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.

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For whom?

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

Learning objectives

Upon completion, the participant should be able to:
  1. The students will be able to explain RS theory, technology, typical applications, and be able to identify and download relevant RS data and products
  2. The students will be able to pre-process, extract and analyse common indices, design and collect groundtruth points, and conduct land cover classification
  3. The students will be able to extract biophysical, infrastructure and management features of agricultural system
  4. The students will be able to explain the theory and implement pySEBAL model to estimate ET, yield, and WP
  5. 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
  6. The students will be able to produce water accounts for an irrigation system using remote sensing information and evaluate the performance of the system.

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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