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UID:pretalx-foss4g-europe-2024-academic-track-GTHSBN@talks.staging.osgeo.or
 g
DTSTART;TZID=EET:20240703T150000
DTEND;TZID=EET:20240703T153000
DESCRIPTION:The Sentinel-2 mission\, pivotal to the European Space Agency's
  Copernicus program\, features two satellites with the MultiSpectral Instr
 ument (MSI) for high-to-medium resolution (10-60 m) imaging in visible (VI
 S)\, near-infrared (NIR)\, and shortwave infrared (SWIR) bands. Its 180° 
 satellite phasing allows for a 5-day revisit time at the equator\, essenti
 al for Earth Observation (EO) tasks. Sentinel-2 Surface Reflectance (SR) i
 s crucial in detailed Earth surface analysis. However\, for enhanced accur
 acy in SR data\, it is imperative to perform adjustments that simulate a n
 adir viewing perspective (Roy et al.\, 2016). This correction mitigates th
 e directional effects caused by the anisotropy of SR and the variability i
 n sunlight and satellite viewing angles. Such adjustments are essential fo
 r the consistent comparison of images captured at different times and unde
 r varying conditions. This is particularly critical for processing and ana
 lysing Earth System Data Cubes (ESDCs\, Mahecha et al.\, 2020)\, which are
  increasingly used due to their organised spatiotemporal structure and the
  ease of their generation from cloud-stored data (Montero et al.\, 2023).\
 n\nThe MODIS BRDF/Albedo product presents spectral Bidirectional Reflectan
 ce Distribution Function (BRDF) model parameters\, enabling the calculatio
 n of directional reflectance across any specified sensor viewing and solar
  angles. Building on this foundation\, Roy et al. (2008\, 2016) introduced
  a novel approach leveraging MODIS BRDF parameters\, named the c-factor\, 
 for the adjustment of Landsat SR data. This adjustment produces Nadir BRDF
  Adjusted Reflectance (NBAR) by multiplying the observed Landsat SR with t
 he ratio of reflectances predicted by the MODIS BRDF model for both the ob
 served Landsat SR and a standard nadir view under fixed solar zenith condi
 tions. Subsequently\, Roy et al. (2017) expanded this method to include ad
 justments for multiple Sentinel-2 spectral bands (VIS to SWIR).\n\nWhile t
 he c-factor method facilitates straightforward computation for individual 
 Sentinel-2 images\, there is a notable absence of a unified Python framewo
 rk to apply this conversion uniformly across multiple images\, especially 
 for ESDCs derived from cloud-stored data.\n\nTo bridge this gap\, we intro
 duce “sen2nbar\,” a Python package specifically developed to convert S
 entinel-2 SR data to NBAR. This tool is versatile\, aiming for converting 
 both individual images and ESDCs generated from cloud-stored data\, thus s
 treamlining the conversion process for Sentinel-2 data users.\n\nThe "sen2
 nbar" package\, meticulously designed for simplicity\, facilitates the dir
 ect conversion of Sentinel-2 Level 2A (L2A) SR data to NBAR through a sing
 le function. To streamline this process\, the package is segmented into mu
 ltiple modules\, each dedicated to specific tasks within the NBAR computat
 ion pipeline. These modules include functions for extracting sun and senso
 r viewing angles from metadata\, calculating geometric and volumetric kern
 els\, computing the BRDF model\, and determining the c-factor.\n\n“sen2n
 bar” supports NBAR calculations for three distinct data structures:\n\n1
 . **Complete scenes via SAFE files**: Users can input a local SAFE file fr
 om a Sentinel-2 L2A scene. The package processes this file\, generating a 
 new folder where each spectral band is adjusted to NBAR at its original re
 solution. The adjusted images are saved as Cloud Optimised GeoTIFF (COG) f
 iles\, with an option for users to choose standard GeoTIFF formats instead
 .\n\n2. **Xarray Data Arrays via “stackstac”**: For ESDCs obtained as 
 xarray data array objects from a SpatioTemporal Asset Catalog (STAC) using
  stackstac and pystac-client\, “sen2nbar” requires the xarray object\,
  the STAC endpoint\, and the Sentinel-2 L2A collection name. This informat
 ion allows the package to access STAC for metadata retrieval necessary for
  adjusting the data cube. The spatial coverage and resolution in this scen
 ario might differ from complete scenes\, and "sen2nbar" adjusts only the s
 pecific area and timeframe retrieved for the given resolution.\n\n3. **Xar
 ray Data Arrays via “cubo”**: When users have ESDCs formed as xarray d
 ata arrays through cubo\, which builds upon stackstac and incorporates the
  STAC endpoint and the collection name as attributes\, “sen2nbar” dire
 ctly adjusts these to NBAR\, utilising the methodology described in the st
 ackstac case.\n\nFor the latter two scenarios\, “sen2nbar” works witho
 ut writing files to disk\, instead returning an xarray data array object c
 ontaining the NBAR values. The package is designed to handle available ban
 ds without errors for missing bands\, acknowledging that users may not req
 uire all bands and might have generated ESDCs with selected bands. Additio
 nally\, if the input arrays are ‘lazy’ arrays\, created using dask arr
 ays (a default in stackstac or cubo)\, “sen2nbar” executes calculation
 s in parallel\, ensuring efficient computation of NBAR values.\n\nImportan
 tly\, “sen2nbar” automatically harmonises SR data for images with a pr
 ocessing baseline of 04.00 or higher before performing NBAR\, ensuring con
 sistency and accuracy in the processed data.\n\n"sen2nbar" efficiently com
 putes NBAR values from Sentinel-2 L2A SR data. The software supports compl
 ete SAFE files processing as well as the adjustment of ESDCs sourced from 
 STAC and COG files\, utilising tools such as “stackstac” and “cubo
 ”. This versatility is encapsulated in a streamlined design\, allowing f
 or the adjustment of various data formats through a single\, user-friendly
  tool\, adapted to diverse user requirements.\n\n"sen2nbar" is anticipated
  to become a key resource for geospatial Python users\, especially in Eart
 h System research. This tool is set to improve analyses conducted by scien
 tists and students by significantly reducing the time and effort tradition
 ally spent on technical adjustments. Its impact is expected to be particul
 arly profound for multitemporal analyses\, facilitating more efficient and
  streamlined investigations. This includes Artificial Intelligence (AI) re
 search\, particularly for studies involving multidimensional EO data. By u
 tilising "sen2nbar"\, AI-based research can achieve more reliable outcomes
 \, enhancing the overall quality and credibility of the findings.\n\nThe 
 “sen2nbar” package is open-source and readily available on GitHub (htt
 ps://github.com/ESDS-Leipzig/sen2nbar) under an MIT License. This encourag
 es contributions from the global community\, fostering collaborative devel
 opment and continuous improvement. While prior experience in Remote Sensin
 g can be advantageous for users\, it is not a prerequisite for using it. T
 he package is equipped with comprehensive documentation and tutorials\, al
 l designed to be beginner-friendly and facilitate easy adoption of the pac
 kage.
DTSTAMP:20260417T012418Z
LOCATION:Omicum
SUMMARY:Facilitating advanced Sentinel-2 analysis through a simplified comp
 utation of Nadir BRDF Adjusted Reflectance - David Montero Loaiza
URL:https://talks.staging.osgeo.org/foss4g-europe-2024-academic-track/talk/
 GTHSBN/
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