDSAT/LC08/C02/T1_L2
         * LANDSAT/LE07/C02/T1_L2
         * LANDSAT/LT05/C02/T1_L2
         * LANDSAT/LC08/C01/T1_TOA or LANDSAT/LC08/C01/T1_RT_TOA
         * LANDSAT/LE07/C01/T1_TOA or LANDSAT/LE07/C01/T1_RT_TOA
         * LANDSAT/LT05/C01/T1_TOA
        
        
        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:
        
        =================  ======================================
        SPACECRAFT_ID      Band Names
        =================  ======================================
        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
        =================  =============================================
        system:index       - 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
        SPACECRAFT_ID      - 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).
        
        Example
        -------
        
        .. code-block:: python
        
            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('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).
        
        .. code-block:: python
        
            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
        
        Example Notebooks
        =================
        
        Detailed Jupyter Notebooks of the various approaches for calling the OpenET SSEBop model are provided in the "examples" folder.
        
        + `Computing daily ET for a single Landsat image <examples/single_image.ipynb>`__
        + `Computing a daily ET image collection from Landsat image collection <examples/collection.ipynb>`__
        + `Computing annual ET from a collection <examples/interpolate.ipynb>`__
        
        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_.
        
        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 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
        
        Elevation
        ---------
        The default elevation dataset is the USGS SRTM global image asset.
        
        Default Asset ID: `USGS/SRTMGL1_003 <https://developers.google.com/earth-engine/datasets/catalog/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.
        
        .. image:: docs/Tcorr_table.PNG
        
        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
        
        Installation
        ============
        
        The OpenET SSEBop python module can be installed via pip:
        
        .. code-block:: console
        
            pip install openet-ssebop
        
        Dependencies
        ============
        
         * `earthengine-api <https://github.com/google/earthengine-api>`__
         * `openet-core <https://github.com/Open-ET/openet-core-beta>`__
        
        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.
        
        .. code-block:: console
        
            import openet.ssebop as ssebop
        
        Development and Testing
        =======================
        
        Please see the `CONTRIBUTING.rst <CONTRIBUTING.rst>`__.
        
        References
        ==========
        
        .. _references:
        
        .. [Senay2013]
         | 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).
         | `https://doi.org/10.1111/jawr.12057 <https://doi.org/10.1111/jawr.12057>`__
        .. [Senay2016]
         | 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.
         | `https://doi.org/10.1016/j.rse.2015.12.043 <https://doi.org/10.1016/j.rse.2015.12.043>`__
        .. [Senay2017]
         | 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.
         | `https://doi.org/10.1016/j.rse.2017.05.005 <https://doi.org/10.1016/j.rse.2017.05.005>`__
        .. [Senay2018]
         | 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).
         | `https://doi.org/10.13031/aea.12614 <https://doi.org/10.13031/aea.12614>`__
        .. [Senay2019]
         | 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.
         | `https://doi.org/10.3390/rs11131587 <https://doi.org/10.3390/rs11131587>`__
        .. [Schauer2019]
         | 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.
         | `https://doi.org/10.3390/rs11151782 <https://doi.org/10.3390/rs11151782>`__
        
        .. |build| image:: https://github.com/Open-ET/openet-ssebop-beta/workflows/build/badge.svg
           :alt: Build status
           :target: https://github.com/Open-ET/openet-ssebop-beta
        .. |version| image:: https://badge.fury.io/py/openet-ssebop.svg
           :alt: Latest version on PyPI
           :target: https://badge.fury.io/py/openet-ssebop
        .. |codecov| image:: https://codecov.io/gh/Open-ET/openet-ssebop-beta/branch/master/graphs/badge.svg
           :alt: Coverage Status
           :target: https://codecov.io/gh/Open-ET/openet-ssebop-beta
        
Keywords: SSEBop OpenET Evapotranspiration Earth Engine
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/x-rst
