Understanding Sentinel-2 L2A Scene Classification Map with Python Codes
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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)
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.
The L2A Scene Classification Map is generated using a machine learning algorithm that analyzes the spectral and spatial properties of the pixels in the image and assigns them to one of 11 different classes. These classes include “No Data”, “Saturated or Defective”, “Dark area pixels”, “Cloud Shadows”, “Vegetation”, “non-Vegetated”, “Water”, “Unclassified”, “Cloud medium”, “Cloud high”, “Thin cirrus” and “Snow”. Each of these classes is associated with specific spectral and spatial signatures that allow the machine learning algorithm to accurately classify pixels based on their characteristics.
The L2A Scene Classification Map provides valuable information on the land cover and land use of the areas imaged by Sentinel-2. This information is critical for a range of environmental monitoring and management…
