Updated README to include TSEB and new notebooks.

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README.md

@@ -7,62 +7,111 @@
 
 **Evapotranspiration (ET) models for use in python and with integration into Google Earth Engine.**
 
-geeet aims to provide users with hybrid evapotranspiration (ET) models that run with numerical values and with Google Earth Engine images. 
+*geeet* provides hybrid evapotranspiration (ET) models that run with numerical values and with Google Earth Engine images. 
 
 - GitHub repo: https://github.com/kaust-halo/geeet
 - PyPI: https://pypi.org/project/geeet/
 - Conda-forge: https://anaconda.org/conda-forge/geeet
 - Free software: MIT license
 
-## Features
+## Introduction
 
-This initial release features a PT-JPL model adapted for arid environments (as described in Aragon et al., 2018). A notebook example is included [here](./examples/notebooks/01_PTJPL.ipynb). 
+*geeet* is a Python package providing a common set of building blocks for estimating evapotranspiration (ET) from remote sensing observations. It also features complete ET models such as [PT-JPL](https://doi.org/10.1016/j.rse.2007.06.025) and [TSEB](https://doi.org/10.1016/0168-1923(95)02265-Y). All modules in *geeet* are designed to work with the input data provided in two formats: (1) as [numpy ndarrays](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) and (2) as [Google Earth Engine](https://earthengine.google.com/) (GEE) [images](https://developers.google.com/earth-engine/apidocs/ee-image). GEE is a cloud-based platform dedicated to Earth observation research that offers a [multi-petabyte catalogue](https://developers.google.com/earth-engine/datasets/) of geospatial data. Importantly, GEE offers cloud computing capabilities, which means that a user can interact with this geospatial data directly without having to download or process any data on premises. Access to these cloud-based services requires [signing up for a GEE account](https://earthengine.google.com/signup/). The *geeet* Python package was created to offer ET modeling tools that work for any user, whether they have a GEE account or not. For this reason, numpy is the only requirement for *geeet*. 
 
-### PT-JPL model for arid environments (as described in [Aragon et al., 2018](http://dx.doi.org/10.3390/rs10121867))   
+## Installation
 
-To run the PT-JPL model (arid environments) for a single site, run:
+*geeet* is available on the [Python Package Index (pip)](https://pypi.org/project/geeet/). To install *geeet* using pip, run this command:
 
-```python
-from geeet.ptjpl import ptjpl_arid
-ET = ptjpl_arid(RH=25.3, Temp_C=24.3442, Press=94.524, Rn=374.8117, NDVI=0.7588, F_aparmax=0.7295, doy=105, time = 11, longitude = 38.4381)  
-# eot_params: Day of year, time, longitude, standard meridian
+```
+pip install geeet
 ```
 
-To run the same model using a *Google Earth Engine image*, run:
+If you want to install the latest development version hosted on github, run this command:
 
-```python
-from geeet.ptjpl import ptjpl_arid
-ET = ptjpl_arid(img)
 ```
-where img is a `ee.Image` and all required inputs are given as bands within `img`.  
+pip install git+https://github.com/kaust-halo/geeet
+```
+
+*geeet* is alsao available on [conda-forge](https://anaconda.org/conda-forge/geeet). To install *geeet* using conda, run this command:
+
+```
+conda install -c conda-forge geeet
+```
+
+The only requirement is a modern python installation (e.g. >3.6) and [numpy](https://numpy.org/). However, to test any of the *GEE* capabilities of *geeet* you will need to install the Python earthengine API (available through [pip](https://pypi.org/project/earthengine-api/) and [conda](https://anaconda.org/conda-forge/earthengine-api)), and have a [GEE account](https://earthengine.google.com/signup/). 
+
+## First time use: running a toy example
+
+If you have a GEE account and the earthengine API installed, we recommend first taking a look at [this notebook](https://github.com/kaust-halo/geeet/blob/main/examples/notebooks/01_geeet.ipynb) demonstrating the basic use of the hybrid ET models with a simple toy example. In a nutshell, running one of the pre-built models can be done in two lines of code, e.g.:
+
+```
+from geeet.tseb import import tseb_series
+et_tseb = tseb_series(img = sample_tseb_inputs) 
+```
 
-For more information, see the [notebook example](./examples/notebooks/01_PTJPL.ipynb). 
+where [sample_tseb_inputs] is a [ee.Image] containing all the necessary inputs for the TSEB model. You can use the same function to run the same model with numpy arrays:
 
-This function can also be mapped to an `ee.ImageCollection`:
+```
+et_tseb_out = tseb_series(\
+    Tr = Tr, Alb = albedo, NDVI = NDVI, P = P, Ta = Ta, U = U, \
+    Sdn = Sdn, Ldn = Ldn, doy = doy, time = Time, Vza = Vza,\
+    longitude = lon, latitude = lat, zU = zU, zT = zT)
+```
+
+where in this case you supply all the inputs as numpy arrays through keyword arguments.
+
+> The keyword argument `img` is reserved only for GEE images. If `img` is supplied and is an instance of `ee.Image`, all other keyword arguments are ignored (with exceptions for some scalar parameters. Check the docstrings for each model). 
+
+*geeet* models can also be mapped to an [`ee.ImageCollection`](https://developers.google.com/earth-engine/guides/ic_creating), e.g.:
 
 ```python
 from geeet.ptjpl import ptjpl_arid
 et_outputs = et_inputs.map(ptjpl_arid)
 ```
-where `et_inputs` is an `ee.ImageCollection` with the required inputs (see the [notebook example](./examples/notebooks/02_PTJPL_collection.ipynb)). 
+where `et_inputs` is an `ee.ImageCollection` with the required inputs.
 
-## Installation
+## PT-JPL model for arid environments (as described in [Aragon et al., 2018](http://dx.doi.org/10.3390/rs10121867))
 
-`pip install geeet`
+For a simple on-premises test of this PT-JPL model, run:
+```python
+from geeet.ptjpl import ptjpl_arid
+ptjpl_arid(Ta=25+273, P = 95500, NDVI = 0.75, F_aparmax=0.73, Rn=487.87, RH=25, doy=1, time=11, longitude=38.25)
+```
 
-or
+The [second notebook example](./examples/notebooks/02_demo_using_GEE_data.ipynb) is a self-contained, more realistic example that demonstrates the use of real GEE datasets with this PT-JPL model. 
 
-`conda install -c conda-forge geeet`
+You can preview a pre-processed output of this example [here](https://code.earthengine.google.com/?scriptPath=users%2Flopezvoliver%2Fgeeet%3Aptjpl_sample_outputs_coarse) (requires a GEE account).  
+
+## Two-source Energy Balance model ([TSEB](https://doi.org/10.1016/0168-1923(95)02265-Y))
+
+*geeet* includes a two-source energy balance model mostly based on the original parameterizations of [Norman et al., 1995](https://doi.org/10.1016/0168-1923(95)02265-Y). Specifically, it initializes the estimates of the temperatures of the soil and the canopy layers using a Priestley-Taylor equation. It then iteratively updates the temperatures, energy fluxes, and resistance values using the in-series resistance network parameterization. 
+
+For a simple on-premises test of this TSEB model, run:
+
+```python
+from geeet.tseb import tseb_series
+tseb_series(Tr = 26+273, Alb = 0.22, NDVI = 0.75, P=95500, Ta = 25+273, U=2, Sdn=745, Ldn=345, doy=1, time=11, Vza=0, longitude=38.25, latitude=30.25, zU=10, zT=2)
+```
+
+For a more realistic example using GEE data, see the following:
+1. [This notebook](./examples/notebooks/03_prepare_ECMWF_L8_inputs_for_TSEB.ipynb) demonstrates the inputs data setup using [Landsat-8 satellite imagery](https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2#bands) and meteorological data from the [ECMWF ERA5 reanalysis](https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY#bands). The resulting image contains all the inputs necessary to run the TSEB model and is available as a [public GEE asset](https://code.earthengine.google.com/?asset=projects/geeet-public/assets/TSEB_sample_inputs) and can be visualized with this [sample GEE code editor script](https://code.earthengine.google.com/?scriptPath=users%2Flopezvoliver%2Fgeeet%3Atseb_sample_inputs_vis).  
+2. [This notebook](./examples/notebooks/04_TSEB_crop_water_use.ipynb) shows how to run the TSEB model using the sample GEE TSEB inputs asset. The resulting image contains all the inputs as well as the outputs of the TSEB model and is available as a [public GEE asset](https://code.earthengine.google.com/?asset=projects/geeet-public/assets/TSEB_sample_outputs). It can also be visualized with this [sample GEE code editor script](https://code.earthengine.google.com/?scriptPath=users%2Flopezvoliver%2Fgeeet%3Atseb_sample_outputs_vis). 
+3. Finally, [this notebook](./examples/notebooks/05_TSEB_on_premises.ipynb) demonstrates running the TSEB model using the same data but as numpy arrays. To do this, the sample inputs image was exported to a [GeoTIFF file](./examples/data/tseb_sample_inputs.tif). It also compares the output of the "on-premises" model to the output from the GEE model run (the outputs image was also exported as a [GeoTIFF file](./examples/data/tseb_sample_outputs.tif)).
 
 ## References
 
 References for each model are found in [REFERENCES.txt](REFERENCES.txt). The source code for each module contains references for each function as well. Finally, each model contains two functions to display the references: `cite()` shows the main citation for the model, while `cite_all()` shows all the references for that model.
 
-If you use this package for research, please cite the relevant model. 
+If you use this package for research, please cite the relevant model references. 
 
 ## Contributions
 
-Contributions are welcome. We aim to include as many ET models to allow researchers to intercompare the different models. 
+Contributions are welcome. Please [open an issue](https://github.com/kaust-halo/geeet/issues) to:
+- suggest a relevant ET model 
+- report a bug
+- report an issue
+
+You can also submit a [pull requests](https://github.com/kaust-halo/geeet/pulls) if you have another working ET model - at least with numpy arrays as inputs. 
 
 ## Credits