Remote Sensing for Agricultural Water Management
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: 01 July 2019
End: 19 July 2019
ECTS credit points: 5
Deadline IHE application: 31 May 2019 - 23.59 (CET)
Course fee: € 2850
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
- 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.
Course Content
Subject 1: Introduction to Remote sensing:
Basics of RS, introduction to common data portal, satellites, typical application od RS and existing products, Hands-on exercises on need analysis and acquiring of relevant data.
Subject 2: Remote Sensing data analysis, groundtruthing, and land cover classification:
Overview of RS data processing flow, common indices, and classification theory; Ground Truthing methods; Hands-on exercises (1) GT collection, (2) Landsat data pre-processing, extracting common indices, categorize them, and (3) Land cover classification and accuracy assessment
Subject 3: Mapping agricultural systems:
RS for agricultural management, Hands-on exercise on mapping agriculture infrastructure, crop intensity and irrigation command, integrating RS data and secondary data
Subject 4: Remote sensing for Evapotransipration, yield and Water Productivity assessment (pySEBAL)
Theory of SEBAL, Introduction to pySEBAL model, hands-on exercise on running pySEBAL to estimate ET, biomass, and WP.
Subject 5: Remote sensing for enhancing performance of irrigation systems
Assessment of the irrigation performance using remote sensing, interpreting WP and other performance indicators results to identify gaps, diagnose water management problems, and attribute to relevant factors for improvements
Subject 6: Remote Sensing for Irrigation water Accounting
Producing water accounts for an irrigation system using remote sensing information
Lecturers
- Dr. Poolad Karimi
- Prof. Wim Bastiaanssen
- Dr. Xueliang Cai
- Dr. Jonna van Opstal
- Dr. Sajid Pareeth