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https://github.com/radwinskis/RadGEEToolbox

Python package simplifying large-scale operations using Google Earth Engine (GEE) for users who utilize Landsat and Sentinel
https://github.com/radwinskis/RadGEEToolbox

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Python package simplifying large-scale operations using Google Earth Engine (GEE) for users who utilize Landsat and Sentinel

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[![PyPI version](https://badge.fury.io/py/RadGEEToolbox.svg)](https://pypi.org/project/RadGEEToolbox/)

# RadGEEToolbox 🛠️



### 🌎 Python package simplifying large-scale operations using Google Earth Engine (GEE) for users who utilize Landsat and Sentinel 



### [See documentation here](https://radgeetoolbox.readthedocs.io/en/latest/)



Initially created by Mark Radwin to help simplify processing imagery for PhD studies and general image exploration, this package offers helpful functionality with an outlook to add furthur functionality to aid assorted Earth observation specialists. 



The package is divided into six modules:

- LandsatCollection

   - Define and process Landsat 5 TM, 8 OLI, and 9 OLI surface reflectance imagery

- Sentinel1Collection

   - Define and process Sentinel 1 Synthetic Aperture Radar (SAR) backscatter imagery

- Sentinel2Collection

   - Define and process Sentinel 2 MSI surface reflectance imagery

- CollectionStitch

   - Accessory module to perform mosaicing functions on traditional GEE collections

- GetPalette

   - Retrieve color palettes compatible with visualiztion GEE layers

- VisParams

   - Alternative to visualization parameters dictionaries, define vis params using a function and retrieve palettes from GetPalette - makes visualizing images a bit easier



LandsatCollection.py, Sentinel1Collection.py, and Sentinel2Collection.py are the main modules for the majority of image processing. 



CollectionStitch.py, GetPalette.py, and VisParams.py are supplemental for additional processing and image display



Almost all functionality is server-side friendly AND most results are cached, providing very fast processing times.



You can easily go back-and-forth from RadGEEToolbox and GEE objects to maximize efficiency in workflow.



### 🤔 Why use RadGEEToolbox?



If you are a new or expert user of GEE Landsat or Sentinel imagery, these tools greatly help reduce the amount of code needed for satellite image filtering, processing, analysis, and visualization. 



Although similar toolset packages exist, RadGEEToolbox offers differing functionality and additional functionality not found in other existing toolsets. 



### Features:

- Streamlined image collection definition and filtering

- Retrieve dates of images

- Mask clouds in image collection

- Mask water in image collection via two methods

- Mask to water in image collection via two methods

- Mask image collection inside geometry

- Mask image collection outside geometry

- Mosaic image collections which share dates of observations and copy all image properties from collection of choice

- Mosaic images that share the same date from a single image collection

- Easily choose image from collection for visualizing or processing using index or date string

- Calculate a variety of spectral index products: NDWI, NDVI, LST (celcius), NDTI (turbidity), relative chlorophyll-a concentrations, halite index (see Radwin & Bowen, 2021), and gypsum index (modified from Radwin & Bowen, 2021)

- Temporally reduce image collections using: minimum, maximum, median, or mean

- Calculate geodesically corrected surface area from pixels corresponding to values greater than a defined threshold from any singleband image

- Calculate geodesically corrected surface area from NDWI (water) pixels using dynamic thresholding via Otsu methods

- Easily call in a variety of useful color palettes for image visualization

- Easily define visualization parameter dictionaries

- AND more with a continual list of growing features



### New features included in version 1.5.0

- Support for Sentinel 1 SAR data

   - Includes all high-level functionality from the LandsatCollection and Sentinel2Collection modules

   - Easy to defin the type of S1 data to use (instrument mode, polarization, pixel size, orbit direction, etc)

   - Multilooking

   - Speckle filtering (Lee-Sigma)

   - Converting dB to sigma naught, or sigma naught to dB

- Support for calculating the values along a transect or collection of transects

   - Works for each image collection module

   - Can iterate along an image collection and store transect values for each transect and for each image

   - Stores transect data as a DataFrame

   - Automatically saves transect data to csv files when iterating along a collection



### ⌨️ Basic Usage

RadGEEToolbox is organized so that all functions are associated with each LandsatCollection and Sentinel2Collection class modules. You will need to define a base class collection, using arguments standard to defining ee.ImageCollections, and then you can call any of the class property attributes, methods, or static methods to complete processing. Utilizing class attribute propertues allows for very short code lines and very fast processing, while utilizing the methods allows for expanded customization of processing but requires more arguments and interaction from the user. The choice is up to you!



- #### See the /Example Notebooks folder for examples showing how to define and filter collections, process the rasters for multispectral or other spectral products, and easily access color palettes and visualization parameter dictionaries for image visualization. 



- #### See below for basic examples using the LandsatCollection class module. 



- #### See https://radgeetoolbox.readthedocs.io/en/latest/ for documentation.

```

# Create base class image collection

image_collection = LandsatCollection(start_date, end_date, tile_row, tile_path, cloud_percentage_threshold)



# retrieve latest image in collection as eeImage

latest_image = image_collection.image_grab(-1) 



# Get eeImageCollection from class collection

ee_image_collection = image_collection.collection 



# return cloud-masked LandsatCollection image collection

cloud_masked_collection = image_collection.masked_clouds_collection 



# return cloud-masked land surface temperature collection

LST_cloudless_collection = cloud_masked_collection.LST 



# return NDWI LandsatCollection image collection

NDWI_collection = image_collection.ndwi 



# Example showing how class functions work with any LandsatCollection image collection object, returning latest ndwi image

latest_NDWI_image = NDWI_collection.image_grab(-1) 



# See example notebooks for more details of usage

```



## 🚀 Installation Instructions



### 🔍 Prerequisites



- **Python**: Ensure you have version 3.6 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 1.5.0 using pip (NOTE: it is recommended to create a new virtual environment):



```bash

pip install RadGEEToolbox==1.5.0

```



### 📦 Installing via Conda



To install `RadGEEToolbox` version 1.5.0 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 `1.5.0` printed as the version number.