
This program focuses on identifying and mapping small-scale irrigation areas, a critical task for effective water resource management and food security. It introduces a novel method that combines high-resolution multispectral time-series satellite imagery with digital elevation model (DEM) analysis and expert local knowledge. This approach is particularly useful in complex topographic and semi-arid environments, offering a more accurate way to delineate irrigated land compared to previous methods. The method was developed and tested in the Zamra catchment in Ethiopia, a region prioritizing agricultural irrigation expansion to improve livelihoods and food security. The course will delve into the steps involved in this new approach, including setting expert-defined thresholds for various geographic and spectral data, applying a random forest classifier for mapping, and characterizing different irrigation scheme types. By learning this technique, participants can contribute to better water management strategies and support food security initiatives. The course also touches upon the importance of utilizing publicly available satellite data and cloud services for hydrological analysis.
This course familiarizes you with the wealth of data available in Copernicus and Sentinel repositories and demonstrates how to use cloud services for data extraction and processing. The scope is limited to datasets crucial for hydrological analysis, focusing on retrieving satellite images (Sentinel 2), elevation and soil information, and precipitation time series.
While not a degree program, the skills learned in this course are applicable to roles in water resource management, agricultural monitoring, environmental analysis, and remote sensing applications, particularly in regions focused on food security and sustainable development.
The course 'Exploration of Copernicus and Sentinel Data' is offered free of charge.