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.

翻译：欧洲航天局哥白尼计划中的Sentinel-2（S2）任务为地球表面分析提供了关键数据。其Level-2A产品通过多光谱仪器（MSI）提供高至中分辨率（10-60米）的地表反射率（SR）数据。为提高SR数据的准确性和可比性，模拟天底观测视角的调整至关重要。这些校正处理了SR的各向异性特性以及太阳和观测角度的变化性，确保了不同时间和条件下图像比较的一致性。$c$因子法作为一种简单而有效的算法，利用MODIS BRDF模型对观测到的S2 SR进行调整，以实现天底BRDF调整反射率（NBAR）。尽管$c$因子可简单应用于单幅图像，但缺乏一个应用于多幅S2图像和来自云存储数据的地球系统数据立方体（ESDC）的连贯Python框架。本文介绍sen2nbar，这是一个专门设计的Python软件包，用于将S2 SR数据转换为NBAR，支持来自云存储数据的单幅图像和ESDC。该软件包通过单一函数简化了S2 SR数据到NBAR的转换，并组织成模块以实现高效流程管理。通过促进来自时空资产目录（STAC）的SAFE文件和ESDC的NBAR转换，sen2nbar被开发为一个灵活的工具，能够处理多样化的数据格式需求。我们预计sen2nbar将极大地促进S2数据的标准化和统一化，为不同应用领域的各类用户提供稳健的解决方案。sen2nbar是一个开源工具，可在https://github.com/ESDS-Leipzig/sen2nbar获取。