SMADI

Soil Moisture Anomaly Detection Indicators

This repository contributes to a visiting research activity within the framework of EUMETSAT HSAF, hosted by TU Wien, on the subject “development of workflows for climate normal and anomaly calculation for satellite soil moisture products”.

SMADI is a comprehensive workflow designed to compute climate normals and detect anomalies in satellite soil moisture data. The primary focus is on ASCAT surface soil moisture (SSM) products. By establishing the distribution of SSM for each period and location, SMADI computes climatology, or climate normals, and subsequently identifies anomalies.

The core objective of SMADI is to leverage these anomaly indicators to identify and highlight extreme events such as droughts and floods, providing valuable insights for environmental monitoring and management. Furthermore, the methods used apply to other meteorological variables, such as precipitation, temperature, and more.

Features

Data Reading: Read and preprocess the input data from Supported data sources. smadi.data_reader
Climatology: Compute the climatology for the input data based on different time steps (e.g., monthly, dekadal, weekly, etc.). smadi.climatology
Anomaly Detection: Detect anomalies based on the computed climatology using different anomaly detection indices. smadi.anomaly_detectors
Visualization: Visualize the computed climatology and anomalies as time series, maps, and histograms. smadi.plot , smadi.map

Case Studies

Romania Drought Event 2007

Romania ASCAT SSM Anomaly Maps of the 2007 Drought Event

Senegal Drought Event 2014

Senegal ASCAT SSM Anomaly Maps of the 2014 Drought Event

Germany Flood Event 2021

Germany ASCAT SSM Anomaly Maps of the 2021 Flood Event

Nigeria Flood Event 2022

Nigeria ASCAT SSM Anomaly Maps of the 2022 Flood Event