Understanding Sentinel-2 L2A Scene Classification Map with Python Codes

Sentinel-2 L2A Scene Classification Map Classes

Next related article: Create a water mask from Sentinel-2 satellite imagery using the Scene Classification Layer (SCL)

Create a water mask from Sentinel-2 satellite imagery using the Scene Classification Layer (SCL)

Sentinel-2 is a satellite mission developed by the European Space Agency (ESA) as part of the Copernicus program. The Sentinel-2 mission is designed to provide high-resolution, multispectral imagery of the Earth’s surface for a wide range of environmental monitoring and management applications. One of the key products of the Sentinel-2 mission is the Level-2A (L2A) Scene Classification Map, which provides information on the land cover and land use of the areas imaged by the satellite.

Before diving into the details of the Sentinel-2 L2A Scene Classification Map, it’s important to understand the basics of Sentinel-2 data processing. Sentinel-2 data is available in two levels: Level-1C (L1C) and Level-2A (L2A). Level-1C data is the raw, unprocessed data from the satellite, while Level-2A data is processed to correct for atmospheric effects and other factors that can impact the quality and accuracy of the data. The L2A Scene Classification Map is derived from Level-2A data.