Young and mid-career professionals, engineers and technicians and academics involved in irrigation system management in various government and non-government organizations.
Dates, Fee, ECTS
Start: 29 June 2020
End: 17 July 2020
Deadline IHE application: 28 May 2020 - 23.59 (CET)
Course fee: € 2910
VAT is not included in the course fee
- The students will be able to explain RS theory, technology, typical applications, and be able to identify and download relevant RS data and products
- The students will be able to pre-process, extract and analyse common indices, design and collect groundtruth points, and conduct land cover classification
- The students will be able to extract biophysical, infrastructure and management features of agricultural system
- The students will be able to explain the theory and implement pySEBAL model to estimate ET, yield, and WP
- The students will be able to assess the irrigation performance using remote sensing, Interpret them to identify gaps, diagnose water management problems, and attribute to relevant factors for improvements
- The students will be able to produce water accounts for an irrigation system using remote sensing information and evaluate the performance of the system.
- 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