Crop Water Modelling & Evapotranspiration Estimation

This hands-on workshop introduces participants to the principles and practical methods of evapotranspiration (ET) estimation, crop water requirement modelling, and geospatial assessment of agricultural water demand. The programme integrates meteorological and crop data with GIS, Remote Sensing, cloud-based Earth Observation and computational tools.

Participants will work through an integrated case study using QGIS, FAO CROPWAT, Google Earth Engine and Python/Google Colab, progressing from fundamental concepts to crop water modelling, satellite-based assessment and integrated decision-support applications.

Core Capabilities

What You Will Learn

ET & Energy Balance Concepts

FAO CROPWAT Irrigation Scheduling

Google Earth Engine Mapping

Python & Colab Workflows

Master reference ET (ETo), actual ET (ETa), crop coefficients (Kc), and satellite surface energy balance principles.

Model crop water requirements, soil moisture deficits, and optimum irrigation schedules using climate and soil inputs.

Process Landsat and Sentinel imagery archives in GEE using JavaScript to map regional ET distributions.

Automate climate data ingestion, raster algebra, time-series plotting, and ET modeling in Python environments.

Course Highlights

8 Hours

Live Interactive Practical Instruction

Code Provided

Ready-to-run GEE & Python Colab Notebooks

Real Datasets

Hands-on Watershed & Farm Case Studies

Certified

Official Skill Development Certificate

Day 1: ET Fundamentals & Remote Sensing Data

Day 2: FAO CROPWAT & Irrigation Water Demand

Day 3: Google Earth Engine for ET Mapping

Day 4: Python & Colab Spatial Workflows

Preparation and exploration of climate and crop datasets

Importing and managing spatial and tabular datasets in QGIS

Study-area and agricultural-data visualization

Preparing datasets for subsequent crop-water modelling exercises

Setting up climate, rainfall, crop and soil parameters in CROPWAT

Estimation and interpretation of reference evapotranspiration

Crop evapotranspiration and crop water requirement calculation

Effective rainfall and irrigation requirement assessment

Interpretation of CROPWAT outputs

Linking model results with GIS-based agricultural information

Accessing satellite datasets using Google Earth Engine

Study-area and temporal filtering

Basic cloud filtering and image processing

NDVI calculation and visualization

Extraction and interpretation of vegetation time-series information

Exploration and visualization of satellite-based ET information

Comparison of spatial and temporal patterns

Introduction to a Python/Google Colab workflow for crop-water calculations

Importing and processing climate and crop datasets

Automating ETc and crop-water calculations

Visualization of temporal crop-water demand

Integration and interpretation of CROPWAT, GIS and Earth Observation outputs

End-to-end case-study demonstration

Software & Tools Covered

QGIS

FAO CROPWAT

Google Earth Engine

Python & Google Colab

Open-source desktop GIS for spatial data preprocessing, coordinate handling, and professional map rendering.

Standard decision-support program for calculating crop water requirements and irrigation scheduling.

Petabyte-scale cloud platform for multi-spectral satellite image processing and automated ET mapping.

Cloud Python environment for spatial data automation, statistical climate analysis, and customized plotting.

Expected Learning Outcomes

• Understand the fundamentals of evapotranspiration and crop-water requirement.

• Explain the relationships among ET₀, Kc, ETc, effective rainfall and irrigation requirement.

• Prepare climate, crop and spatial datasets for agricultural water assessment.

• Perform crop-water requirement modelling using FAO CROPWAT.

• Use QGIS for spatial preparation, visualization and interpretation of crop-water information.

• Use Google Earth Engine for satellite-based vegetation and ET assessment.

• Perform basic computational automation of crop-water calculations using Python/Google Colab.

• Integrate meteorological, crop and Earth Observation information for agricultural water-resource assessment and decision support.

Researchers & Academics

Agronomists & Hydrologists

GIS & RS Professionals

Students & Engineers

Enhance research methodology and publication rigor with satellite-based agricultural hydrology models.

Apply state-of-the-art geospatial ET analysis to optimize farm-level irrigation management.

Expand spatial analytics capabilities into GEE cloud scripting and automated Python workflows.

Build in-demand practical technical skills in environmental data science and climate hydrology.

Empowering Researchers & Engineers with Applied Geospatial Expertise

INDACA Institute of Skills delivers rigorous, code-first professional development taught by domain experts. Our workshops emphasize actionable skills, real datasets, and reproducible scientific workflows.

Dr. Ibrahim Bathis K

Dr. Ibrahim Bathis K is a distinguished researcher and faculty member at IREEE, Kerala. He specializes in remote sensing applications in water resources, satellite evapotranspiration modeling, and spatial climate analysis.

With extensive academic research and practical consulting background, Dr. Bathis excels at translating complex satellite energy balance models into intuitive, practical workflows for participants of all backgrounds.

Registration Closes: 17 September 2026 | Limited Seats Available. Master QGIS, FAO CROPWAT, Google Earth Engine, and Python for crop water assessment.