Online Course on Remote Sensing for Agricultural Water Management

Develop skills to use remote sensing for land cover classification, estimating evapotranspiration, water productivity, irrigation performance assessment & irrigation water accounting.

Apply now for

20202021

For whom?

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.

Prerequisites

General 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
Duration: 16 weeks
Deadline IHE application: 05 September 2021 - 23.59 (CET)
Course fee: € 1035

VAT is not included in the course fee

Learning objectives

Upon completion, the participant should be able to:
  1. Explain RS theory, technology, typical applications of earth observation data
  2. Download, pre-process satellite data, extract biophysical features, derive and analyse vegetation indices in agricultural systems
  3. Perform land cover classification using time series satellite data through application of machine learning algorithms in desktop and cloud based platforms
  4. Explain the theory and implement surface energy balance model to estimate Evapotranspiration (ET), biomass production, and Water Productivity (WP)
  5. Assess the irrigation performance using remote sensing, interpret them to identify gaps, diagnose water management problems, and attribute to relevant factors for improvements

Course content

  • 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

Key lecturers

  • Dr. Poolad Karimi
  • Dr. Sajid Pareeth
  • Mr. Tim Busker

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