Surface Soil Moisture Anomaly Detection Workflow (in progress)

Overview

SSMAD (Surface Soil Moisture Anomaly Detection) is a workflow designed to compute climate normals and anomalies of ASCAT surface soil moisture data in the context of flood and drought monitoring.

Features

ASCAT Data Processing: Efficiently processes ASCAT surface soil moisture data.
Climate Normals Calculation: Computes climate normals for surface soil moisture.
Anomaly Detection: Identifies anomalies in surface soil moisture based on computed normals.
Visualization: Generates visualizations to aid in interpreting results.
Documentation: Detailed documentation to guide users through the workflow.

Getting Started

To get started with SSMAD, follow these steps:

Clone the Repository:

git clone https://github.com/MuhammedM294/ssmad.git
cd ssmad

Install Dependencies:
```
pip install -r requirements.txt
```
Run the Workflow:
```
python workflow.py
```

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

The project utilizes data from ASCAT for surface soil moisture anomalies computation.

Feel free to use, modify, and extend this workflow for your research needs.