  The Sentinel-2 (S2) mission from the European Space Agency's Copernicus
program provides essential data for Earth surface analysis. Its Level-2A
products deliver high-to-medium resolution (10-60 m) surface reflectance (SR)
data through the MultiSpectral Instrument (MSI). To enhance the accuracy and
comparability of SR data, adjustments simulating a nadir viewing perspective
are essential. These corrections address the anisotropic nature of SR and the
variability in sun and observation angles, ensuring consistent image
comparisons over time and under different conditions. The $c$-factor method, a
simple yet effective algorithm, adjusts observed S2 SR by using the MODIS BRDF
model to achieve Nadir BRDF Adjusted Reflectance (NBAR). Despite the
straightforward application of the $c$-factor to individual images, a cohesive
Python framework for its application across multiple S2 images and Earth System
Data Cubes (ESDCs) from cloud-stored data has been lacking. Here we introduce
sen2nbar, a Python package crafted to convert S2 SR data to NBAR, supporting
both individual images and ESDCs derived from cloud-stored data. This package
simplifies the conversion of S2 SR data to NBAR via a single function,
organized into modules for efficient process management. By facilitating NBAR
conversion for both SAFE files and ESDCs from SpatioTemporal Asset Catalogs
(STAC), sen2nbar is developed as a flexible tool that can handle diverse data
format requirements. We anticipate that sen2nbar will considerably contribute
to the standardization and harmonization of S2 data, offering a robust solution
for a diverse range of users across various applications. sen2nbar is an
open-source tool available at https://github.com/ESDS-Leipzig/sen2nbar.
