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Facilitating Advanced Sentinel-2 Analysis Through a Simplified Computation of Nadir BRDF Adjusted Reflectance

David Montero, Miguel D. Mahecha, César Aybar, Clemens Mosig, Sebastian Wieneke

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 https://github.com/ESDS-Leipzig/sen2nbar.

Facilitating Advanced Sentinel-2 Analysis Through a Simplified Computation of Nadir BRDF Adjusted Reflectance

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 -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 -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.
Paper Structure (21 sections, 11 equations, 4 figures, 1 table)

This paper contains 21 sections, 11 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. The sen2nbar Python package
  3. The axioms module
  4. The metadata module
  5. Solar and sensor viewing angles:
  6. Processing Baseline:
  7. The kernels module
  8. Volumetric Kernel:
  9. Geometric Kernel:
  10. The brdf module
  11. The c_factor module
  12. The nbar module
  13. SAFE files:
  14. Earth System Data Cubes:
  15. Showcase
  16. ...and 6 more sections

Figures (4)

  • Figure 1: Flowcharts depicting the process of computing NBAR for SAFE files (a) and ESDCs (b). In (a), NBAR values are calculated for each spectral band and subsequently stored either as GeoTIFF or Cloud Optimized GeoTIFF (COG) within a newly created folder titled "NBAR" inside the original SAFE file's directory. For ESDCs, depicted in (b), the NBAR computation results in the generation of a new ESDC, represented as an xarray object. The dashed arrows linking the STAC object in (b) signify that this procedure is automated, eliminating the need for users to manually extract items from the STAC.
  • Figure 2: Diagram illustrating the $c$-factor computation block. The arrays depicted in this block serve as examples derived from a single metadata file, with the understanding that values could vary across different files.
  • Figure 3: ESDCs illustrating the difference between NBAR and SR values. The initial ESDC presents an RGB composite utilizing visible bands to create a true color reference image. The maximum (blue) and minimum (red) $\Delta\rho$ values for each band are indicated in the lower-right corner of the corresponding ESDC.
  • Figure 4: ESDCs illustrating the difference between indices calculated using NBAR and SR values. The first row presents the four calculated indices using the SR values. The second row presents the $\Delta\psi$ values for each ESDC. The maximum (green) and minimum (brown) $\Delta\psi$ values for each index are indicated in the lower-right corner of the corresponding ESDC.