RadGEEToolbox 🛠

🌎 Streamlined Multispectral & SAR Analysis for Google Earth Engine Python API

See documentation here

RadGEEToolbox is an open-source Python package that simplifies the processing and analysis of satellite imagery using the Google Earth Engine Python API. It provides ready-to-use tools for filtering, masking, mosaicking, spectral index calculations, and extracting statistics from multispectral (Landsat, Sentinel-2) and SAR (Sentinel-1) datasets.

Designed for both new and advanced users of Google Earth Engine, RadGEEToolbox minimizes repetitive scripting, accelerates common remote sensing workflows, and aims to maximize efficiency within the constraints of the Google Earth Engine API. Whether you’re building a time series of vegetation indices or extracting surface properties along transects, this package helps get results faster.

Although similar packages exist (eemont, geetools, etc.), RadGEEToolbox extends functionality and provides cohesive, chainable methods for research oriented projects working with Landsat TM & OLI, Sentinel-1 SAR, and/or Sentinel-2 MSI datasets (Table 1). The ultimate goal of RadGEEToolbox is to make satellite image processing easier and faster for real world applications relying on the most commonly utilized remote sensing platforms.

Table 1. Comparison of functionality between RadGEEToolbox, eemont, and geetools.

| Capability | RadGEEToolbox | eemont | geetools | |:--------------:|:---:|:---:|:---:| | Dataset & Workflow Specific API's | YES | NO | NO | | Synthetic Aperture Radar (S1) Support | YES | NO | NO | | Zonal Time-series Extraction | YES | YES | YES | | Area Time-series Extraction | YES | NO | NO | | Transect Time-series Extraction | YES | NO | NO | | Comprehensive Preprocessing Operations | YES | YES | YES | | Reflectance Scaling (DN to ρ) | YES | YES | YES | | Land Surface Temperature Calculation (Landsat) | YES | NO | NO | | Image Selection by Date or Index | YES | YES | NO | | Visualization Presets/Tools | YES | NO | NO |

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Getting Started with Google Earth Engine

RadGEEToolbox requires access to Google Earth Engine (GEE) and proper authentication with the Python API.

For more details, see the official Google Earth Engine Python API Getting Started Guide or Python Installation Guide.

1. Sign Up for Earth Engine

Apply for access here: https://earthengine.google.com/signup/ using a Google account. Approval typically takes a few days.

2. Install the Earth Engine Python API

The Earth Engine API is installed automatically when you install RadGEEToolbox. However, if you would like to install manually:

bash pip install earthengine-api

3. Authenticate & Initialize

Prior to using the Earth Engine Python client library, you need to authenticate and use the resultant credentials to initialize the Python client. The authentication flows use Cloud Projects to authenticate, and they're used for unpaid (free, noncommercial) use as well as paid use.

Run the following during your first use: python import ee ee.Authenticate() NOTE: Your GEE credentials will not permanantly be stored on your PC and you will periodically need to re-run ee.Authenticate() when ee.Initialize() returns an authentication error.

ee.Autheticate() will select the best authentication mode for your environment, and prompt you to confirm access for your scripts. To initialize, you will need to provide a project that you own, or have permissions to use. This project will be used for running all Earth Engine operations:

python ee.Initialize(project='my-project') Replacing 'my-project' with the name of the Google Cloud Project you created on sign-up or any Google Cloud Project that has the GEE API enabled.

4. Authentication Best Practices

It is reccomended to use the following authentication procedure once you have completed your initial authentication: ```python import ee

try: ee.Initialize() except Exception as e: ee.Authenticate() ee.Initialize() ```

5. Troubleshooting

AttributeError: module 'ee' has no attribute 'Initialize'

➤ Ensure that earthengine-api is installed properly.

403 Access Denied

➤ Your GEE account might not be approved, or the API is not enabled for your project.

Initialization fails

➤ Try using ee.Initialize(project='your-project-id') explicitly in case you are just calling ee.Initialize()

See the official GEE documentation for authentication »

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Key Features

• Modular tools for processing Landsat, Sentinel-1 SAR, and Sentinel-2 imagery
• Efficient filtering, masking, and mosaicking of Earth Engine image collections
• Built-in support for computing spectral indices (NDWI, NDVI, LST, turbidity, chlorophyll, etc.)
• SAR utilities for multilooking, speckle filtering, and backscatter conversion
• Automated extraction of transect and zonal statistics across image collections
• Easy conversion between RadGEEToolbox and standard Earth Engine objects
• Server-side–friendly workflows and caching for faster, scalable processing

Features of RadGEEToolbox will be expanded in the future - if there is something you would like to see implemented in RadGEEToolbox, please open an issue or discussion on GitHub.

🔍 For a full breakdown of available tools, see the RadGEEToolbox documentation »

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Installation Instructions

Prerequisites

• Python: Ensure you have version 3.8 or higher installed.
• pip: This is Python's package installer.
• conda-forge: Community led Conda package installer channel

Installing via pip

To install RadGEEToolbox version 1.6.7 using pip (NOTE: it is recommended to create a new virtual environment):

bash pip install RadGEEToolbox==1.6.7

Installing via Conda

To install RadGEEToolbox version 1.6.7 using conda-forge (NOTE: it is recommended to create a new virtual environment):

bash conda install conda-forge::radgeetoolbox

Manual Installation from Source

1. Clone the Repository: bash git clone https://github.com/radwinskis/RadGEEToolbox.git

2. Navigate to Directory: bash cd RadGEEToolbox

3. Install the Package: bash pip install .

✅ Verifying the Installation

To verify that RadGEEToolbox was installed correctly:

python python -c "import RadGEEToolbox; print(RadGEEToolbox.__version__)"

You should see 1.6.7 printed as the version number.

Want to Visualize Data? Install These Too

The core functionality of this package is focused on the processing of geospatial and remote sensing data using Google Earth Engine. It does not include interactive mapping functionality by default.

If you wish to view GEE data on interactive maps (e.g., following along with the mapping portions of the example notebooks), you will need to install the optional geemap package in the environment that RadGEEToolbox is installed:

python pip install geemap or

bash conda install conda-forge::geemap If you plan to use geemap in a Jupyter Notebook, you will also need to install ipykernel:

python pip install ipykernel or

bash conda install anaconda::ipykernel

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Usage Example

Below is an example use case using the LandsatCollection module to create an NDWI image collection, create water classification maps, and create a time series of water area.

1. Create a Filtered Landsat Image Collection

```python

1. Import necessary packages and modules

import ee from RadGEEToolbox import LandsatCollection

2. Authenticate & Initialize GEE API

ee.Authenticate() ee.Initialize(project='my-cloud-project-ID) #replace with your Google Cloud project ID

3. Define study area boundary - in this case Lake Powell, Utah

study_area = ee.Geometry.Polygon( [[[-111.35875055487008, 37.19999663127137], [-111.35875055487008, 37.00119876939416], [-111.12048456365915, 37.00119876939416], [-111.12048456365915, 37.19999663127137]]])

3. Create a Landsat image collection for a given time range and study area

includes filtering imagery based on areal percentage of cloud cover

WRS-2 tile(s) can also be used for filtering instead of an ROI geometry

collection = LandsatCollection( startdate='2020-01-01', #date formats of 'YYYY-MM-DD' enddate='2025-01-01', cloudpercentagethreshold=10, #filtering to <10% cloud coverage boundary=study_area #ee.Geometry() of your study area )

4. Check collection by printing the dates of all images in the collection

dates = collection.dates print(dates) ['2020-01-13', '2020-02-14', '2020-05-04', '2020-05-20', '2020-07-07', '2020-08-08', '2020-08-24', '2020-09-25', '2020-10-11', '2020-10-27', '2020-11-28', '2020-12-30', '2021-01-15', '2021-01-31', '2021-04-05', '2021-05-07', '2021-06-08', '2021-07-10', '2021-08-27', '2021-10-30', '2021-11-10', '2021-11-10', '2021-12-01', '2021-12-17', '2022-01-02', '2022-01-10', '2022-01-26', '2022-02-11', '2022-02-19', '2022-02-27', '2022-03-23', '2022-04-08', '2022-05-02', '2022-05-18', '2022-05-26', '2022-06-11', '2022-06-27', '2022-07-13', '2022-08-06', '2022-08-22', '2022-08-30', '2022-09-07', '2022-09-23', '2022-10-01', '2022-10-09', '2022-10-17', '2022-10-25', '2022-11-10', '2023-01-21', '2023-03-18', '2023-04-11', '2023-06-22', '2023-06-30', '2023-07-08', '2023-07-16', '2023-09-18', '2023-09-26', '2023-10-04', '2023-10-20', '2023-11-13', '2023-11-21', '2023-12-07', '2023-12-15', '2023-12-31', '2024-01-16', '2024-03-20', '2024-04-13', '2024-04-21', '2024-05-07', '2024-05-23', '2024-06-08', '2024-06-16', '2024-07-02', '2024-07-10', '2024-07-26', '2024-08-19', '2024-08-27', '2024-09-04', '2024-09-12', '2024-09-20', '2024-09-28', '2024-10-06', '2024-10-22', '2024-10-30', '2024-11-07', '2024-11-15', '2024-12-01'] ```

2. Apply a Cloud Mask and Compute NDWI

```python

1. Mask clouds

cloudmaskedcollection = collection.maskedcloudscollection

2. Create a collection of singleband NDWI images with band names of 'ndwi'

cloudmaskedNDWIcollection = cloudmasked_collection.ndwi

BONUS - 3. Create water classification maps using a user-set binary NDWI threshold

waterclassificationmaps = cloudmaskedcollection.ndwi_collection( threshold=0 ) ```

Visualization of true color and classified water (in blue) from one of the dates in the collection. To see the code used to display this image, please view this Example Notebook

3. Calculate Water Area Time Series

python calculate_water_area = cloud_masked_NDWI_collection.PixelAreaSumCollection( band_name='ndwi', #specify band to use from collection geometry=study_area, #ee.Geometry() of your study area threshold=0, #binary classification threshold for unclassified rasters, scale=90 #pixel size for zonal statistics ) water_area_time_series = calculate_water_area.aggregate_array('ndwi').getInfo() print('List of square meters of water in images:', water_area_time_series)

To see the code used to display this plot, please view this Example Notebook

For details about Sentinel-1 SAR and Sentinel-2 MSI modules, and all other available Landsat or cross-module functions, please refer to the RadGEEToolbox documentation. You can also explore /Example Notebooks for more usage examples.

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Contributing

Contributions are welcome! If you’d like to suggest a feature, report a bug, or contribute code or documentation, please visit the GitHub Issues page to get started or view the CONTRIBUTING.md file in the main directory.

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License

RadGEEToolbox is released under the MIT License.