Analysing Sentinel 2 Imagery with ChatGPT and Python: Example Codes for NDVI and False Color Composites

Analysing Sentinel 2 Imagery with ChatGPT and Python.

Sentinel 2 is a satellite system that provides high-resolution multispectral imagery of the Earth’s surface. These images contain a wealth of information that can be used to monitor changes in land use, vegetation, water resources, and many other environmental factors. Analysing Sentinel 2 imagery can be a daunting task, but with the help of ChatGPT and Python, it can become much simpler and more efficient. In this post, we will discuss how to use ChatGPT to analyse Sentinel 2 imagery, and we will provide some example codes in Python to get you started. The codes is this article were created by chatGPT.

Before we begin, it’s important to note that analysing Sentinel 2 imagery requires some familiarity with remote sensing concepts and techniques. However, if you are new to remote sensing, don’t worry! There are plenty of resources available online to help you get started. Additionally, Python has many libraries that can make working with Sentinel 2 imagery much easier, including rasterio, geopandas, and matplotlib.

One of the main tasks when analysing Sentinel 2 imagery is to extract information from the various spectral bands that are available. There are 13 bands in total, ranging from the visible spectrum (bands 2, 3, 4) to the shortwave infrared (bands 11, 12). Each band provides information about different features on the Earth’s…