Course Objectives

• Understand the principles of precision agriculture and remote sensing.
• Learn to use Google Earth Engine for agricultural applications.
• Acquire skills in processing and analyzing remote sensing data.
• Apply remote sensing techniques to monitor and manage agricultural resources.
• Develop the ability to interpret remote sensing data for decision-making in agriculture.

Course Prerequisites

• Basic understanding of agriculture.
• Familiarity with geographic information systems (GIS).
• Basic programming skills (preferably in JavaScript or Python).

Course Structure

The course is divided into weekly modules, each focusing on different aspects of precision agriculture using remote sensing and Google Earth Engine.

Week 1: Introduction to Precision Agriculture and Remote Sensing

• Lecture: Overview of Precision Agriculture
  • Definition and Importance
  • Historical Development
• Lecture: Basics of Remote Sensing
  • Principles and Types of Remote Sensing
  • Key Remote Sensing Platforms and Sensors
• Practical: Introduction to Google Earth Engine (GEE)
  • Setting Up GEE Account
  • GEE Interface and Tools

Week 2: Remote Sensing Data for Agriculture

• Lecture: Types of Remote Sensing Data
  • Optical, Thermal, and Radar Data
  • Data Sources (Landsat, Sentinel, MODIS, etc.)
• Lecture: Spectral Signatures of Crops
  • Understanding Vegetation Indices (NDVI, EVI)
• Practical: Accessing and Visualizing Remote Sensing Data in GEE
  • Importing and Visualizing Satellite Imagery

Week 3: Image Processing and Analysis in GEE

• Lecture: Image Preprocessing
  • Atmospheric Correction, Radiometric and Geometric Corrections
• Lecture: Image Classification Techniques
  • Supervised and Unsupervised Classification
• Practical: Implementing Image Preprocessing and Classification in GEE
  • Example: Classifying Land Cover Types

Week 4: Vegetation Indices and Crop Monitoring

• Lecture: Vegetation Indices for Agriculture
  • Calculating and Interpreting NDVI, EVI, SAVI, etc.
• Lecture: Crop Monitoring Techniques
  • Phenology, Growth Stages, and Stress Detection
• Practical: Calculating Vegetation Indices in GEE
  • Monitoring Crop Health Over Time

Week 5: Soil and Water Management Using Remote Sensing

• Lecture: Soil Moisture and Health Monitoring
  • Remote Sensing Techniques for Soil Analysis
• Lecture: Water Management in Agriculture
  • Irrigation Scheduling and Water Use Efficiency
• Practical: Analyzing Soil and Water Data in GEE
  • Case Study: Assessing Soil Moisture Levels

Week 6: Precision Agriculture Applications

• Lecture: Yield Prediction Models
  • Remote Sensing for Yield Estimation
• Lecture: Pest and Disease Monitoring
  • Identifying and Managing Crop Diseases
• Practical: Building a Yield Prediction Model in GEE
  • Integrating Remote Sensing Data for Yield Forecasting

Week 7: Advanced Topics and Case Studies

• Lecture: Machine Learning in Precision Agriculture
  • Applications of AI and ML in Agriculture
• Lecture: Case Studies
  • Successful Implementations of Precision Agriculture Using Remote Sensing
• Practical: Implementing Machine Learning Algorithms in GEE
  • Example: Crop Type Classification Using ML