Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/davemlz/eemont

A python package that extends Google Earth Engine.
https://github.com/davemlz/eemont

earth-engine geographic-information-systems gis google google-earth-engine python python3 raster remote-sensing satellite-imagery satellite-images spectral-indices

Last synced: 4 days ago
JSON representation

A python package that extends Google Earth Engine.

Awesome Lists containing this project

README 

        


  header





    A python package that extends Google Earth Engine







    PyPI





    conda-forge





    License





    Documentation Status





    Tests





    GitHub Sponsors





    Buy me a coffee





    Ko-fi





    GEE Community





    Twitter





    JOSS





    Black





    isort






---



**GitHub**: [https://github.com/davemlz/eemont](https://github.com/davemlz/eemont)



**Documentation**: [https://eemont.readthedocs.io/](https://eemont.readthedocs.io/)



**PyPI**: [https://pypi.org/project/eemont/](https://pypi.org/project/eemont/)



**Conda-forge**: [https://anaconda.org/conda-forge/eemont](https://anaconda.org/conda-forge/eemont)



**Tutorials**: [https://github.com/davemlz/eemont/tree/master/docs/tutorials](https://github.com/davemlz/eemont/tree/master/docs/tutorials)



**Paper**: [https://joss.theoj.org/papers/10.21105/joss.03168](https://joss.theoj.org/papers/10.21105/joss.03168)



---



## Overview



[Google Earth Engine](https://earthengine.google.com/) is a cloud-based service for 

geospatial processing of vector and raster data. The Earth Engine platform has a 

[JavaScript and a Python API](https://developers.google.com/earth-engine/guides) with 

different methods to process geospatial objects. Google Earth Engine also provides a 

[HUGE PETABYTE-SCALE CATALOG](https://developers.google.com/earth-engine/datasets/) of 

raster and vector data that users can process online (e.g. Landsat Missions Image 

Collections, Sentinel Missions Image Collections, MODIS Products Image Collections, World 

Database of Protected Areas, etc.). The eemont package extends the 

[Google Earth Engine Python API](https://developers.google.com/earth-engine/guides/python_install) 

with pre-processing and processing tools for the most used satellite platforms by adding 

utility methods for different 

[Earth Engine Objects](https://developers.google.com/earth-engine/guides/objects_methods_overview) 

that are friendly with the Python method chaining.



## Google Earth Engine Community: Developer Resources



The eemont Python package can be found in the 

[Earth Engine Community: Developer Resources](https://developers.google.com/earth-engine/tutorials/community/developer-resources) 

together with other awesome resources such as [geemap](https://geemap.org/) and 

[rgee](https://github.com/r-spatial/rgee).



## How does it work?



The eemont python package extends the following Earth Engine classes:



- [ee.Feature](https://developers.google.com/earth-engine/guides/features)

- [ee.FeatureCollection](http://developers.google.com/earth-engine/guides/feature_collections)

- [ee.Geometry](https://developers.google.com/earth-engine/guides/geometries)

- [ee.Image](https://developers.google.com/earth-engine/guides/image_overview)

- [ee.ImageCollection](https://developers.google.com/earth-engine/guides/ic_creating)

- [ee.List](https://developers.google.com/earth-engine/guides/objects_methods_overview)

- [ee.Number](https://developers.google.com/earth-engine/guides/objects_methods_overview)



New utility methods and constructors are added to above-mentioned classes in order

to create a more fluid code by being friendly with the Python method chaining. These

methods are mandatory for some pre-processing and processing tasks (e.g. clouds masking,

shadows masking, image scaling, spectral indices computation, etc.), and they are

presented as simple functions that give researchers, students and analysts the chance to

analyze data with far fewer lines of code.



Look at this simple example where a

[Sentinel-2 Surface Reflectance Image Collection](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR)

is pre-processed and processed in just one step:



```python

import ee, eemont

   

ee.Authenticate()

ee.Initialize()



point = ee.Geometry.PointFromQuery(

    'Cali, Colombia',

    user_agent = 'eemont-example'

) # Extended constructor



S2 = (ee.ImageCollection('COPERNICUS/S2_SR')

    .filterBounds(point)

    .closest('2020-10-15') # Extended (pre-processing)

    .maskClouds(prob = 70) # Extended (pre-processing)

    .scaleAndOffset() # Extended (pre-processing)

    .spectralIndices(['NDVI','NDWI','BAIS2'])) # Extended (processing)

```



And just like that, the collection was pre-processed, processed and ready to be analyzed!



## Installation



Install the latest version from PyPI:



```

pip install eemont

```



Upgrade `eemont` by running:



```

pip install -U eemont

```



Install the latest version from conda-forge:



```

conda install -c conda-forge eemont

```



Install the latest dev version from GitHub by running:



```

pip install git+https://github.com/davemlz/eemont

```



## Features



Let's see some of the main features of eemont and how simple they are compared to the GEE

Python API original methods:



### Overloaded Operators



The following operators are overloaded: +, -, \*\, /, //, %, \**\ , <<, >>, &, \|\, <, <=,

==, !=, >, >=, -, ~. (and you can avoid the `ee.Image.expression()` method!)



 GEE Python API 

 eemont-style 



  

``` python

ds = 'COPERNICUS/S2_SR'

          

S2 = (ee.ImageCollection(ds)

.first())



def scaleImage(img):

    scaling = img.select('B.*')

    x = scaling.multiply(0.0001)

    scaling = img.select(['AOT','WVP'])

    scaling = scaling.multiply(0.001)

    x = x.addBands(scaling)

    notScaling = img.select([

        'SCL',

        'TCI.*',

        'MSK.*',

        'QA.*'

    ]))

    return x.addBands(notScaling)

    

S2 = scaleImage(S2)



exp = '2.5*(N-R)/(N+(6*R)-(7.5*B)+1)'



imgDict = {

'N': S2.select('B8'),

'R': S2.select('B4'),

'B': S2.select('B2')

}



EVI = S2.expression(exp,imgDict)

```



``` python

ds = 'COPERNICUS/S2_SR'

          

S2 = (ee.ImageCollection(ds)

.first()

.scale())



N = S2.select('B8')

R = S2.select('B4')

B = S2.select('B2')



EVI = 2.5*(N-R)/(N+(6*R)-(7.5*B)+1)

```



### Clouds and Shadows Masking



Masking clouds and shadows can be done using eemont with just one method: `maskClouds()`!



 GEE Python API 

 eemont-style 



  

``` python

ds = 'LANDSAT/LC08/C01/T1_SR'

          

def maskCloudsShadows(img):

    c = (1 << 3)

    s = (1 << 5)

    qa = 'pixel_qa'

    qa = img.select(qa)

    cm = qa.bitwiseAnd(c).eq(0)

    sm = qa.bitwiseAnd(s).eq(0)

    mask = cm.And(sm)

    return img.updateMask(mask)

    

(ee.ImageCollection(ds)

    .map(maskCloudsShadows))

```



``` python

ds = 'LANDSAT/LC08/C01/T1_SR'

          

(ee.ImageCollection(ds)

    .maskClouds())

```



### Image Scaling and Offsetting



Scaling and offsetting can also be done using eemont with just one method: `scale()`!



 GEE Python API 

 eemont-style 



  

``` python

def scaleBands(img):

    scaling = img.select([

    'NDVI',

    'EVI',

    'sur.*'

    ])

    x = scaling.multiply(0.0001)

    scaling = img.select('.*th')

    scaling = scaling.multiply(0.01)

    x = x.addBands(scaling)

    notScaling = img.select([

    'DetailedQA',

    'DayOfYear',

    'SummaryQA'

    ])

    return x.addBands(notScaling)              



ds = 'MODIS/006/MOD13Q1'



(ee.ImageCollection(ds)

    .map(scaleBands))

```



``` python

ds = 'MODIS/006/MOD13Q1'

          

(ee.ImageCollection(ds)

    .scaleAndOffset())

```



### Complete Preprocessing



The complete preprocessing workflow (Masking clouds and shadows, and image scaling and

offsetting) can be done using eemont with just one method: `preprocess()`!



 GEE Python API 

 eemont-style 



  

``` python

ds = 'LANDSAT/LC08/C01/T1_SR'

          

def maskCloudsShadows(img):

    c = (1 << 3)

    s = (1 << 5)

    qa = 'pixel_qa'

    qa = img.select(qa)

    cm = qa.bitwiseAnd(c).eq(0)

    sm = qa.bitwiseAnd(s).eq(0)

    mask = cm.And(sm)

    return img.updateMask(mask)

    

def scaleBands(img):

    scaling = img.select('B[1-7]')

    x = scaling.multiply(0.0001)

    scaling = img.select([

    'B10','B11'

    ])

    scaling = scaling.multiply(0.1)

    x = x.addBands(scaling)

    notScaling = img.select([

    'sr_aerosol',

    'pixel_qa',

    'radsat_qa'

    ])

    return x.addBands(notScaling)

    

(ee.ImageCollection(ds)

    .map(maskCloudsShadows)

    .map(scaleBands))

```



``` python

ds = 'LANDSAT/LC08/C01/T1_SR'

          

(ee.ImageCollection(ds)

    .preprocess())

```



### Spectral Indices



Do you need to compute several spectral indices? Use the `spectralIndices()` method! The

indices are taken from [Awesome Spectral Indices](https://github.com/davemlz/awesome-spectral-indices).



 GEE Python API 

 eemont-style 



  

``` python

ds = 'LANDSAT/LC08/C01/T1_SR'

          

def scaleImage(img):

    scaling = img.select('B[1-7]')

    x = scaling.multiply(0.0001)

    scaling = img.select(['B10','B11'])

    scaling = scaling.multiply(0.1)

    x = x.addBands(scaling)

    notScaling = img.select([

        'sr_aerosol',

        'pixel_qa',

        'radsat_qa'

    ]))

    return x.addBands(notScaling)



def addIndices(img):

    x = ['B5','B4']

    a = img.normalizedDifference(x)

    a = a.rename('NDVI')

    x = ['B5','B3']

    b = img.normalizedDifference(x)

    b = b.rename('GNDVI')

    x = ['B3','B6']

    c = img.normalizedDifference(x)

    c = b.rename('NDSI')

    return img.addBands([a,b,c])                    



(ee.ImageCollection(ds)

    .map(scaleImage)

    .map(addIndices))

```



``` python

ds = 'LANDSAT/LC08/C01/T1_SR'

          

(ee.ImageCollection(ds)

    .scaleAndOffset()

    .spectralIndices([

        'NDVI',

        'GNDVI',

        'NDSI'])

)

```



The list of available indices can be retrieved by running:



``` python 

eemont.listIndices()

```



Information about the indices can also be checked:



``` python 

indices = eemont.indices() 

indices.BAIS2.formula

indices.BAIS2.reference

```



### Closest Image to a Specific Date



Struggling to get the closest image to a specific date? Here is the solution: the

`closest()` method!



 GEE Python API 

 eemont-style 



  

``` python

ds = 'COPERNICUS/S5P/OFFL/L3_NO2'

          

xy = [-76.21, 3.45]

poi = ee.Geometry.Point(xy)



date = ee.Date('2020-10-15')

date = date.millis()



def setTimeDelta(img):              

    prop = 'system:time_start'

    prop = img.get(prop)

    prop = ee.Number(prop)              

    delta = prop.subtract(date)

    delta = delta.abs()              

    return img.set(

    'dateDist',

    delta)                     



(ee.ImageCollection(ds)

    .filterBounds(poi)

    .map(setTimeDelta)

    .sort('dateDist')

    .first())

```



``` python

ds = 'COPERNICUS/S5P/OFFL/L3_NO2'

          

xy = [-76.21, 3.45]

poi = ee.Geometry.Point(xy)



(ee.ImageCollection(ds)

    .filterBounds(poi)

    .closest('2020-10-15'))

```



### Time Series By Regions



The JavaScript API has a method for time series extraction (included in the `ui.Chart`

module), but this method is missing in the Python API... so, here it is!



PD: Actually, there are two methods that you can use: `getTimeSeriesByRegion()` and

`getTimeSeriesByRegions()`!



``` python

f1 = ee.Feature(ee.Geometry.Point([3.984770,48.767221]).buffer(50),{'ID':'A'})

f2 = ee.Feature(ee.Geometry.Point([4.101367,48.748076]).buffer(50),{'ID':'B'})

fc = ee.FeatureCollection([f1,f2])



S2 = (ee.ImageCollection('COPERNICUS/S2_SR')

    .filterBounds(fc)

    .filterDate('2020-01-01','2021-01-01')

    .maskClouds()

    .scaleAndOffset()

    .spectralIndices(['EVI','NDVI']))



# By Region

ts = S2.getTimeSeriesByRegion(reducer = [ee.Reducer.mean(),ee.Reducer.median()],

                                geometry = fc,

                                bands = ['EVI','NDVI'],

                                scale = 10)



# By Regions

ts = S2.getTimeSeriesByRegions(reducer = [ee.Reducer.mean(),ee.Reducer.median()],

                                collection = fc,

                                bands = ['EVI','NDVI'],

                                scale = 10)

```



### Constructors by Queries



Don't you have the coordinates of a place? You can construct them by using queries!



``` python

usr = 'my-eemont-query-example'

   

seattle_bbox = ee.Geometry.BBoxFromQuery('Seattle',user_agent = usr)

cali_coords = ee.Feature.PointFromQuery('Cali, Colombia',user_agent = usr)

amazonas_river = ee.FeatureCollection.MultiPointFromQuery('Río Amazonas',user_agent = usr)

```



### JavaScript Modules



This is perhaps the most important feature in `eeExtra`! What if you could use a

JavaScript module (originally just useful for the Code Editor) in python or R? Well,

wait no more for it!



 JS (Code Editor) 

 Python (eemont) 

 R (rgee+) 



  

``` javascript

var usr = 'users/sofiaermida/'

var rep = 'landsat_smw_lst:'

var fld = 'modules/'

var fle = 'Landsat_LST.js'

var pth = usr+rep+fld+fle

var mod = require(pth)

var LST = mod.collection(

    ee.Geometry.Rectangle([

        -8.91,

        40.0,

        -8.3,

        40.4

    ]),

    'L8',

    '2018-05-15',

    '2018-05-31',

    true

)

```



  

``` python

import ee, eemont

ee.Initialize()

usr = 'users/sofiaermida/'

rep = 'landsat_smw_lst:'

fld = 'modules/'

fle = 'Landsat_LST.js'

pth = usr+rep+fld+fle

ee.install(pth)

mod = ee.require(pth)

LST = mod.collection(

    ee.Geometry.Rectangle([

        -8.91,

        40.0,

        -8.3,

        40.4

    ]),

    'L8',

    '2018-05-15',

    '2018-05-31',

    True

)

```



``` r

library(rgee)

library(rgeeExtra)

ee_Initialize()

usr <- 'users/sofiaermida/'

rep <- 'landsat_smw_lst:'

fld <- 'modules/'

fle <- 'Landsat_LST.js'

pth <- paste0(usr,rep,fld,fle)

mod <- ee$require(pth)

LST = mod$collection(

    ee$Geometry$Rectangle(c(

        -8.91,

        40.0,

        -8.3,

        40.4

    )),

    'L8',

    '2018-05-15',

    '2018-05-31',

    TRUE

)

```



## License



The project is licensed under the MIT license.



## How to cite



Do you like using eemont and think it is useful? Share the love by citing it!:



```

Montero, D., (2021). eemont: A Python package that extends Google Earth Engine. 

Journal of Open Source Software, 6(62), 3168, https://doi.org/10.21105/joss.03168

```

   

If required, here is the BibTex!:



```

@article{Montero2021,

    doi = {10.21105/joss.03168},

    url = {https://doi.org/10.21105/joss.03168},

    year = {2021},

    publisher = {The Open Journal},

    volume = {6},

    number = {62},

    pages = {3168},

    author = {David Montero},

    title = {eemont: A Python package that extends Google Earth Engine},

    journal = {Journal of Open Source Software}

}

```



## Artists



- [David Montero Loaiza](https://github.com/davemlz): Lead Developer of eemont and eeExtra.

- [César Aybar](https://github.com/csaybar): Lead Developer of rgee and eeExtra.

- [Aaron Zuspan](https://github.com/aazuspan): Plus Codes Constructors and Methods, Panchromatic Sharpening and Histogram Matching Developer.



## Credits



Special thanks to [Justin Braaten](https://github.com/jdbcode) for featuring eemont in

tutorials and the GEE Community: Developer Resources Page, to

[César Aybar](https://github.com/csaybar) for the formidable help with Awesome Spectral

Indices and to the JOSS Review Team ([Katy Barnhart](https://github.com/kbarnhart),

[Jayaram Hariharan](https://github.com/elbeejay), [Qiusheng Wu](https://github.com/giswqs)

and [Patrick Gray](https://github.com/patrickcgray)) for the comments, suggestions and contributions!