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Facilitating Advanced Sentinel-2 Analysis Through a Simplified Computation of Nadir BRDF Adjusted Reflectance (2404.15812v2)

Published 24 Apr 2024 in cs.CV and astro-ph.IM

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.

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References (32)
  1. Canopy near-infrared reflectance and terrestrial photosynthesis. Science Advances, 3(3), Mar. 2017. ISSN 2375-2548. doi: 10.1126/sciadv.1602244. URL http://dx.doi.org/10.1126/sciadv.1602244.
  2. Dynamic world, near real-time global 10 m land use land cover mapping. Scientific Data, 9(1), June 2022. ISSN 2052-4463. doi: 10.1038/s41597-022-01307-4. URL http://dx.doi.org/10.1038/s41597-022-01307-4.
  3. A unified vegetation index for quantifying the terrestrial biosphere. Science Advances, 7(9), Feb. 2021. ISSN 2375-2548. doi: 10.1126/sciadv.abc7447. URL http://dx.doi.org/10.1126/sciadv.abc7447.
  4. The harmonized landsat and sentinel-2 surface reflectance data set. Remote Sensing of Environment, 219:145–161, Dec. 2018. ISSN 0034-4257. doi: 10.1016/j.rse.2018.09.002. URL http://dx.doi.org/10.1016/j.rse.2018.09.002.
  5. Sentinel-2: Esa’s optical high-resolution mission for gmes operational services. Remote Sensing of Environment, 120:25–36, May 2012. ISSN 0034-4257. doi: 10.1016/j.rse.2011.11.026. URL http://dx.doi.org/10.1016/j.rse.2011.11.026.
  6. Evaluating the capabilities of sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 82:83–92, Aug. 2013. ISSN 0924-2716. doi: 10.1016/j.isprsjprs.2013.04.007. URL http://dx.doi.org/10.1016/j.isprsjprs.2013.04.007.
  7. Array programming with numpy. Nature, 585(7825):357–362, Sept. 2020. ISSN 1476-4687. doi: 10.1038/s41586-020-2649-2. URL http://dx.doi.org/10.1038/s41586-020-2649-2.
  8. Spectralgpt: Spectral remote sensing foundation model. 2023. doi: 10.48550/ARXIV.2311.07113. URL https://arxiv.org/abs/2311.07113.
  9. S. Hoyer and J. Hamman. xarray: N-d labeled arrays and datasets in python. Journal of Open Research Software, 5(1):10, Apr. 2017. ISSN 2049-9647. doi: 10.5334/jors.148. URL http://dx.doi.org/10.5334/jors.148.
  10. Foundation models for generalist geospatial artificial intelligence, 2023. URL https://arxiv.org/abs/2310.18660.
  11. Large carbon uptake by an unmanaged 250-year-old deciduous forest in central germany. Agricultural and Forest Meteorology, 118(3–4):151–167, Sept. 2003. ISSN 0168-1923. doi: 10.1016/s0168-1923(03)00115-1. URL http://dx.doi.org/10.1016/S0168-1923(03)00115-1.
  12. X. Li and A. Strahler. Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: effect of crown shape and mutual shadowing. IEEE Transactions on Geoscience and Remote Sensing, 30(2):276–292, Mar. 1992. ISSN 0196-2892. doi: 10.1109/36.134078. URL http://dx.doi.org/10.1109/36.134078.
  13. An algorithm for the retrieval of albedo from space using semiempirical brdf models. IEEE Transactions on Geoscience and Remote Sensing, 38(2):977–998, Mar. 2000. ISSN 0196-2892. doi: 10.1109/36.841980. URL http://dx.doi.org/10.1109/36.841980.
  14. Earth system data cubes unravel global multivariate dynamics. Earth System Dynamics, 11(1):201–234, Feb. 2020. ISSN 2190-4987. doi: 10.5194/esd-11-201-2020. URL http://dx.doi.org/10.5194/esd-11-201-2020.
  15. A standardized catalogue of spectral indices to advance the use of remote sensing in earth system research. Scientific Data, 10(1), Apr. 2023a. ISSN 2052-4463. doi: 10.1038/s41597-023-02096-0. URL http://dx.doi.org/10.1038/s41597-023-02096-0.
  16. Data cubes for earth system research: Challenges ahead. July 2023b. doi: 10.31223/x58m2v. URL http://dx.doi.org/10.31223/X58M2V.
  17. On-demand earth system data cubes, 2024. URL https://arxiv.org/abs/2404.13105.
  18. On the potential of sentinel-2 for estimating gross primary production. IEEE Transactions on Geoscience and Remote Sensing, 60:1–12, 2022. ISSN 1558-0644. doi: 10.1109/tgrs.2022.3152272. URL http://dx.doi.org/10.1109/TGRS.2022.3152272.
  19. M. Rocklin. Dask: Parallel computation with blocked algorithms and task scheduling. In Proceedings of the Python in Science Conference, SciPy. SciPy, 2015. doi: 10.25080/majora-7b98e3ed-013. URL http://dx.doi.org/10.25080/Majora-7b98e3ed-013.
  20. J. Ross. The radiation regime and architecture of plant stands. Springer Netherlands, 1981. ISBN 9789400986473. doi: 10.1007/978-94-009-8647-3. URL http://dx.doi.org/10.1007/978-94-009-8647-3.
  21. A bidirectional reflectance model of the earth’s surface for the correction of remote sensing data. Journal of Geophysical Research: Atmospheres, 97(D18):20455–20468, Dec. 1992. ISSN 0148-0227. doi: 10.1029/92jd01411. URL http://dx.doi.org/10.1029/92JD01411.
  22. Monitoring vegetation systems in the Great Plains with ERTS. Technical report, NASA, 1974. URL https://ntrs.nasa.gov/citations/19740022614.
  23. A general method to normalize landsat reflectance data to nadir brdf adjusted reflectance. Remote Sensing of Environment, 176:255–271, Apr. 2016. ISSN 0034-4257. doi: 10.1016/j.rse.2016.01.023. URL http://dx.doi.org/10.1016/j.rse.2016.01.023.
  24. Adjustment of sentinel-2 multi-spectral instrument (msi) red-edge band reflectance to nadir brdf adjusted reflectance (nbar) and quantification of red-edge band brdf effects. Remote Sensing, 9(12):1325, Dec. 2017a. ISSN 2072-4292. doi: 10.3390/rs9121325. URL http://dx.doi.org/10.3390/rs9121325.
  25. Multi-temporal modis–landsat data fusion for relative radiometric normalization, gap filling, and prediction of landsat data. Remote Sensing of Environment, 112(6):3112–3130, June 2008. ISSN 0034-4257. doi: 10.1016/j.rse.2008.03.009. URL http://dx.doi.org/10.1016/j.rse.2008.03.009.
  26. Examination of sentinel-2a multi-spectral instrument (msi) reflectance anisotropy and the suitability of a general method to normalize msi reflectance to nadir brdf adjusted reflectance. Remote Sensing of Environment, 199:25–38, Sept. 2017b. ISSN 0034-4257. doi: 10.1016/j.rse.2017.06.019. URL http://dx.doi.org/10.1016/j.rse.2017.06.019.
  27. Lexcube: Interactive visualization of large earth system data cubes. IEEE Computer Graphics and Applications, 44(1):25–37, Jan. 2024. ISSN 1558-1756. doi: 10.1109/mcg.2023.3321989. URL http://dx.doi.org/10.1109/MCG.2023.3321989.
  28. Bigearthnet: A large-scale benchmark archive for remote sensing image understanding. In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, July 2019. doi: 10.1109/igarss.2019.8900532. URL http://dx.doi.org/10.1109/IGARSS.2019.8900532.
  29. Attention is all you need, 2017. URL https://arxiv.org/abs/1706.03762.
  30. Technical note: A view from space on global flux towers by modis and landsat: the fluxneteo data set. Biogeosciences, 19(11):2805–2840, June 2022. ISSN 1726-4189. doi: 10.5194/bg-19-2805-2022. URL http://dx.doi.org/10.5194/bg-19-2805-2022.
  31. On the derivation of kernels for kernel‐driven models of bidirectional reflectance. Journal of Geophysical Research: Atmospheres, 100(D10):21077–21089, Oct. 1995. ISSN 0148-0227. doi: 10.1029/95jd02371. URL http://dx.doi.org/10.1029/95JD02371.
  32. Sits-former: A pre-trained spatio-spectral-temporal representation model for sentinel-2 time series classification. International Journal of Applied Earth Observation and Geoinformation, 106:102651, Feb. 2022. ISSN 1569-8432. doi: 10.1016/j.jag.2021.102651. URL http://dx.doi.org/10.1016/j.jag.2021.102651.

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