Accessing Sentinel 2 data by Country and Calculating NDVI Time Series in Google Earth Engine (GEE)

This project focuses on harnessing the capabilities of Google Earth Engine (GEE) to access and analyze Sentinel-2 satellite imagery for environmental monitoring and agricultural assessment. The primary objective is to compute the Normalized Difference Vegetation Index (NDVI) time series, which serves as a vital indicator of vegetation health and land cover changes over time.

The process begins with acquiring Sentinel-2 data, which provides high-resolution optical imagery suitable for analyzing various land surface characteristics. Users will learn how to efficiently query and filter the data based on specific temporal and spatial parameters, ensuring that the selected images align with their study area and timeframe.

Once the data is accessed, the project will guide users through the steps of calculating NDVI, a widely used vegetation index that leverages the red and near-infrared bands of the Sentinel-2 imagery. The NDVI values will be computed for each image in the time series, enabling users to visualize and analyze trends in vegetation health over time.

Additionally, the project will cover techniques for visualizing NDVI time series data using GEE’s powerful mapping tools, allowing for clear representation of changes in vegetation cover. By the end of this project, participants will gain practical experience in remote sensing data processing, time series analysis, and the application of NDVI in various ecological and agricultural contexts.

This tutorial is ideal for researchers, students, and professionals interested in remote sensing, environmental science, or agriculture who wish to leverage cloud-based tools for large-scale data analysis.