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.


Working knowledge in remote sensing and Geoinformatics and its application in water related issues

Dates, Fee, ECTS

Start: 04 July 2022
End: 22 July 2022
ECTS credit points: 5
Deadline IHE application: 03 June 2022 - 23.59 (CET)
Course fee: € 3000

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 of earth observation data
  2. The students will be able to acquire and pre-process high resolution satellite data, extract biophysical features, derive and analyse vegetation indices in agricultural systems
  3. The students will be able to map crop types using time series of big satellite data through application of machine learning algorithms in cloud based platforms
  4. The students will be able to explain the theory and implement surface energy balance model to estimate Evapotranspiration (ET), biomass production, and Water Productivity (WP)
  5. The students will be able to conduct irrigation performance assessment using remote sensing data, to identify gaps, diagnose issues and propose improvements

Course content

Topic 1: Introduction to Earth observation and remote sensing techniques Basics of RS and spatial big 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.

Topic 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.

Topic 3: Remote sensing and big data for mapping crop types Classification algorithms; machine learning approaches in crop classification from bigdata; ground truthing methods; accuracy assessment; hands-on exercises on crop type classification using open source libraries and cloud based Google Earth Engine (GEE)

Topic 4: Remote sensing for Evapotransipration, yield and WP assessment (SEBAL) Theory of SEBAL, Introduction to pySEBAL model, hands-on exercise on running pySEBAL to estimate ET, biomass, and WP. The skills acquired will be applied to the case study/assignment in progress during the class.

Topic 5: Remote sensing for irrigation water accounting and performance assessment 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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