Sentinel-3A (S3A) Albedo Euro Data Cube (EDC) Processing Pipeline

1. Divide Area Into Tiles
2. Fetch S3A OLCI & SLSTR products using SentinelHub (SH)
3. Start Multiprocessing
4. Simple Cloud Detection Algorithm (SCDA) & Masking Non-Ice/Snow areas
5. Get Forecasted Aerosol data from Copernicus Atmospgeric Monotoring Service (cams)
6. Compute outputs with pySICE
7. Merge all tiles into daily mosaics
8. Load all daily mosiacis to one multidimensional NetCDF file
9. Upload to Dataverse and Thredds Server

The steps above are encapsulated in ./src/pysicehub.py.

In more detail:

Divide Area Into Tiles

• Every area is divided into 50km x 50km tiles with a 10 km buffer

Fetch S3A OLCI & SLSTR products

• Download OLCI and SLSTR products for each tile concurrently through SH
• Donwloads the S3A scene with the lowest Sun Zenith Angle

Start Multiprocessing

• Use a specified number of cores (default is 12) to process each tile in parrallel ’

Simple Cloud Detection Algorithm (SCDA) & Masking Non-Ice/Snow areas

• Using SLSTR, clouds in every tile are detected and masked
• Using a predifened mask, all areas in the tile that are not snow or ice are masked

Get Forecasted Aerosol data from Copernicus Atmospgeric Monotoring Service (cams)

• Download raw forecasted aerosol optical depth at 550 nm and 670 nm for each tile
• interpolate and regrid to SICE 500m grid
• compute ångstrøm paramter

pySICE

• Run ./src/pysice.py on all tiles

Mosaic

• Merge all tiles into moasic

Outputs

File Name	Description	Units
grain_diameter		mm
snow_specific_surface_area		m2kg-1
sza	sun zenith angle
vza	viewing zenith angle
saa	sun azimuth angle
vaa	viewing azimuth angle
AOD_550	Aerosol Optical Depth (550nm)	m
ANG	ångstrøm parameter
albedo_bb_planar_nir	near infrared broadband planar albedo	700-2400 nm
albedo_bb_planar_sw	shortwave broadband planar albedo	300-2400 nm
albedo_bb_planar_vis	visible broadband planar albedo	300-700 nm
albedo_bb_spherical_nir	near infrared broadband spherical albedo	700-2400 nm
albedo_bb_spherical_sw	shortwave broadband spherical albedo	300-2400 nm
albedo_bb_spherical_vis	visible broadband spherical albedo	300-700 nm
r_TOA_01..21_reflectance	top of the atmosphere reflectance (OLCI bands 1 through 21)
rBRR_01..21	bottom of the atmosphere surface reflectance (OLCI bands 1 through 21)
albedo_spectral_planar_01..21	spectral planar albedo (OLCI bands 1 through 21)
isnow	See next table.

Diagnostic Code	Description
0	clean snow
1	polluted snow
6	polluted snow for which r0 was calculated and not derived from observations
7	polluted snow of calculated spherical albedo in bands 1 and 2 >0.98 reprocessed as clean snow
100	sza>75, no retrival
102	TOA reflectance at band 21 < 0.1, no retrieval
104	grain_diameter < 0.1, no retrieval, potential cloud flag
-n	impossible to solve polluted snow albedo equation at band n

Development Environment

Python

Run the following using ./environement.yml in order to set up an identical Anaconda Python environment.

conda env create -f environement.yml

And then run conda activate SICE to activate the SICE environment.

ResolvePackageNotFound error can be raised. In that case, run conda env export --no-builds > environment.yml instead.