Sorting of Cloud Cover in Sentinel Imagery using (Python and Google Earth Engine)

This python group project aims to sort, filter, classify and remove cloud-contaminated pixels from satellite imagery (sentinel imagery) within a defined Area of Interest (AOI) by downloading a shapefile, using Google Earth Engine (GEE) and python (geemap). Cloud removal in Google Earth Engine allows you to hide or highlight specific parts of an image based on certain conditions or criteria. The main objective is to ensure that we are able to identify cloud free image which can be used for futher analysis.

Installation

1. Install Python: We installed anaconda to create and activate a new working environment.
2. Install the necessary packages such as geemap and geopandas, using pip.
3. Install a Jupyter Notebook which contains sections for accessing and filtering GEE data, implementing cloud masking, and sorting the results. The code is organized for ease of understanding and reusability.

code to install the necessary packages using pip in python

!pip install geopandas

!pip install geemap

code to create a new envirronment in python

conda create -n gee_env python=3.9

conda activate gee_env

code to install jupyter notebook

pip install jupyter

jupyter notebook

After installing the packages, We authenticate with Google Earth Engine account to access it in jupyter.

import ee

ee.Authenticate()

ee.Initialize()

Path to the shapefile

We use an AOI by downloading a shp file of a region around salzburg

shapefile_path = "C:\Users\owhor\Downloads\PLUS_softwaredev_cloudsort-main\PLUS_softwaredev_cloudsort-main\Sample_data\Sample_AOI\AOI_Salzachauen_buffer_150m_WGS84_33N_gcs.shp"

Setting the Time for the Sentinel-2 image collection.

start_date = "2020-01-01"

end_date = "2021-01-01"

Visualizing our AOI on a Map

Create an interactive map and display our AOI:

Map = geemap.Map()

aoi = geemap.shp_to_ee("C:\Users\owhor\Downloads\PLUS_softwaredev_cloudsort-main\PLUS_softwaredev_cloudsort-main\Sample_data\Sample_AOI\AOI_Salzachauen_buffer_150m_WGS84_33N_gcs.shp")

Add AOI to the map and center it

Map.addLayer(aoi, {}, "AOI")

Map.centerObject(aoi, 11)

Display the map

Map

Educational Materials

For further learning, refer to the learning_resources.md file, which contains guides, instructions, and videos relevant to GEE, geemap, and cloud cover classification techniques.

Contributors

Owhorji Miracle Chinwe

Yana Nikolova

Damilola Oluwaseun Alfred

Lea Effertz