Abstract
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 this https URL.
Abstract (translated)
欧洲航天局 Copernicus 计划中的 Sentinel-2 (S2) 任务为地球表面分析提供了必要数据。其 Level-2A 产品通过 MultiSpectral Instrument (MSI) 提供了高至中分辨率（10-60 m）的表面反射率（SR）数据。为了提高 SR 数据的准确性和可比性，调整模拟顶视 perspective 是必要的。这些调整解决了 SR 的各向同性特性以及太阳和观察角度的变异性，确保在时间和不同条件下进行一致图像比较。$c$-factor 方法，一种简单而有效的算法，通过使用 MODIS BRDF 模型对观察到的 S2 SR 进行调整，以实现顶视 BRDF 调整反射率（NBAR）。尽管 $c$-factor 的应用非常简单，但在单个图像上进行调整仍然存在局限性。为了在多个 S2 图像和地球系统数据立方 (ESDC) 上应用 $c$-factor，我们缺乏一个统一 Python 框架。在这里，我们介绍 sen2nbar，一个 Python 包，专门用于将 S2 SR 数据转换为 NBAR，支持单张图像和来自云存储数据的 ESDC。这个软件包通过一个模块化的方式组织单函数转换 S2 SR 数据到 NBAR，简化了转换过程。通过促进 SAFE 文件和 ESDC 从空间时间资产目录（STAC）中的转换，sen2nbar 被开发成为一个灵活的工具，可以处理各种数据格式要求。我们预计，sen2nbar 将极大地促进 S2 数据的标准化和统一，为各种应用提供稳健的解决方案。sen2nbar是一个开源工具，您可以在这个链接处获得：https://www.esa.int/web/api/仁/sentinel2/send2nbar/
URL
https://arxiv.org/abs/2404.15812
