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projects:earth_observation:sen2cor_processing_handbook [2024/12/06 09:45] – kymkiprojects:earth_observation:sen2cor_processing_handbook [2024/12/06 09:56] (current) – kymki
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-==== The Sen2cor Processing Handbook ====+==== The Sentinel 2 Data Processing Handbook ====
  
 This post is work in progress. You have been warned! This post is work in progress. You have been warned!
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 However, some atmospheric effects are highly variable over the Earth’s surface and can be difficult to correct in Landsat imagery. While it is not always necessary to atmospherically correct Landsat data to surface values, there are instances where this level of correction is needed. In general, absolute atmospheric corrections are needed when (1) an empirical model is being created for application beyond the data used to develop it, (2) there is a comparison being made to ground reflectance data such as a field-based spectroradi- ometer, or (3) as an alternative to relative correction when comparisons are being made across multiple images. All atmospheric correction methods have associated assump- tions about the target and the nature of the atmospheric particles or emissivity (for land surface temperature). There are numerous atmospheric correction methods available, ranging from simple approaches that use only within-image information such as dark object subtraction (Chavez 1988), to more complex and data-intensive approaches such as the method used for the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) products (Masek et al. 2006) However, some atmospheric effects are highly variable over the Earth’s surface and can be difficult to correct in Landsat imagery. While it is not always necessary to atmospherically correct Landsat data to surface values, there are instances where this level of correction is needed. In general, absolute atmospheric corrections are needed when (1) an empirical model is being created for application beyond the data used to develop it, (2) there is a comparison being made to ground reflectance data such as a field-based spectroradi- ometer, or (3) as an alternative to relative correction when comparisons are being made across multiple images. All atmospheric correction methods have associated assump- tions about the target and the nature of the atmospheric particles or emissivity (for land surface temperature). There are numerous atmospheric correction methods available, ranging from simple approaches that use only within-image information such as dark object subtraction (Chavez 1988), to more complex and data-intensive approaches such as the method used for the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) products (Masek et al. 2006)
 +
 +=== The Topography ===
 +
 +The processes of georeferencing (alignment of imagery to its correct geographic location) and orthorectifying (correction for the effects of relief and view direction on pixel location) are components of geometric correction necessary to ensure the exact positioning of an image. Imagery can be positioned relative to the datum, topog- raphy, or other data types, including reference data and additional geospatial layers that might be used in the analyses.
 +
 +Discrepancies should be corrected prior to analysis using a process known as co-registration (often referred to as just registration). Registration involves aligning data layers relative to one another, while georef- erencing involves aligning layers to the correct geographic location. Registration is a critical step in preprocessing Landsat imagery for ecological analysis, since a misregis- tration can result in significant errors, especially in change detection analyses (Sundaresan et al. 2007). When relating Landsat data to ancillary georeferenced data, such as GPS-marked plot data, images should be georeferenced rather than registered to maintain alignment between data. There are numerous approaches for both georefer- encing and registering Landsat data, and the process might involve a simple pixel shift or a more complex auto- mated feature detection and matching between images (for review, see Brown 1992, Zitová and Flusser 2003).
 +
 +Solar correction does not account for illumination effects from slope, aspect, and elevation that can cause variations in reflectance values for similar features with different terrain positions (Riaño et al. 2003). Topographic correction is the process used to account for these effects. While this correction is not always required, it can be especially important for applications in mountain systems or rugged terrain (Colby 1991, Riaño et al. 2003, Shepherd and Dymond 2003), which are common settings for sat- ellite monitoring due to the difficulty of accessing these environments for field measurements.
 +
 +An important distinction should be made between top- ographic and terrain correction. Topographic correction is a radiometric process while terrain correction is geometric in nature. Although Landsat Level-1 products are terrain corrected, this does not account for the same effects as a topographic correction. Terrain correction ensures each pixel is displayed as viewed from directly above regardless of topography or view angle, and, while important, does not account for the same effects as topographic correction.
 +
 +While this preprocessing step can be more important than atmospheric correction for some applications in topographically complex regions (Vanonckelen et al. 2013), this step is not needed for every scenario.
 +
 +Correcting for these they may be computationally costly and the corrections themselves are imperfect. They may introduce artifacts in the data, for instance, or only partially capable of correcting for the effect they are designed for. The correction for these distortions and how they are addressed are often mentioned in bullet-point fashion in documentation, but without much reference, reasoning or justification.
 +
 +== Level 0 == 
 +
 +The Sentinel-2 [[https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/product-types/level-0|Level 0 product,]] unlike L1C, is not available for public access and is the first processing level performed by the Payload Data Ground Segment (PDGS). Its processing step takes the MSI raw data as input from th Copernicus Ground Segment and ..
 +
 +    error checks the satellite telemetry data,
 +    generates a preliminary low-res image and cloudmask for early filtering of data of poor quality,
 +    dates the individual lines in the recieved image to enable the exact capture time of each ISP within a predefined granule (geographic region) to be recorded,
 +    packages Instrument Source Packets obtained from the satellite ground station network into granules
 +    
 +== Level-0 Consolidated ==
 +
 +This intermediary product contains L-0 and all the meta-data required for subsequent L1 processing. These packets of data are compressed and stored. Like the L0 product L0C is not available to the public.
 +
 +== Level-1 A ==
 +
 +This processing step refers to the decompression of the L0C product and applies no processing beyond that.
 +
 +== Level-1 B ==
 +
 +   1.  Level-1B radiometric processing, including:
 +
 +    dark signal correction
 +    pixel response non-uniformity correction
 +    crosstalk correction
 +    defective pixels identification
 +    high spatial resolution bands restoration (de-convolution and de-noising)
 +    binning of the 60 m spectral bands.
 +
 +   2. Resampling on the common geometry grid for registration between the Global Reference Image (GRI) and the reference band (B4 by default).
 +   3. Collection of the tie-points from the two images for registration between the GRI and the reference band.
 +   4. Tie-points filtering for image-GRI registration: filtering of the tie-points over several areas. A minimum number of tie-points is required.
 +   5. Refinement of the viewing model using the initialised viewing model and and Ground Control Points (GCPs). The output refined model ensures registration between the GRI and the reference band.
 +   6. Level-1B imagery compression utilises the JPEG2000 algorithm.
 +   
 +== Level 1 ==
 +
 +‌‌Sentinel-2 MSI L1C data undergoes a number of pre-processing steps before it is ready for use. These steps include radiometric and geometric correction, atmospheric correction, and cloud and water masking.‌‌‌‌‌‌‌‌DEM https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/definitions
 +
 +Radiometric correction is applied to adjust the data to account for variations in the instrument's sensitivity and to remove any effects of the atmosphere on the measured radiance.
 +
 +Geometric correction is applied to remove any distortion in the image caused by the satellite's motion and to project the data onto a consistent map projection.
 +
 +Atmospheric correction is applied to remove the effects of the atmosphere on the measured radiance. This step is necessary because the atmosphere can cause the measured radiance to be either higher or lower than the actual surface reflectance.
 +
 +Cloud and water masking is applied to identify and mask out any clouds or water bodies in the image. This step is necessary because clouds and water can interfere with the analysis of the data.
 +
 +Overall, the pre-processing steps applied to Sentinel-2 MSI L1C data are designed to correct for various factors that can affect the quality of the data and to make the data more consistent and usable for various applications.
 +
 +== Outlook and advice == 
 +
 +We recommend taking a parsimonious approach to preprocessing; correct the artifacts necessary for a particular application, but avoid unnecessary steps that may introduce additional artifacts without gaining additional value (Song et al. 2001, Riaño et al. 2003, Kennedy et al. 2009).
 +
 +Sources, further reading:
 +
 +    Young, Nicholas & Anderson, Ryan & Chignell, Stephen & Vorster, Anthony & Lawrence, Rick & Evangelista, Paul. 2017. A survival guide to Landsat preprocessing. Ecology. 98. 920-932. 10.1002/ecy.1730.
 +    Gyanesh Chander, Brian L. Markham, Dennis L. Helder. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors, Remote Sensing of Environment, Volume 113, Issue 5, Pages 893-903,‌‌