| File Name: | Leaf Area Index Monitoring Using MODIS in Google Earth Engin |
| Content Source: | https://www.udemy.com/course/leaf-area-index-modis-google-earth-engine |
| Genre / Category: | Other Tutorials |
| File Size : | 526.6 MB |
| Publisher: | Earth’s AI |
| Updated and Published: | July 23, 2025 |
This course provides a comprehensive introduction to monitoring Leaf Area Index (LAI) using MODIS satellite data within the Google Earth Engine (GEE) platform. LAI is a critical biophysical parameter representing the total leaf area per unit ground surface area, essential for understanding vegetation health, growth, and ecosystem productivity. Through this course, learners will gain expertise in remote sensing fundamentals and how to leverage satellite-derived LAI data to analyze vegetation dynamics across spatial and temporal scales.
Focusing on the MODIS MOD15A2H dataset, which provides 8-day composites of LAI at 500-meter resolution, students will learn how to filter, process, and analyze time series data in GEE. The course guides learners in defining Areas of Interest (AOIs), extracting relevant data, computing composites such as mean LAI over specific periods, and visualizing results through thematic maps. Additionally, the integration of true color imagery (MODIS MOD09A1) enables users to validate and contextualize vegetation patterns visually.
The practical component includes exporting processed LAI and true color imagery as GeoTIFF files for further analysis in GIS software, making it valuable for both academic research and industrial applications such as precision agriculture, forestry management, and environmental monitoring. Learners will also build time series charts within GEE to track seasonal and annual LAI variations, fostering an understanding of phenological trends and environmental impacts.
By the end of the course, participants will be equipped to implement advanced remote sensing techniques, interpret satellite data for vegetation analysis, and apply these skills to real-world scenarios using the powerful cloud-based GEE platform.
Who this course is for:
- Students and researchers in agriculture, environmental science, geography, or remote sensing looking to apply satellite data in real-world scenarios.
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