Young and mid-career professionals, engineers and technicians and academics involved in agricultural water and irrigation system management in various government and non-government organizations.
PrerequisitesGeneral knowledge about remote sensing and GIS and their application in water related issues
Dates, Fee, ECTS
Start: 07 September 2020
End: 15 January 2021
Duration: 16 weeks
ECTS credit points: 5
Deadline IHE application: 05 September 2020 - 23.59 (CET)
Course fee: € 1035
Start: 06 September 2021
End: 14 January 2022
Deadline IHE application: 05 September 2021 - 23.59 (CET)
Course fee: € 1035
VAT is not included in the course fee
- Explain RS theory, technology, typical applications of earth observation data
- Download, pre-process satellite data, extract biophysical features, derive and analyse vegetation indices in agricultural systems
- Perform land cover classification using time series satellite data through application of machine learning algorithms in desktop and cloud based platforms
- Explain the theory and implement surface energy balance model to estimate Evapotranspiration (ET), biomass production, and Water Productivity (WP)
- Assess the irrigation performance using remote sensing, interpret them to identify gaps, diagnose water management problems, and attribute to relevant factors for improvements
- Subject 1: Introduction to Earth observation and remote sensing techniques
Basics of RS and spatial data; introduction to common RS data portal; earth observation satellites; typical application of RS and existing products; hands-on exercises on need analysis and acquiring of relevant data.
- Subject 2: Remote Sensing data analysis
Overview of RS data processing flow; satellite data pre-processing; mapping and visualizing spatial data; image analysis; hands-on exercise on deriving vegetation indices, zonal statistics using open source software and libraries.
- Subject 3: Land cover classification
Land cover classification theory; classification algorithms; machine learning approaches in classification; ground truthing methods; accuracy assessment; hands-on exercises on land cover classification using open source QGIS and cloud based Google Earth Engine (GEE)
- Subject 4: Remote sensing for Evapotransipration, biomass production and water productivity assessment
Theory of Surface Energy Balance Algorithm for Land (SEBAL); Introduction to python based implementation of SEBAL (pySEBAL); hands-on exercise on running pySEBAL to estimate evapotranspiration, biomass, and water productivity.
- Subject 5: Remote sensing for enhancing performance of irrigation systems
Assessment of the irrigation performance using remote sensing based indicators for productivity, adequacy, reliability, and equity; interpreting results to identify gaps, diagnose water management problems, and attribute to relevant factors for improvements; perform irrigation scheme level water accounting
- Dr. Poolad Karimi
- Dr. Sajid Pareeth
- Mr. Tim Busker