Algorithm descriptions
EnMAP Level 1B data reader
The EnPT reader module allows you to read in EnMAP Level-1B data in the official format as provided by the EnMAP Ground Segment. After data extraction EnPT expects a folder that includes the following files:
Filename
Description
ENMAP*L1B*-HISTORY.XML
EnMAP ground segment history file for L1B processing
ENMAP*L1B*-LOG.XML
EnMAP ground segment log file
ENMAP*L1B*-METADATA.XML
L1B metadata file
ENMAP*L1B*-QL_PIXELMASK_SWIR.TIF
Defective pixel mask SWIR
ENMAP*L1B*-QL_PIXELMASK_VNIR.TIF
Defective pixel mask VNIR
ENMAP*L1B*-QL_QUALITY_CIRRUS.TIF
Quality cirrus
ENMAP*L1B*-QL_QUALITY_CLASSES.TIF
Quality classes
ENMAP*L1B*-QL_QUALITY_CLOUD.TIF
Quality cloud
ENMAP*L1B*-QL_QUALITY_CLOUDSHADOW.TIF
Quality cloud shadow
ENMAP*L1B*-QL_QUALITY_HAZE.TIF
Quality haze
ENMAP*L1B*-QL_QUALITY_SNOW.TIF
Quality snow
ENMAP*L1B*-QL_QUALITY_TESTFLAGS_SWIR.TIF
Quality test flags SWIR
ENMAP*L1B*-QL_QUALITY_TESTFLAGS_VNIR.TIF
Quality test flags VNIR
ENMAP*L1B*-QL_SWIR.TIF
SWIR quicklook image
ENMAP*L1B*-QL_VNIR.TIF
VNIR quicklook image
ENMAP*L1B*-SPECTRAL_IMAGE_SWIR.TIF
EnMAP L1B SWIR spectral image
ENMAP*L1B*-SPECTRAL_IMAGE_VNIR.TIF
EnMAP L1B VNIR spectral image
EnPT reads the raster files lazily / on-demand to save memory during execution. Metadata read from the ENMAP*-METADATA.XML file are stored in memory and used at multiple stages of the pre-processing pipeline.
Conversion to top-of-atmosphere radiance
The image data is directly transformed from digital numbers (DNs, as provided by the DLR) to top-of-atmosphere radiance in mW/m2/sr/nm.
Filling the gap between adjacent EnMAP images
There is a gap of around 20 pixels in along-track-direction between the VNIR and SWIR image of the official Level-1B product and up to 1 pixel in across-track direction. Since the Level-2A processing in EnPT requires overlapping data, the data is cut to the overlap area, which in turn leads to blank spaces between two adjacent Level-2A tiles. To create overlapping Level-2A tiles in along-track direction for a further image mosaicking, EnPT offers the opportunity to consider information from neighboring Level-1B image tiles which is then appended to the actual EnMAP image tile to be processed.
Dead pixel correction
The EnPT dead pixel correction uses the pixel masks provided by the official Level-1B product and interpolates the EnMAP image data at the indicated dead pixel positions. It supports two interpolation algorithms:
- spectral interpolation (default)
Interpolates the data in the spectral domain.
Points outside the data range are extrapolated.
possible interpolation methods: linear, nearest, zero, slinear, quadratic, cubic, etc.
- spatial interpolation
Interpolates the data spatially.
Remaining missing data positions (e.g., outermost columns) are spectrally interpolated.
possible interpolation methods: linear, bilinear, cubic, spline
Atmospheric correction
EnPT uses SICOR (Sensor Independent Atmosperic Correction of optical Earth observation data from multi- and hyperspectral instruments) for atmospheric correction, i.e., for the conversion of TOA-(top-of-atmosphere) radiance to BOA- (bottom-of-atmosphere / surface) reflectance. SICOR is a Python based open-source package developed at the German Research Centre for Geosciences (GFZ) Potsdam. For details on the underlying algorithm, please refer to the documentation pages of SICOR.
Optionally, EnPT uses the Polymer algorithm for atmospheric correction over water (Steinmetz et al. (2011)). Polymer is a spectral matching algorithm in which atmospheric and oceanic signals are obtained simultaneously using the fully available visible spectrum. The algorithm was developed by Hygeos (https://www.hygeos.com/); it is available as a Python package and it has been largely applied to ocean colour sensors. The Polymer algorithm was integrated into EnPT using the wrapper module ACwater developed at AWI Bremerhaven in cooperation with GFZ. In addition, Polymer was further adapted to process EnMAP L1B satellite data. For details on the underlying Polymer algorithm, please refer to Steinmetz et al. (2011) and Soppa et al. (2021).
The following ACwater/Polymer outputs can be included into the EnMAP Level 2A product generated by EnPT (added in version 0.19.0):
normalized water leaving reflectance
chlorophyll-a concentration (logchl)
fb coefficient that scales the backscattering coefficient of particles (logfb)
reflectance of the sun glint (Rgli)
TOA reflectance at 865 nm corrected for Rayleigh scattering (Rnir)
quality flags (bitmask).
Spatial Co-Registration
EnPT enables the geometric adaptation of EnMAP data to a user-provided image scene (e.g. Sentinel-2). Spatial misregistrations are detected using the open-source Python package AROSICS (An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data). It has been developed at the German Research Centre for Geosciences (GFZ) Potsdam. For detailed algorithm description and use cases refer to the corresponding (open-access) paper that can be found here: Scheffler D, Hollstein A, Diedrich H, Segl K, Hostert P. AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data. Remote Sensing. 2017; 9(7):676.
In EnPT, AROSICS is used to automatically compute thousands of tie points between a selected EnMAP band the user-provided reference image. The computed shifts are considered in the orthorectification step.
Orthorectification
EnMAP Level 1B data are provided in sensor geometry, i.e., the image data don’t have map coordinates but only image coordinates. For the ortho-rectification of the data EnPT uses a set of Rational Polynomial Coefficients (RPCs) provided for each band of the two EnMAP subsystems (VNIR and SWIR). Together with a user provided digital elevation model these RPC coefficients enable a highly accurate assignment of map coordinates to each pixel of the EnMAP Level-1B images. The RPC coefficients already include the official information about detector coregistration and keystone. This way image map coordinates are calculated internally for each pixel and band considering the spatial misregistrations estimated by AROSICS on demand. Resampling is done using a fast KDTree gaussian weighting neighbour approach implemented in the Python library pyresample (find the documentation here).
In this processing step, the EnMAP VNIR is merged with the SWIR subsystem and from now on stored in a single 3D array.
EnMAP Level 2A data writer
The EnPT writer module writes the computed EnMAP Level-2A data (orthorectified bottom-of-atmosphere reflectance for land surfaces or normalized water-leaving reflectance for water surfaces if the atmospheric correction runs in water or combined mode) to disk after finishing the processing pipeline. The data format produced by EnPT is based on the official Level-2A format of the ground segment. However, due to differences in the underlying algorithms, EnPT also produces a slightly different Level-2A data format. The current differences are summarized below:
Filename
official L2A format
EnPT
Description
ENMAP*L2A*.log
no
yes
EnPT log file
ENMAP*L2A*-HISTORY.XML
yes
no
EnMAP ground segment history file for L2A processing
ENMAP*L2A*-LOG.XML
yes
no
EnMAP ground segment log file
ENMAP*L2A*-METADATA.XML
yes
yes
L2A metadata file
ENMAP*L2A*-QL_PIXELMASK.TIF
yes
planned
Defective pixel mask
ENMAP*L2A*-QL_QUALITY_CIRRUS.TIF
yes
yes
Quality cirrus
ENMAP*L2A*-QL_QUALITY_CLASSES.TIF
yes
yes
Quality classes
ENMAP*L2A*-QL_QUALITY_CLOUD.TIF
yes
yes
Quality cloud
ENMAP*L2A*-QL_QUALITY_CLOUDSHADOW.TIF
yes
yes
Quality cloud shadow
ENMAP*L2A*-QL_QUALITY_HAZE.TIF
yes
yes
Quality haze
ENMAP*L2A*-QL_QUALITY_SNOW.TIF
yes
yes
Quality snow
ENMAP*L2A*-QL_QUALITY_TESTFLAGS.TIF
yes
no
Quality test flags
ENMAP*L2A*-QL_SWIR.TIF
yes
yes
SWIR quicklook image
ENMAP*L2A*-QL_VNIR.TIF
yes
yes
VNIR quicklook image
ENMAP*L2A*-SPECTRAL_IMAGE.TIF
yes
yes
EnMAP L2A bottom-of-atmosphere reflectance (land) or normalized water leaving reflectance (water)
ENMAP*L2A*-ACOUT_POLYMER_*RNIR.TIF
no
optional
TOA reflectance at 863 nm corrected for Rayleigh scattering
ENMAP*L2A*-ACOUT_POLYMER_*RGLI.TIF
no
optional
Reflectance of the sun glint predicted from ECMWF wind speed
ENMAP*L2A*-ACOUT_POLYMER_*LOGCHL.TIF
no
optional
Chlorophyll-a concentration (mg/m3, in 10-based logarithm)
ENMAP*L2A*-ACOUT_POLYMER_*LOGFB.TIF
no
optional
Particle scattering factor fb in Park & Ruddick (2005) (in 10-based logarithm)
ENMAP*L2A*-ACOUT_POLYMER_*BITMASK.TIF
no
optional
Polymer quality flags (more information below)
The Polymer quality flags bitmask represents a bit-encoded product with the following flag values:
Flag name
Flag value
Description
LAND
1
Land mask
CLOUD_BASE
2
Polymer’s basic cloud mask
L1_INVALID
4
Invalid level1 pixel
NEGATIVE_BB
8
(deprecated flag)
OUT_OF_BOUNDS
16
Retrieved marine parameters are outside valid bounds
EXCEPTION
32
A processing error was encountered
THICK_AEROSOL
64
Thick aerosol flag
HIGH_AIR_MASS
128
Air mass exceeds 5
EXTERNAL_MASK
512
Pixel was masked using external mask
CASE2
1024
Pixel was processed in “case2” mode
INCONSISTENCY
2048
Inconsistent result was detected (atmospheric reflectance out of bounds)
ANOMALY_RWMOD_BLUE
4096
Excessive difference was found at 412nm between Rw and Rwmod
Value 0 represents water (all fine, no flags), value -9999 represents no-data.