Sentinel-3#
Let’s use EOReader to open and read some Sentinel-3 bands and see the specificities of OLCI and SLSTR constellations.
# Imports
import os
# EOReader
from eoreader.reader import Reader
from eoreader.bands import YELLOW, Oa21, RED, SWIR_2, F1
# Declare the reader (only once)
reader = Reader()
# Create logger
import logging
from sertit import logs
logger = logging.getLogger("eoreader")
logs.init_logger(logger)
Sentinel-3 OLCI#
# First of all, let's focus on Sentinel-3 OLCI data
olci_path = os.path.join(
"/home", "prods", "S3",
"S3A_OL_1_EFR____20191215T105023_20191215T105323_20191216T153115_0179_052_322_2160_LN1_O_NT_002.zip"
)
olci_prod = reader.open(olci_path, remove_tmp=True)
olci_prod
eoreader.S3OlciProduct 'S3A_OL_1_EFR____20191215T105023_20191215T105323_20191216T153115_0179_052_322_2160_LN1_O_NT_002'
Attributes:
condensed_name: 20191215T105023_S3_OLCI_EFR
path: /home/prods/S3/S3A_OL_1_EFR____20191215T105023_20191215T105323_20191216T153115_0179_052_322_2160_LN1_O_NT_002.zip
constellation: Sentinel-3 OLCI
sensor type: Optical
product type: OL_1_EFR___
default pixel size: 300.0
default resolution: 300.0
acquisition datetime: 2019-12-15T10:50:23.000506
band mapping:
COASTAL_AEROSOL: 3
BLUE: 4
DEEP_BLUE: 2
GREEN: 6
YELLOW: 7
RED: 8
VEGETATION_RED_EDGE_1: 11
VEGETATION_RED_EDGE_2: 12
VEGETATION_RED_EDGE_3: 16
NIR: 17
NARROW_NIR: 17
WATER_VAPOUR: 20
Oa01: 1
Oa09: 9
Oa10: 10
Oa13: 13
Oa14: 14
Oa15: 15
Oa18: 18
Oa19: 19
Oa21: 21
GREEN_I: 5
needs extraction: False
# Get the bands information
olci_prod.bands
eoreader.SpectralBand 'Oa01'
Attributes:
id: 1
eoreader_name: Oa01
common_name:
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 400.0
Bandwidth (nm): 15.0
description: Aerosol correction, improved water constituent retrieval
eoreader.SpectralBand 'Oa02'
Attributes:
id: 2
eoreader_name: DEEP_BLUE
common_name:
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 412.5
Bandwidth (nm): 10.0
description: Yellow substance and detrital pigments (turbidity)
eoreader.SpectralBand 'Oa03'
Attributes:
id: 3
eoreader_name: COASTAL_AEROSOL
common_name: coastal
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 442.5
Bandwidth (nm): 10.0
description: Chlorophyll absorption maximum, biogeochemistry, vegetation
eoreader.SpectralBand 'Oa04'
Attributes:
id: 4
eoreader_name: BLUE
common_name: blue
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 490.0
Bandwidth (nm): 10.0
description: High Chlorophyll
eoreader.SpectralBand 'Oa05'
Attributes:
id: 5
eoreader_name: GREEN_I
common_name:
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 510.0
Bandwidth (nm): 10.0
description: Chlorophyll, sediment, turbidity, red tide
eoreader.SpectralBand 'Oa06'
Attributes:
id: 6
eoreader_name: GREEN
common_name: green
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 560.0
Bandwidth (nm): 10.0
description: Chlorophyll reference (Chlorophyll minimum)
eoreader.SpectralBand 'Oa07'
Attributes:
id: 7
eoreader_name: YELLOW
common_name: yellow
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 620.0
Bandwidth (nm): 10.0
description: Sediment loading
eoreader.SpectralBand 'Oa08'
Attributes:
id: 8
eoreader_name: RED
common_name: red
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 665.0
Bandwidth (nm): 10.0
description: Chlorophyll (2nd Chlorophyll absorption maximum), sediment, yellow substance / vegetation
eoreader.SpectralBand 'Oa09'
Attributes:
id: 9
eoreader_name: Oa09
common_name:
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 673.75
Bandwidth (nm): 7.5
description: For improved fluorescence retrieval and to better account for smile together with the bands 665 and 680 nm
eoreader.SpectralBand 'Oa10'
Attributes:
id: 10
eoreader_name: Oa10
common_name:
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 681.25
Bandwidth (nm): 7.5
description: Chlorophyll fluorescence peak, red edge
eoreader.SpectralBand 'Oa11'
Attributes:
id: 11
eoreader_name: VEGETATION_RED_EDGE_1
common_name: rededge
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 708.75
Bandwidth (nm): 10.0
description: Chlorophyll fluorescence baseline, red edge transition
eoreader.SpectralBand 'Oa12'
Attributes:
id: 12
eoreader_name: VEGETATION_RED_EDGE_2
common_name: rededge
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 753.75
Bandwidth (nm): 7.5
description: O2 absorption/clouds, vegetation
eoreader.SpectralBand 'Oa13'
Attributes:
id: 13
eoreader_name: Oa13
common_name:
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 761.25
Bandwidth (nm): 2.5
description: O2 absorption band/aerosol correction.
eoreader.SpectralBand 'Oa14'
Attributes:
id: 14
eoreader_name: Oa14
common_name:
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 764.375
Bandwidth (nm): 3.75
description: Atmospheric correction
eoreader.SpectralBand 'Oa15'
Attributes:
id: 15
eoreader_name: Oa15
common_name:
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 767.5
Bandwidth (nm): 2.5
description: O2A used for cloud top pressure, fluorescence over land
eoreader.SpectralBand 'Oa16'
Attributes:
id: 16
eoreader_name: VEGETATION_RED_EDGE_3
common_name: rededge
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 778.75
Bandwidth (nm): 15.0
description: Atmos. corr./aerosol corr.
eoreader.SpectralBand 'Oa17'
Attributes:
id: 17
eoreader_name: NIR
common_name: nir
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 865.0
Bandwidth (nm): 20.0
description: Atmospheric correction/aerosol correction, clouds, pixel co-registration
eoreader.SpectralBand 'Oa17'
Attributes:
id: 17
eoreader_name: NARROW_NIR
common_name: nir08
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 865.0
Bandwidth (nm): 20.0
description: Atmospheric correction/aerosol correction, clouds, pixel co-registration
eoreader.SpectralBand 'Oa18'
Attributes:
id: 18
eoreader_name: Oa18
common_name:
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 885.0
Bandwidth (nm): 10.0
description: Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring
eoreader.SpectralBand 'Oa19'
Attributes:
id: 19
eoreader_name: Oa19
common_name:
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 900.0
Bandwidth (nm): 10.0
description: Water vapour absorption/vegetation monitoring (maximum reflectance)
eoreader.SpectralBand 'Oa20'
Attributes:
id: 20
eoreader_name: WATER_VAPOUR
common_name: nir09
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 940.0
Bandwidth (nm): 20.0
description: Water vapour absorption, Atmospheric correction/aerosol correction
eoreader.SpectralBand 'Oa21'
Attributes:
id: 21
eoreader_name: Oa21
common_name:
gsd (m): 300.0
asset_role: reflectance
Center wavelength (nm): 1020.0
Bandwidth (nm): 40.0
description: Atmospheric correction/aerosol correction
⚠️ Note that mapped bands need to be called by their mapped name and the specific ones with their true Sentinel-3 names
# Load the Yellow band and the far NIR one
olci_bands = olci_prod.load([YELLOW, Oa21])
2025-12-23 12:01:48,489 - [DEBUG] - Loading bands ['YELLOW', 'Oa21']
2025-12-23 12:01:48,840 - [DEBUG] - Converting YELLOW to reflectance
/opt/conda/lib/python3.11/site-packages/rasterio/__init__.py:366: NotGeoreferencedWarning: The given matrix is equal to Affine.identity or its flipped counterpart. GDAL may ignore this matrix and save no geotransform without raising an error. This behavior is somewhat driver-specific.
dataset = writer(
/opt/conda/lib/python3.11/site-packages/xarray/namedarray/core.py:264: UserWarning: Duplicate dimension names present: dimensions {'bands'} appear more than once in dims=('bands', 'bands'). We do not yet support duplicate dimension names, but we do allow initial construction of the object. We recommend you rename the dims immediately to become distinct, as most xarray functionality is likely to fail silently if you do not. To rename the dimensions you will need to set the ``.dims`` attribute of each variable, ``e.g. var.dims=('x0', 'x1')``.
self._dims = self._parse_dimensions(dims)
/opt/conda/lib/python3.11/site-packages/xarray/namedarray/core.py:264: UserWarning: Duplicate dimension names present: dimensions {'bands'} appear more than once in dims=('bands', 'bands'). We do not yet support duplicate dimension names, but we do allow initial construction of the object. We recommend you rename the dims immediately to become distinct, as most xarray functionality is likely to fail silently if you do not. To rename the dimensions you will need to set the ``.dims`` attribute of each variable, ``e.g. var.dims=('x0', 'x1')``.
self._dims = self._parse_dimensions(dims)
/opt/conda/lib/python3.11/site-packages/xarray/namedarray/core.py:264: UserWarning: Duplicate dimension names present: dimensions {'bands'} appear more than once in dims=('bands', 'bands'). We do not yet support duplicate dimension names, but we do allow initial construction of the object. We recommend you rename the dims immediately to become distinct, as most xarray functionality is likely to fail silently if you do not. To rename the dimensions you will need to set the ``.dims`` attribute of each variable, ``e.g. var.dims=('x0', 'x1')``.
self._dims = self._parse_dimensions(dims)
/opt/conda/lib/python3.11/site-packages/xarray/namedarray/core.py:264: UserWarning: Duplicate dimension names present: dimensions {'bands'} appear more than once in dims=('bands', 'bands'). We do not yet support duplicate dimension names, but we do allow initial construction of the object. We recommend you rename the dims immediately to become distinct, as most xarray functionality is likely to fail silently if you do not. To rename the dimensions you will need to set the ``.dims`` attribute of each variable, ``e.g. var.dims=('x0', 'x1')``.
self._dims = self._parse_dimensions(dims)
2025-12-23 12:01:49,676 - [DEBUG] - Geocoding YELLOW
2025-12-23 12:01:57,552 - [DEBUG] - Converting Oa21 to reflectance
/opt/conda/lib/python3.11/site-packages/xarray/namedarray/core.py:264: UserWarning: Duplicate dimension names present: dimensions {'bands'} appear more than once in dims=('bands', 'bands'). We do not yet support duplicate dimension names, but we do allow initial construction of the object. We recommend you rename the dims immediately to become distinct, as most xarray functionality is likely to fail silently if you do not. To rename the dimensions you will need to set the ``.dims`` attribute of each variable, ``e.g. var.dims=('x0', 'x1')``.
self._dims = self._parse_dimensions(dims)
/opt/conda/lib/python3.11/site-packages/xarray/namedarray/core.py:264: UserWarning: Duplicate dimension names present: dimensions {'bands'} appear more than once in dims=('bands', 'bands'). We do not yet support duplicate dimension names, but we do allow initial construction of the object. We recommend you rename the dims immediately to become distinct, as most xarray functionality is likely to fail silently if you do not. To rename the dimensions you will need to set the ``.dims`` attribute of each variable, ``e.g. var.dims=('x0', 'x1')``.
self._dims = self._parse_dimensions(dims)
/opt/conda/lib/python3.11/site-packages/xarray/namedarray/core.py:264: UserWarning: Duplicate dimension names present: dimensions {'bands'} appear more than once in dims=('bands', 'bands'). We do not yet support duplicate dimension names, but we do allow initial construction of the object. We recommend you rename the dims immediately to become distinct, as most xarray functionality is likely to fail silently if you do not. To rename the dimensions you will need to set the ``.dims`` attribute of each variable, ``e.g. var.dims=('x0', 'x1')``.
self._dims = self._parse_dimensions(dims)
/opt/conda/lib/python3.11/site-packages/xarray/namedarray/core.py:264: UserWarning: Duplicate dimension names present: dimensions {'bands'} appear more than once in dims=('bands', 'bands'). We do not yet support duplicate dimension names, but we do allow initial construction of the object. We recommend you rename the dims immediately to become distinct, as most xarray functionality is likely to fail silently if you do not. To rename the dimensions you will need to set the ``.dims`` attribute of each variable, ``e.g. var.dims=('x0', 'x1')``.
self._dims = self._parse_dimensions(dims)
2025-12-23 12:01:58,235 - [DEBUG] - Geocoding Oa21
2025-12-23 12:02:05,977 - [DEBUG] - Read YELLOW
2025-12-23 12:02:05,994 - [DEBUG] - Manage nodata for band YELLOW
2025-12-23 12:02:05,999 - [DEBUG] - Converting YELLOW to reflectance (if needed)
2025-12-23 12:02:06,000 - [DEBUG] - Clip the reflectance array to 0 as minimum value (in some cases, reflectance can have higher value than 1)
/opt/conda/lib/python3.11/site-packages/rasterio/warp.py:387: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.
dest = _reproject(
2025-12-23 12:02:09,831 - [DEBUG] - Read Oa21
2025-12-23 12:02:09,849 - [DEBUG] - Manage nodata for band Oa21
2025-12-23 12:02:09,852 - [DEBUG] - Converting Oa21 to reflectance (if needed)
2025-12-23 12:02:09,853 - [DEBUG] - Clip the reflectance array to 0 as minimum value (in some cases, reflectance can have higher value than 1)
# Plot a subsampled version
olci_bands[YELLOW][:, ::10, ::10].plot(robust=True, cmap="mako")
<matplotlib.collections.QuadMesh at 0x7f4bd0b36650>
olci_bands[Oa21][:, ::10, ::10].plot(robust=True, cmap="mako")
<matplotlib.collections.QuadMesh at 0x7f4bd0651010>
Sentinel-3 SLSTR#
# Other SLSTR imports
from eoreader.keywords import SLSTR_VIEW, SLSTR_STRIPE, SLSTR_RAD_ADJUST
from eoreader.products import SlstrRadAdjustTuple, SlstrRadAdjust, SlstrView, SlstrStripe
# Then, let's focus on Sentinel-3 SLSTR data (extracted here, but a zip would work)
slstr_path = os.path.join(
"/home", "prods", "S3",
"S3B_SL_1_RBT____20191115T233722_20191115T234022_20191117T031722_0179_032_144_3420_LN2_O_NT_003.SEN3"
)
slstr_prod = reader.open(slstr_path, remove_tmp=True)
slstr_prod
eoreader.S3SlstrProduct 'S3B_SL_1_RBT____20191115T233722_20191115T234022_20191117T031722_0179_032_144_3420_LN2_O_NT_003'
Attributes:
condensed_name: 20191115T233722_S3_SLSTR_RBT
path: /home/prods/S3/S3B_SL_1_RBT____20191115T233722_20191115T234022_20191117T031722_0179_032_144_3420_LN2_O_NT_003.SEN3
constellation: Sentinel-3 SLSTR
sensor type: Optical
product type: SL_1_RBT___
default pixel size: 500.0
default resolution: 500.0
acquisition datetime: 2019-11-15T23:37:22.254773
band mapping:
GREEN: S1
RED: S2
NIR: S3
NARROW_NIR: S3
SWIR_CIRRUS: S4
SWIR_1: S5
SWIR_2: S6
THERMAL_IR_1: S8
THERMAL_IR_2: S9
S7: S7
F1: F1
F2: F2
needs extraction: False
# Get the bands information
slstr_prod.bands
eoreader.SpectralBand 'F1'
Attributes:
id: F1
eoreader_name: F1
common_name:
gsd (m): 1000.0
asset_role: brightness_temperature
Center wavelength (nm): 3742.0
Bandwidth (nm): 398.0
description: Active fire, brightness temperature, 1km
eoreader.SpectralBand 'F2'
Attributes:
id: F2
eoreader_name: F2
common_name:
gsd (m): 1000.0
asset_role: brightness_temperature
Center wavelength (nm): 10854.0
Bandwidth (nm): 776.0
description: Active fire, brightness temperature, 1km
eoreader.SpectralBand 'S1'
Attributes:
id: S1
eoreader_name: GREEN
common_name: green
gsd (m): 500.0
asset_role: reflectance
Center wavelength (nm): 554.2700000000001
Bandwidth (nm): 19.259999999999998
description: Cloud screening, vegetation monitoring, aerosol
eoreader.SpectralBand 'S2'
Attributes:
id: S2
eoreader_name: RED
common_name: red
gsd (m): 500.0
asset_role: reflectance
Center wavelength (nm): 659.47
Bandwidth (nm): 19.25
description: NDVI, vegetation monitoring, aerosol
eoreader.SpectralBand 'S3'
Attributes:
id: S3
eoreader_name: NIR
common_name: nir
gsd (m): 500.0
asset_role: reflectance
Center wavelength (nm): 868.0
Bandwidth (nm): 20.6
description: NDVI, cloud flagging, pixel co-registration
eoreader.SpectralBand 'S3'
Attributes:
id: S3
eoreader_name: NARROW_NIR
common_name: nir08
gsd (m): 500.0
asset_role: reflectance
Center wavelength (nm): 868.0
Bandwidth (nm): 20.6
description: NDVI, cloud flagging, pixel co-registration
eoreader.SpectralBand 'S4'
Attributes:
id: S4
eoreader_name: SWIR_CIRRUS
common_name: cirrus
gsd (m): 500.0
asset_role: reflectance
Center wavelength (nm): 1374.8
Bandwidth (nm): 20.8
description: Cirrus detection over land
eoreader.SpectralBand 'S5'
Attributes:
id: S5
eoreader_name: SWIR_1
common_name: swir16
gsd (m): 500.0
asset_role: reflectance
Center wavelength (nm): 1613.3999999999999
Bandwidth (nm): 60.68
description: Cloud clearing, ice, snow, vegetation monitoring
eoreader.SpectralBand 'S6'
Attributes:
id: S6
eoreader_name: SWIR_2
common_name: swir22
gsd (m): 1000.0
asset_role: reflectance
Center wavelength (nm): 2255.7
Bandwidth (nm): 50.15
description: Vegetation state and cloud clearing
eoreader.SpectralBand 'S7'
Attributes:
id: S7
eoreader_name: S7
common_name:
gsd (m): 1000.0
asset_role: brightness_temperature
Center wavelength (nm): 3742.0
Bandwidth (nm): 398.0
description: SST, LST, Active fire, brightness temperature, 1km
eoreader.SpectralBand 'S8'
Attributes:
id: S8
eoreader_name: THERMAL_IR_1
common_name: lwir11
gsd (m): 1000.0
asset_role: brightness_temperature
Center wavelength (nm): 10854.0
Bandwidth (nm): 776.0
description: SST, LST, Active fire, brightness temperature, 1km
eoreader.SpectralBand 'S9'
Attributes:
id: S9
eoreader_name: THERMAL_IR_2
common_name: lwir12
gsd (m): 1000.0
asset_role: brightness_temperature
Center wavelength (nm): 12022.5
Bandwidth (nm): 905.0
description: SST, LST, brightness temperature, 1km
⚠️Same remarks than above about mapped and specific band names apply.
⚠️Note that native radiance bands are converted into reflectance, whereas brightness temperature bands are not.
# Load bands with nadir view and stripe B
# (for bands that have a stripe B, the other will load their unique stripe, namely A or I)
# RED: only stripe A
# SWIR_2: has strip 1, B and TDI (c)
# F1: has only stripe I
slstr_bn_bands = slstr_prod.load([RED, SWIR_2, F1], slstr_view="n", slstr_stripe="b")
slstr_bn_bands_2 = slstr_prod.load([RED, SWIR_2, F1], **{SLSTR_VIEW: SlstrView.NADIR, SLSTR_STRIPE: SlstrStripe.B})
2025-12-23 12:02:14,905 - [DEBUG] - Loading bands ['RED', 'SWIR_2', 'F1']
2025-12-23 12:02:15,072 - [DEBUG] - Converting RED to reflectance
2025-12-23 12:02:19,172 - [DEBUG] - Geocoding RED
2025-12-23 12:02:24,109 - [DEBUG] - Converting SWIR_2 to reflectance
2025-12-23 12:02:28,245 - [DEBUG] - Geocoding SWIR_2
2025-12-23 12:02:32,814 - [DEBUG] - Geocoding F1
2025-12-23 12:02:37,050 - [DEBUG] - Read RED
2025-12-23 12:02:37,067 - [DEBUG] - Manage nodata for band RED
2025-12-23 12:02:37,070 - [DEBUG] - Converting RED to reflectance (if needed)
2025-12-23 12:02:37,071 - [DEBUG] - Clip the reflectance array to 0 as minimum value (in some cases, reflectance can have higher value than 1)
2025-12-23 12:02:39,811 - [DEBUG] - Read SWIR_2
2025-12-23 12:02:39,828 - [DEBUG] - Manage nodata for band SWIR_2
2025-12-23 12:02:39,831 - [DEBUG] - Converting SWIR_2 to reflectance (if needed)
2025-12-23 12:02:39,832 - [DEBUG] - Clip the reflectance array to 0 as minimum value (in some cases, reflectance can have higher value than 1)
2025-12-23 12:02:42,428 - [DEBUG] - Read F1
2025-12-23 12:02:42,445 - [DEBUG] - Manage nodata for band F1
2025-12-23 12:02:42,449 - [DEBUG] - Converting F1 to reflectance (if needed)
2025-12-23 12:02:42,450 - [DEBUG] - Clip the reflectance array to 0 as minimum value (in some cases, reflectance can have higher value than 1)
2025-12-23 12:02:45,121 - [DEBUG] - Loading bands ['RED', 'SWIR_2', 'F1']
2025-12-23 12:02:45,122 - [DEBUG] - Read RED
2025-12-23 12:02:45,140 - [DEBUG] - Read SWIR_2
2025-12-23 12:02:45,156 - [DEBUG] - Read F1
⚠️You can use any keyword by importing it or copy its value in string. However, it is always safer to import the keywords and use the enum.
⚠️Please bear in mind that oblique and nadir views are not stackable ! However, you can stack different stripes
⚠️An unsafe behaviour would be to load them one at a time without collocating them: their reprojection grid may vary as their GCPs also vary
# Plot a subsampled version
slstr_bn_bands[RED][:, ::10, ::10].plot(robust=True, cmap="mako")
<matplotlib.collections.QuadMesh at 0x7f4bd0a39090>
slstr_bn_bands[SWIR_2][:, ::10, ::10].plot(robust=True, cmap="mako")
<matplotlib.collections.QuadMesh at 0x7f4bd0151050>
slstr_bn_bands[F1][:, ::10, ::10].plot(robust=True, cmap="mako")
<matplotlib.collections.QuadMesh at 0x7f4bcffcf0d0>
⚠️Sentinel-3 SLSTR radiance is not nominal, so EUMETSAT advises the user to make some radiance adjustments (as stated here)
These coefficients have been added and several sets exist:
# The last one (S3.PN-SLSTR-L1.08, since 18/05/2021) which is also the default one
SlstrRadAdjust.S3_PN_SLSTR_L1_08
<SlstrRadAdjust.S3_PN_SLSTR_L1_08: SlstrRadAdjustTuple(S1_n=0.97, S2_n=0.98, S3_n=0.98, S4_n=1.0, S5_n=1.11, S6_n=1.13, S1_o=0.94, S2_o=0.95, S3_o=0.95, S4_o=1.0, S5_o=1.04, S6_o=1.07)>
# The two older sets given by EUMETSAT are the same
assert SlstrRadAdjust.S3_PN_SLSTR_L1_07 == SlstrRadAdjust.S3_PN_SLSTR_L1_06
SlstrRadAdjust.S3_PN_SLSTR_L1_07
<SlstrRadAdjust.S3_PN_SLSTR_L1_06: SlstrRadAdjustTuple(S1_n=1.0, S2_n=1.0, S3_n=1.0, S4_n=1.0, S5_n=1.12, S6_n=1.15, S1_o=1.0, S2_o=1.0, S3_o=1.0, S4_o=1.0, S5_o=1.2, S6_o=1.26)>
# Moreover, SNAP uses a different set with unknown origin (optional, in S3MPC Calibration)
SlstrRadAdjust.SNAP
<SlstrRadAdjust.SNAP: SlstrRadAdjustTuple(S1_n=1.0, S2_n=1.0, S3_n=1.0, S4_n=1.0, S5_n=1.12, S6_n=1.13, S1_o=1.0, S2_o=1.0, S3_o=1.0, S4_o=1.0, S5_o=1.15, S6_o=1.14)>
# A default set also exists, with every coefficient set to 1.0
SlstrRadAdjust.NONE
<SlstrRadAdjust.NONE: SlstrRadAdjustTuple(S1_n=1.0, S2_n=1.0, S3_n=1.0, S4_n=1.0, S5_n=1.0, S6_n=1.0, S1_o=1.0, S2_o=1.0, S3_o=1.0, S4_o=1.0, S5_o=1.0, S6_o=1.0)>
# You can use your own set by creating one.
# All the coefficients are set to 1.0 by default, so just modify the one you want
# The band keywords are {true_name}_{view_letter}
# RED is S2
user_set = SlstrRadAdjustTuple(S1_n=1.15, S2_o=1.12)
# However please bear in mind that if you want to reload the same band with a different adjustment,
# you have to remove the temporary process folder or the previous band will be reloaded.
slstr_prod.clean_tmp()
# To apply these sets when loading a band, just add the keyword when loading it
red_pn_08 = slstr_bn_bands[RED]
slstr_red_bn = slstr_prod.load(
RED,
**{
SLSTR_VIEW: SlstrView.NADIR,
SLSTR_STRIPE: SlstrStripe.B,
SLSTR_RAD_ADJUST: user_set
}
)
red_user = slstr_red_bn[RED]
red_user[:, ::10, ::10].plot(robust=True, cmap="mako")
2025-12-23 12:02:46,435 - [DEBUG] - Loading bands ['RED']
2025-12-23 12:02:46,611 - [DEBUG] - Converting RED to reflectance
2025-12-23 12:02:50,733 - [DEBUG] - Geocoding RED
2025-12-23 12:02:55,346 - [DEBUG] - Read RED
2025-12-23 12:02:55,363 - [DEBUG] - Manage nodata for band RED
2025-12-23 12:02:55,367 - [DEBUG] - Converting RED to reflectance (if needed)
2025-12-23 12:02:55,367 - [DEBUG] - Clip the reflectance array to 0 as minimum value (in some cases, reflectance can have higher value than 1)
<matplotlib.collections.QuadMesh at 0x7f4bd03027d0>
# We may need to collocate the bands if we want to work on two sets loaded apart
# Indeed, in EOReader, the bands are collocated when loaded together
# For example, if we wanted to work on the SWIR or F1 band,
# as we first loaded them with the RED, they are collocated to this band (the first one)
# Yet, their geodetic grid are different from the RED one (in and bn are slightly different than the an)
# So if we load on a second time only the SWIR or the F1 band, their are chances that the geocoding might be a little different
# The it is best to collocate the two bands just to be sure they will always match (and have the same size)
# To do so you could do:
from sertit import rasters
red_user = rasters.collocate(red_pn_08, other=red_user)
# Here, it is useless as we work on the master band
abs(red_pn_08 - red_user)[:, ::10, ::10].plot(robust=True, cmap="mako")
<matplotlib.collections.QuadMesh at 0x7f4bccd2f0d0>
