Remote Sensing Projects

Overview

This repository contains a collection of Python notebooks for remote sensing applications, focusing on vegetation, soil, and atmospheric analysis. The projects utilize Python and Google Earth Engine (GEE) for processing and analyzing remote sensing data.

Contents

Energy and Radiation

• Energy_Balance.ipynb: Implementation of energy balance calculations using gee data.
• remoreSensing_Shortwave_rad.ipynb: Processing and analysis of shortwave radiation data
• Trapezoid_method.ipynb: Implementation of the trapezoid method for soil moisture estimation

Vegetation and Productivity

• Montein_GPP.ipynb: Gross Primary Production (GPP) calculations using the Monteith approach
• RS_FAO.ipynb: Remote sensing applications following FAO (Food and Agriculture Organization) guidelines

Water and Evapotranspiration

• evapotranspiration_fao.ipynb: FAO-based evapotranspiration calculations
• RADAR.ipynb: RADAR data processing and analysis for moisture detection

Data Processing

• read_netcdf_image_as_data_cube.ipynb: Tools for handling NetCDF files as data cubes

Dependencies

• Python
• Google Earth Engine Python API
• Jupyter Notebook
• Common scientific Python libraries:
  • NumPy
  • Pandas
  • xarray (for NetCDF handling)
  • matplotlib
  • earthengine-api
  • caropy
  • rasterio

Usage

1. Ensure you have all dependencies installed
2. Open the desired notebook using Jupyter Notebook or Colab
3. Follow the instructions within each notebook for specific applications

Project Structure

Remote_Sensing_Projects/
├── Energy_Balance.ipynb
├── Montein_GPP.ipynb
├── RADAR.ipynb
├── RS_FAO.ipynb
├── Trapezoid_method.ipynb
├── evapotranspiration_fao.ipynb
├── read_netcdf_image_as_data_cube.ipynb
└── remoreSensing_Shortwave_rad.ipynb

Features

• Energy balance calculations for land surface
• Vegetation productivity analysis
• Soil moisture estimation
• Evapotranspiration modeling
• RADAR data processing
• NetCDF data handling
• Integration with Google Earth Engine

License

This project is licensed under the HUJI License.

Author

Yehuda Yungstein (Supervised by Dr. David Helman)

Acknowledgments

This repository was created as part of a Remote Sensing course and incorporates standard methodologies from the field of remote sensing and Earth observation.