# This file is part of Py6S.
#
# Copyright 2012 Robin Wilson and contributors listed in the CONTRIBUTORS file.
#
# Py6S is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Py6S is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with Py6S. If not, see <http://www.gnu.org/licenses/>.
import numpy as np
from .. import sixs_exceptions
[docs]def Wavelength(start_wavelength, end_wavelength=None, filter=None):
"""Select one or more wavelengths for the 6S simulation.
There are a number of ways to do this:
1. Pass a single value of a wavelength in micrometres.
The simulation will be performed for just this wavelength::
Wavelength(0.43)
2. Pass a start and end wavelength in micrometres.
The simulation will be performed across this wavelength range with a
constant filter function (spectral response function) of 1.0::
Wavelength(0.43, 0.50)
3. Pass a start and end wavelength, and a filter given at 2.5nm intervals.
The simulation will be performed across this wavelength range using the
given filter function::
Wavelength(0.400, 0.410, [0.7, 0.9, 1.0, 0.3, 0.15])
The filter function must include values for the start and end wavelengths,
plus values every 2.5nm (0.0025um) in between. So, in the example above,
there are five values given: one each for 0.400, 0.4025, 0.405, 0.4075, 0.410.
4. Pass a constant (as defined in this class) for a pre-defined wavelength
range::
Wavelength(PredefinedWavelengths.LANDSAT_TM_B1)
"""
try:
wv_id = start_wavelength[0]
if wv_id > 0:
# It's one of the new predefined wavelengths that I've added
return Wavelength(start_wavelength[1], start_wavelength[2], start_wavelength[3])
else:
wv_type = "%d (Chosen Band)\n" % (-1 * wv_id)
data = None
min_wv = start_wavelength[1]
max_wv = start_wavelength[2]
except Exception:
if end_wavelength is None:
# It's simply a wavelength value
if (
start_wavelength > PredefinedWavelengths.MAX_ALLOWABLE_WAVELENGTH
or start_wavelength < PredefinedWavelengths.MIN_ALLOWABLE_WAVELENGTH
):
raise sixs_exceptions.ParameterError(
"wavelength",
"Wavelength must be between %f and %f"
% (
PredefinedWavelengths.MIN_ALLOWABLE_WAVELENGTH,
PredefinedWavelengths.MAX_ALLOWABLE_WAVELENGTH,
),
)
wv_type = "-1"
data = "%f" % start_wavelength
min_wv = start_wavelength
max_wv = start_wavelength
else:
if (
start_wavelength > PredefinedWavelengths.MAX_ALLOWABLE_WAVELENGTH
or start_wavelength < PredefinedWavelengths.MIN_ALLOWABLE_WAVELENGTH
or end_wavelength > PredefinedWavelengths.MAX_ALLOWABLE_WAVELENGTH
or end_wavelength < PredefinedWavelengths.MIN_ALLOWABLE_WAVELENGTH
):
raise sixs_exceptions.ParameterError(
"wavelength",
"Wavelength must be between %f and %f"
% (
PredefinedWavelengths.MIN_ALLOWABLE_WAVELENGTH,
PredefinedWavelengths.MAX_ALLOWABLE_WAVELENGTH,
),
)
min_wv = start_wavelength
max_wv = end_wavelength
if filter is None:
# They haven't given a filter, so assume filter is constant at 1
wv_type = "0 constant filter function"
data = "%f %f" % (start_wavelength, end_wavelength)
else:
# Filter has been given, so we must use it.
# We check filter has been given at 2.5nm intervals
n_req_filter_values = round((end_wavelength - start_wavelength) / 0.0025) + 1
if len(filter) != n_req_filter_values:
raise sixs_exceptions.ParameterError(
"wavelength",
"You must provide a filter value at 2.5nm (0.0025um) intervals, including both the start and end wavelengths.",
)
wv_type = "1 User's defined filtered function"
data = """%f %f
%s""" % (
start_wavelength,
end_wavelength,
" ".join(map(str, filter)),
)
if data is None:
return_string = wv_type
else:
return_string = """%s
%s\n""" % (
wv_type,
data,
)
return (return_string, min_wv, max_wv)
class PredefinedWavelengths:
MAX_ALLOWABLE_WAVELENGTH = 4
MIN_ALLOWABLE_WAVELENGTH = 0.2
# New predefined wavelengths that I've added to Py6S
# CONSTANT_NAME = (ID, Start Wavelength, End Wavelength, Filter Function)
# Note: IDs must be > 1 for new predefined wavelengths
# Landsat OLI
# Taken from spreadsheet downloadable from http://landsat.gsfc.nasa.gov/?p=5779
# Interpolated to 2.5nm intervals, as required by 6S
LANDSAT_OLI_B1 = (
1,
0.427,
0.457,
np.array(
[
7.30000000e-05,
2.52450000e-03,
2.47670000e-02,
3.85985000e-01,
9.08749000e-01,
9.80591500e-01,
9.86713000e-01,
9.96568500e-01,
9.82780000e-01,
8.25707000e-01,
2.26412000e-01,
2.55700000e-02,
2.41400000e-03,
]
),
)
LANDSAT_OLI_B2 = (
2,
0.436,
0.526,
np.array(
[
1.00000000e-05,
1.79000000e-04,
4.55000000e-04,
1.63350000e-03,
6.86900000e-03,
4.28880000e-02,
2.71370000e-01,
7.90740500e-01,
9.03034000e-01,
9.04677500e-01,
8.89667000e-01,
8.79232000e-01,
8.79688000e-01,
8.89796500e-01,
8.48533000e-01,
8.36270500e-01,
8.68497000e-01,
9.11461500e-01,
9.31726000e-01,
9.54896500e-01,
9.56424000e-01,
9.83834000e-01,
9.89469000e-01,
9.68066500e-01,
9.88729000e-01,
9.61057500e-01,
9.66125000e-01,
9.82077000e-01,
9.63135000e-01,
9.98249000e-01,
8.44893000e-01,
1.19533500e-01,
5.32800000e-03,
1.32850000e-03,
5.16000000e-04,
1.17000000e-04,
2.30000000e-05,
]
),
)
LANDSAT_OLI_B3 = (
3,
0.512,
0.610,
np.array(
[
-4.60000000e-05,
1.78500000e-04,
6.48000000e-04,
1.57400000e-03,
3.44600000e-03,
8.73250000e-03,
2.55130000e-02,
9.69975000e-02,
3.53885000e-01,
8.03215000e-01,
9.54627000e-01,
9.60271500e-01,
9.69873000e-01,
9.69833500e-01,
9.77001000e-01,
9.95392000e-01,
9.82642000e-01,
9.71423000e-01,
9.46245000e-01,
9.62786000e-01,
9.66447000e-01,
9.64176500e-01,
9.83397000e-01,
9.70875500e-01,
9.78208000e-01,
9.77182500e-01,
9.69181000e-01,
9.81277000e-01,
9.68886000e-01,
9.80432000e-01,
9.04478000e-01,
6.05139000e-01,
1.90467000e-01,
2.47350000e-02,
2.57400000e-03,
2.39500000e-04,
0,
0,
0,
0,
]
),
)
LANDSAT_OLI_B4 = (
4,
0.625,
0.690,
np.array(
[
-3.42000000e-04,
1.37250000e-03,
7.19700000e-03,
4.86465000e-02,
2.99778000e-01,
8.34958000e-01,
9.50823000e-01,
9.57268000e-01,
9.84173000e-01,
9.83172500e-01,
9.59441000e-01,
9.54442500e-01,
9.81688000e-01,
9.88501500e-01,
9.76960000e-01,
9.88942000e-01,
9.80678000e-01,
9.66466000e-01,
9.66928000e-01,
7.29107000e-01,
1.23946000e-01,
1.25175000e-02,
1.40200000e-03,
0,
0,
0,
0,
]
),
)
LANDSAT_OLI_B5 = (
5,
0.829,
0.899,
np.array(
[
0,
7.50000000e-05,
3.14000000e-04,
8.52500000e-04,
2.10700000e-03,
5.90150000e-03,
1.73460000e-02,
6.62770000e-02,
2.49733000e-01,
6.63830000e-01,
9.60215000e-01,
9.76869500e-01,
1.00000000e00,
9.78334000e-01,
9.57357000e-01,
9.50103000e-01,
9.48450000e-01,
9.53355500e-01,
9.69821000e-01,
8.39899500e-01,
4.48364000e-01,
1.37481000e-01,
3.45320000e-02,
1.00205000e-02,
2.94400000e-03,
9.67500000e-04,
2.41000000e-04,
1.55000000e-05,
0,
]
),
)
LANDSAT_OLI_B6 = (
6,
1.515,
1.695,
np.array(
[
0,
2.00000000e-04,
4.66000000e-04,
8.45000000e-04,
1.36900000e-03,
2.01550000e-03,
2.88100000e-03,
4.02150000e-03,
5.52800000e-03,
7.88900000e-03,
1.09890000e-02,
1.52755000e-02,
2.18310000e-02,
3.25615000e-02,
4.78640000e-02,
7.09490000e-02,
1.01893000e-01,
1.50884500e-01,
2.20261000e-01,
3.10649000e-01,
4.21470000e-01,
5.52234000e-01,
6.76683000e-01,
7.71509000e-01,
8.54065000e-01,
8.95823500e-01,
9.13009000e-01,
9.25058000e-01,
9.26413000e-01,
9.23818000e-01,
9.22828000e-01,
9.22408500e-01,
9.26605000e-01,
9.43438000e-01,
9.46175000e-01,
9.47297500e-01,
9.52859000e-01,
9.51358500e-01,
9.59047000e-01,
9.59191500e-01,
9.61470000e-01,
9.60494000e-01,
9.64703000e-01,
9.69951500e-01,
9.76906000e-01,
9.81271500e-01,
9.88609000e-01,
9.99010500e-01,
9.99642000e-01,
9.89828000e-01,
9.67125000e-01,
9.26719000e-01,
8.40967000e-01,
7.23103000e-01,
5.73232000e-01,
4.22998000e-01,
2.91752000e-01,
1.95988000e-01,
1.28463000e-01,
8.28380000e-02,
5.27520000e-02,
3.45655000e-02,
2.25040000e-02,
1.47195000e-02,
9.58700000e-03,
6.39600000e-03,
4.25700000e-03,
2.79800000e-03,
1.78100000e-03,
1.14200000e-03,
6.77000000e-04,
3.55000000e-04,
1.12000000e-04,
]
),
)
LANDSAT_OLI_B7 = (
7,
2.037,
2.354,
np.array(
[
0,
1.07000000e-04,
2.40000000e-04,
3.99000000e-04,
5.99000000e-04,
8.80500000e-04,
1.22200000e-03,
1.64550000e-03,
2.18700000e-03,
2.85300000e-03,
3.73300000e-03,
4.88200000e-03,
6.33700000e-03,
8.44950000e-03,
1.10050000e-02,
1.42985000e-02,
1.88990000e-02,
2.45090000e-02,
3.20710000e-02,
4.28190000e-02,
5.64290000e-02,
7.48955000e-02,
1.00640000e-01,
1.36480000e-01,
1.79714000e-01,
2.40483000e-01,
3.11347000e-01,
3.94832500e-01,
4.88816000e-01,
5.73971000e-01,
6.63067000e-01,
7.39406500e-01,
7.92667000e-01,
8.41172500e-01,
8.67845000e-01,
8.86269000e-01,
9.06527000e-01,
9.14538000e-01,
9.29693000e-01,
9.38975000e-01,
9.42952000e-01,
9.44181000e-01,
9.48776000e-01,
9.49521500e-01,
9.56635000e-01,
9.48258500e-01,
9.50874000e-01,
9.47049500e-01,
9.57717000e-01,
9.47095000e-01,
9.51641000e-01,
9.46800000e-01,
9.40311000e-01,
9.46466500e-01,
9.38737000e-01,
9.44439000e-01,
9.44482000e-01,
9.50472000e-01,
9.39939000e-01,
9.37156500e-01,
9.38955000e-01,
9.28123500e-01,
9.30508000e-01,
9.30946000e-01,
9.36472000e-01,
9.34327500e-01,
9.46217000e-01,
9.53826000e-01,
9.63135000e-01,
9.63944000e-01,
9.62905000e-01,
9.61607000e-01,
9.57814000e-01,
9.55657500e-01,
9.51706000e-01,
9.60275500e-01,
9.47696000e-01,
9.59807000e-01,
9.55750000e-01,
9.56607500e-01,
9.66786000e-01,
9.62823000e-01,
9.77637000e-01,
9.83457500e-01,
9.85056000e-01,
9.98627000e-01,
9.92469000e-01,
9.97947000e-01,
9.97261000e-01,
9.89437000e-01,
9.86037000e-01,
9.81280000e-01,
9.72794000e-01,
9.76369500e-01,
9.74409000e-01,
9.63698500e-01,
9.55095000e-01,
9.51391500e-01,
9.22405000e-01,
8.89264000e-01,
8.23876000e-01,
7.21272500e-01,
6.02539000e-01,
4.77695500e-01,
3.55569000e-01,
2.61452500e-01,
1.86151000e-01,
1.31725000e-01,
9.20290000e-02,
6.49895000e-02,
4.63320000e-02,
3.34235000e-02,
2.40000000e-02,
1.76250000e-02,
1.29300000e-02,
9.55700000e-03,
7.08800000e-03,
5.33100000e-03,
3.90300000e-03,
2.83800000e-03,
2.04700000e-03,
1.44950000e-03,
9.74000000e-04,
6.20000000e-04,
3.20000000e-04,
7.35000000e-05,
0,
0,
]
),
)
LANDSAT_OLI_B8 = (
8,
0.488,
0.691,
np.array(
[
2.16000000e-04,
1.30000000e-03,
3.84100000e-03,
1.22590000e-02,
4.27230000e-02,
1.60137500e-01,
4.72496000e-01,
7.45412500e-01,
8.31881000e-01,
8.55321500e-01,
8.59640000e-01,
8.57696000e-01,
8.58455000e-01,
8.58301000e-01,
8.50183000e-01,
8.58223500e-01,
8.61508000e-01,
8.57683500e-01,
8.79249000e-01,
8.91710500e-01,
9.06294000e-01,
9.12867000e-01,
9.02939000e-01,
9.20739500e-01,
9.13020000e-01,
8.85650500e-01,
8.79443000e-01,
8.74179000e-01,
8.75361000e-01,
8.91665000e-01,
8.74097000e-01,
8.86888500e-01,
9.03528000e-01,
9.10950500e-01,
9.13190000e-01,
9.20178000e-01,
9.24431000e-01,
9.29809500e-01,
9.48863000e-01,
9.40543000e-01,
9.45674000e-01,
9.39380000e-01,
9.46659000e-01,
9.34044500e-01,
9.40838000e-01,
9.58039500e-01,
9.68241000e-01,
9.66480500e-01,
9.57232000e-01,
9.47675500e-01,
9.52465000e-01,
9.57481500e-01,
9.64158000e-01,
9.67366000e-01,
9.77026000e-01,
9.76029500e-01,
9.69583000e-01,
9.72807000e-01,
9.65780000e-01,
9.66738000e-01,
9.72067000e-01,
9.79346500e-01,
9.71123000e-01,
9.53377000e-01,
9.63851000e-01,
9.67101500e-01,
9.70613000e-01,
9.79974500e-01,
9.88302000e-01,
9.91753000e-01,
1.00000000e00,
9.98476000e-01,
9.92555000e-01,
9.85811500e-01,
9.13945000e-01,
5.24376500e-01,
1.67313000e-01,
4.61755000e-02,
1.51780000e-02,
6.70950000e-03,
3.22000000e-03,
1.21200000e-03,
]
),
)
LANDSAT_OLI_PAN = LANDSAT_OLI_B8
LANDSAT_OLI_B9 = (
9,
1.340,
1.407,
np.array(
[
0,
2.57500000e-04,
6.47000000e-04,
1.27400000e-03,
2.31800000e-03,
4.70450000e-03,
1.11240000e-02,
3.45385000e-02,
1.15351000e-01,
3.86681500e-01,
7.72118000e-01,
9.00941500e-01,
9.31247000e-01,
9.91687000e-01,
1.00000000e00,
9.77080500e-01,
8.71343000e-01,
6.54888000e-01,
2.97920000e-01,
8.96145000e-02,
2.60840000e-02,
8.30650000e-03,
2.78000000e-03,
1.11900000e-03,
2.20000000e-04,
0,
0,
0,
]
),
)
# RapidEye
# Taken from http://blackbridge.com/rapideye/upload/Spectral_Response_Curves.pdf
# Interpolated to 2.5nm intervals, as required by 6S
# RAPIDEYE_B1 = (10, 0.420, 0.514,
# np.array([ 0.002 , 0. , 0. , 0.0015, 0. , 0. , 0.001 ,
# 0.0095, 0.321 , 0.725 , 0.74 , 0.759 , 0.77 , 0.781 ,
# 0.784 , 0.7935, 0.796 , 0.8005, 0.806 , 0.804 , 0.807 ,
# 0.817 , 0.82 , 0.8275, 0.84 , 0.847 , 0.862 , 0.8765,
# 0.886 , 0.9105, 0.928 , 0.9415, 0.969 , 0.9685, 1. ,
# 0.9875, 0.437 , 0.019, 0.0]))
# RAPIDEYE_B2 = (11, 0.462, 0.595,
# np.array([ 0.002 , 0.0025, 0.002 , 0.021 , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0.0145, 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0.001 , 0.002 ,
# 0.01 , 0.054 , 0.4055, 0.868 , 0.866 , 0.877 , 0.872 ,
# 0.874 , 0.8815, 0.882 , 0.8805, 0.886 , 0.8955, 0.899 ,
# 0.8995, 0.91 , 0.922 , 0.928 , 0.9345, 0.946 , 0.9525,
# 0.96 , 0.972 , 0.976 , 0.975 , 0.989 , 0.9905, 0.984 ,
# 0.997 , 0.97 , 0.6535, 0.039 , 0.004 ]))
# RAPIDEYE_B3 = (12, 0.486, 0.691,
# np.array([ 2.00000000e-03, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 1.00000000e-03, 5.00000000e-04, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 5.00000000e-04,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 5.00000000e-04,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 1.00000000e-03, 4.00000000e-03, 1.80000000e-02,
# 1.43000000e-01, 7.23000000e-01, 8.57500000e-01,
# 8.65000000e-01, 8.81500000e-01, 8.82000000e-01,
# 8.93000000e-01, 9.07000000e-01, 9.12500000e-01,
# 9.18000000e-01, 9.31000000e-01, 9.44000000e-01,
# 9.52500000e-01, 9.61000000e-01, 9.67000000e-01,
# 9.73000000e-01, 9.83000000e-01, 9.91000000e-01,
# 9.87000000e-01, 9.89000000e-01, 9.99000000e-01,
# 9.81000000e-01, 9.28500000e-01, 1.74000000e-01,
# 1.70000000e-02, 0.0]))
# RAPIDEYE_B4 = (13, 0.500, 0.740,
# np.array([ 2.70000000e-02, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 5.00000000e-04,
# 0.00000000e+00, 5.00000000e-04, 1.00000000e-03,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 5.00000000e-04, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 2.20000000e-02,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 5.00000000e-04,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 1.00000000e-03, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 1.00000000e-03,
# 2.00000000e-03, 5.50000000e-03, 2.10000000e-02,
# 1.08500000e-01, 4.91000000e-01, 9.49000000e-01,
# 9.98000000e-01, 9.99500000e-01, 9.98000000e-01,
# 9.91500000e-01, 9.87000000e-01, 9.85500000e-01,
# 9.82000000e-01, 9.74000000e-01, 9.66000000e-01,
# 9.64000000e-01, 9.61000000e-01, 9.45500000e-01,
# 9.39000000e-01, 8.98000000e-01, 4.25000000e-01,
# 9.40000000e-02, 2.00000000e-02, 5.50000000e-03, 0.0]))
# RAPIDEYE_B5 = (14, 0.519, 0.866,
# np.array([ 2.00000000e-03, 5.00000000e-04, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 1.00000000e-03, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 2.00000000e-03,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 1.00000000e-03, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 4.00000000e-03, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 1.00000000e-03, 2.00000000e-03, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 1.00000000e-03, 2.00000000e-03,
# 5.00000000e-04, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 5.00000000e-04, 2.20000000e-02,
# 5.00000000e-04, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 0.00000000e+00, 1.00000000e-03, 1.00000000e-03,
# 2.00000000e-03, 4.50000000e-03, 9.00000000e-03,
# 1.95000000e-02, 4.60000000e-02, 1.27500000e-01,
# 3.45000000e-01, 7.49000000e-01, 9.88000000e-01,
# 9.88000000e-01, 9.69000000e-01, 9.71500000e-01,
# 9.81000000e-01, 9.82000000e-01, 9.74000000e-01,
# 9.65000000e-01, 9.60000000e-01, 9.57500000e-01,
# 9.59000000e-01, 9.59000000e-01, 9.57000000e-01,
# 9.54500000e-01, 9.50000000e-01, 9.45000000e-01,
# 9.39000000e-01, 9.34000000e-01, 9.29000000e-01,
# 9.27000000e-01, 9.26000000e-01, 9.26000000e-01,
# 9.25000000e-01, 9.19500000e-01, 9.11000000e-01,
# 9.00000000e-01, 8.91000000e-01, 8.85500000e-01,
# 8.83000000e-01, 8.78000000e-01, 8.65000000e-01,
# 8.45500000e-01, 8.38000000e-01, 8.37000000e-01,
# 7.12000000e-01, 3.63000000e-01, 1.24000000e-01,
# 4.30000000e-02, 1.60000000e-02, 7.00000000e-03,
# 3.00000000e-03], ))
# # Pleiades 1B
# # Taken from ENVI Spectral Library file
# # Interpolated to 2.5nm intervals, as required by 6S
# #
# # NOTE: There are out-of-band responses for a number of these
# # bands (ie. responses in the NIR for the blue band) and these
# # *are* included, giving strange min/max wavelengths
# #
# PLEIADES_B1 = (15, 0.438, 0.915, # Blue
# np.array([ 0.001603, 0.001338, 0.004344, 0.011072, 0.017627, 0.023367,
# 0.057403, 0.134555, 0.223763, 0.308543, 0.461557, 0.650821,
# 0.755369, 0.74772 , 0.716819, 0.718538, 0.756696, 0.810109,
# 0.842166, 0.823437, 0.775247, 0.752701, 0.78022 , 0.819927,
# 0.851663, 0.860275, 0.858684, 0.865826, 0.882822, 0.904041,
# 0.919695, 0.932596, 0.950233, 0.975798, 0.994977, 1. ,
# 0.995063, 0.980628, 0.94175 , 0.843622, 0.671142, 0.46334 ,
# 0.288865, 0.167078, 0.09018 , 0.050519, 0.031488, 0.023814,
# 0.021311, 0.02063 , 0.01956 , 0.016794, 0.011358, 0.006652,
# 0.004144, 0.00303 , 0.002416, 0.00199 , 0.001568, 0.001136,
# 0.001253, 0. , 0. , 0. , 0. , 0. ,
# 0. , 0.001109, 0.001987, 0.00122 , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0.001119,
# 0. , 0. , 0. , 0. , 0. , 0.001629,
# 0.0024 , 0.001032, 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0.001028, 0.001563, 0.002609,
# 0.004575, 0.009062, 0.017163, 0.023793, 0.021523, 0.015914,
# 0.011956, 0.009482, 0.007869, 0.007224, 0.007242, 0.007568,
# 0.008379, 0.009864, 0.012259, 0.016267, 0.022602, 0.032027,
# 0.04443 , 0.054669, 0.056424, 0.048004, 0.035799, 0.025834,
# 0.018887, 0.014439, 0.011679, 0.009911, 0.008647, 0.00781 ,
# 0.007227, 0.006996, 0.006923, 0.006914, 0.007021, 0.007253,
# 0.007373, 0.007505, 0.00747 , 0.007067]))
# PLEIADES_B2 = (16, 0.490, 0.950, # Green
# np.array([ 0.001521, 0.00413 , 0.009954, 0.019642, 0.032548, 0.056793,
# 0.123791, 0.285909, 0.528976, 0.771625, 0.883804, 0.907957,
# 0.913146, 0.913728, 0.922484, 0.936708, 0.94976 , 0.954499,
# 0.958582, 0.964206, 0.970527, 0.972265, 0.967518, 0.95891 ,
# 0.952449, 0.952466, 0.956048, 0.955179, 0.94899 , 0.947145,
# 0.95445 , 0.97106 , 0.989818, 1. , 0.99536 , 0.969822,
# 0.925304, 0.863324, 0.794828, 0.723897, 0.645327, 0.543852,
# 0.417028, 0.276671, 0.157527, 0.085607, 0.049226, 0.032724,
# 0.023793, 0.018197, 0.014062, 0.009966, 0.005845, 0.003038,
# 0.001536, 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0.003185, 0.014191, 0.008339, 0.002244,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0.001111, 0.001938, 0.003691,
# 0.006357, 0.010271, 0.013108, 0.013261, 0.011115, 0.008366,
# 0.006564, 0.005685, 0.00538 , 0.005623, 0.0062 , 0.007239,
# 0.00892 , 0.01151 , 0.014942, 0.019269, 0.023479, 0.02644 ,
# 0.027049, 0.025545, 0.023397, 0.021395, 0.019944, 0.019253,
# 0.019074, 0.019689, 0.020694, 0.022011, 0.02323 , 0.023757,
# 0.022986, 0.02066 , 0.017225, 0.013292, 0.0]))
# PLEIADES_B3 = (17, 0.448, 0.940, # RED
# np.array([ 0.001195, 0.005142, 0.008003, 0.006693, 0.010859, 0.024691,
# 0.065359, 0.122542, 0.057009, 0.021375, 0.012797, 0.006278,
# 0.002354, 0. , 0. , 0. , 0. , 0. ,
# 0. , 0.001013, 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0.001244, 0.002142,
# 0.003819, 0.006805, 0.012333, 0.022178, 0.041333, 0.078071,
# 0.151934, 0.277675, 0.451038, 0.629132, 0.762549, 0.832945,
# 0.857906, 0.865887, 0.869263, 0.875221, 0.885776, 0.900593,
# 0.917488, 0.93488 , 0.947811, 0.956953, 0.96233 , 0.964767,
# 0.962429, 0.961307, 0.962025, 0.969915, 0.981157, 0.993393,
# 1. , 0.980951, 0.952263, 0.913173, 0.869401, 0.825208,
# 0.783047, 0.736127, 0.673489, 0.587753, 0.480491, 0.363007,
# 0.252303, 0.162603, 0.102221, 0.064127, 0.041916, 0.028464,
# 0.020455, 0.01537 , 0.012117, 0.009881, 0.008317, 0.007102,
# 0.006095, 0.005172, 0.004314, 0.003495, 0.002771, 0.003589,
# 0.003031, 0.002317, 0.001784, 0.001331, 0.001021, 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0.001308, 0.001761, 0.002403,
# 0.003268, 0.004364, 0.005662, 0.00689 , 0.007822, 0.00833 ,
# 0.008436, 0.008185, 0.008138, 0.008001, 0.007768, 0.007784,
# 0.007998, 0.008152, 0.008506, 0.008793, 0.009312, 0.009753,
# 0.010136, 0.010387, 0.010406, 0.010171, 0.009522, 0.008609,
# 0.007337]))
# PLEIADES_B4 = (18, 0.750, 0.945, # NIR
# np.array([ 0.001687, 0.002665, 0.001225, 0. , 0. , 0. ,
# 0. , 0. , 0.001549, 0.00218 , 0.001988, 0.001045,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0.001224, 0.002266, 0.002879,
# 0.002041, 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0. ,
# 0. , 0. , 0. , 0. , 0. , 0.001154,
# 0.001352, 0.001554, 0.001756, 0.001989, 0.002251, 0.002534,
# 0.002903, 0.005236, 0.006401, 0.008013, 0.010147, 0.013109,
# 0.017135, 0.022905, 0.030978, 0.042662, 0.05919 , 0.083507,
# 0.117888, 0.166378, 0.231114, 0.315936, 0.417216, 0.528495,
# 0.640959, 0.747239, 0.838552, 0.908812, 0.959366, 0.988114,
# 1. , 0.999206, 0.989642, 0.967696, 0.951436, 0.937494,
# 0.925472, 0.915223, 0.908783, 0.902608, 0.896683, 0.892483,
# 0.885491, 0.877655, 0.867639, 0.856896, 0.847441, 0.836048,
# 0.823698, 0.813044, 0.801627, 0.792162, 0.782795, 0.776935,
# 0.771465, 0.763145, 0.75434 , 0.745237, 0.734921, 0.725512,
# 0.714614, 0.703089, 0.691948, 0.681648, 0.67052 , 0.659536,
# 0.647596, 0.629185, 0.611093, 0.593432, 0.576059, 0.559994,
# 0.544875, 0.530614, 0.515483, 0.497676, 0.47341 , 0.43877 ,
# 0.393422, 0.336769, 0.274273, 0.211434, 0.153554, 0.106341,
# 0.070442, 0.045912, 0.029535, 0.019275, 0.01268 , 0.008462]))
# PLEIADES_PAN = (19, 0.460, 0.845, # Panchromatic
# np.array([ 0.002795, 0.005578, 0.011986, 0.022135, 0.038918, 0.066419,
# 0.106702, 0.161427, 0.227202, 0.298655, 0.367815, 0.425804,
# 0.46974 , 0.499166, 0.519316, 0.53325 , 0.546726, 0.560304,
# 0.573644, 0.585534, 0.595662, 0.604104, 0.612015, 0.618774,
# 0.625565, 0.631905, 0.637794, 0.642186, 0.644395, 0.644882,
# 0.644885, 0.645114, 0.646836, 0.651318, 0.659091, 0.669141,
# 0.681181, 0.693702, 0.705609, 0.715769, 0.725057, 0.732351,
# 0.738419, 0.743876, 0.749253, 0.753645, 0.758079, 0.761089,
# 0.763913, 0.765235, 0.766172, 0.766597, 0.767495, 0.768755,
# 0.772303, 0.777108, 0.783383, 0.791719, 0.801545, 0.810952,
# 0.820033, 0.829068, 0.836465, 0.842652, 0.848188, 0.851917,
# 0.855505, 0.858336, 0.862017, 0.866684, 0.87158 , 0.878019,
# 0.884707, 0.891347, 0.898891, 0.904582, 0.910509, 0.915462,
# 0.920794, 0.925316, 0.928988, 0.932893, 0.936956, 0.94008 ,
# 0.94416 , 0.94693 , 0.950347, 0.952109, 0.95428 , 0.957441,
# 0.960771, 0.966315, 0.9721 , 0.978828, 0.984596, 0.991222,
# 0.995687, 0.999088, 1. , 0.996327, 0.991468, 0.986969,
# 0.981702, 0.977146, 0.974597, 0.972901, 0.973932, 0.974192,
# 0.976871, 0.979825, 0.982772, 0.984573, 0.985077, 0.983727,
# 0.982597, 0.979023, 0.974595, 0.969741, 0.964814, 0.959839,
# 0.953335, 0.948357, 0.944548, 0.939689, 0.934456, 0.929716,
# 0.924834, 0.918336, 0.911194, 0.9061 , 0.900618, 0.895346,
# 0.890809, 0.889495, 0.888541, 0.880978, 0.870784, 0.856668,
# 0.836935, 0.810241, 0.772852, 0.718955, 0.65285 , 0.571081,
# 0.479585, 0.384649, 0.294773, 0.216645, 0.153132, 0.105705,
# 0.07168 , 0.049143, 0.033776, 0.025699, 0.018176, 0.013197,
# 0.012156, 0.00902 , 0.008416, 0.00428 , 0.003313, 0.002669,
# 0.002212, 0.001829, 0.001529, 0.001363, 0.001215]))
# Sentinel-2A MSI spectral response functions
# Taken from https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/document-library/-/asset_publisher/Wk0TKajiISaR/content/sentinel-2a-spectral-responses
S2A_MSI_01 = (
53,
0.4120,
0.4570,
np.array(
[
0.00177574,
0.00357067,
0.00378029,
0.00162485,
0.00125044,
0.00109536,
0.00182373,
0.01670008,
0.32387506,
0.73834764,
0.80862387,
0.83917843,
0.88731097,
0.99114075,
0.93596338,
0.8379525,
0.61417422,
0.08388263,
0.0,
]
),
)
S2A_MSI_02 = (
54,
0.4390,
0.5340,
np.array(
[
0.01031543,
0.02550952,
0.02269349,
0.01904007,
0.02130721,
0.01793204,
0.01924643,
0.0574268,
0.32799225,
0.73148173,
0.75668081,
0.77382288,
0.86179176,
0.9192643,
0.90035366,
0.87919289,
0.90695544,
0.94165397,
0.8860231,
0.79745698,
0.77209054,
0.82911115,
0.92793997,
0.96755782,
0.95832003,
0.95438995,
0.9770514,
0.96669894,
0.90190134,
0.83341648,
0.82057639,
0.89259188,
0.99163699,
0.9474343,
0.50395858,
0.14371473,
0.04207746,
0.00849869,
0.0,
]
),
)
S2A_MSI_03 = (
55,
0.5380,
0.5830,
np.array(
[
0.01448181,
0.11395589,
0.55322279,
0.87331261,
0.92572845,
0.8767134,
0.8387572,
0.87529183,
0.96017974,
0.9993004,
0.94522089,
0.83711029,
0.76460653,
0.755965,
0.79890086,
0.79383741,
0.40397555,
0.07754079,
0.00108588,
]
),
)
S2A_MSI_04 = (
56,
0.6460,
0.6860,
np.array(
[
0.00141521,
0.25369897,
0.94485805,
0.99703154,
0.99052772,
0.90975472,
0.77345952,
0.7712748,
0.83612975,
0.89873936,
0.95042063,
0.95976475,
0.95527459,
0.81958553,
0.22960399,
0.01516159,
0.0,
]
),
)
S2A_MSI_05 = (
57,
0.6950,
0.7150,
np.array(
[
2.83578563e-02,
5.77459223e-01,
9.98895232e-01,
9.88670233e-01,
9.48473886e-01,
8.94935856e-01,
7.90753191e-01,
1.60051275e-01,
2.74780199e-15,
]
),
)
S2A_MSI_06 = (
58,
0.7310,
0.7510,
np.array(
[
0.00171088,
0.45264387,
0.90232877,
0.9520792,
0.9770217,
0.99440139,
0.90126739,
0.11597958,
0.0,
]
),
)
S2A_MSI_07 = (
59,
0.7690,
0.7990,
np.array(
[
4.58986862e-04,
1.08907239e-01,
6.36854532e-01,
9.80612493e-01,
1.00000000e00,
8.95890946e-01,
7.94076736e-01,
8.11671368e-01,
8.20118286e-01,
6.54111768e-01,
2.30888169e-01,
1.62639307e-02,
0.00000000e00,
]
),
)
S2A_MSI_08 = (
60,
0.7600,
0.9075,
np.array(
[
6.72586786e-04,
0.00000000e00,
0.00000000e00,
0.00000000e00,
0.00000000e00,
1.44779746e-04,
1.75239146e-02,
6.81427309e-02,
1.95910652e-01,
5.03477951e-01,
8.03038519e-01,
9.70893721e-01,
9.88406528e-01,
9.39513050e-01,
9.53957178e-01,
9.82554949e-01,
9.83911811e-01,
9.77666900e-01,
9.87645575e-01,
9.83475703e-01,
9.57210894e-01,
9.06465733e-01,
8.51603307e-01,
7.91559886e-01,
7.49627707e-01,
7.26753218e-01,
7.25638556e-01,
7.19227875e-01,
7.08269542e-01,
7.00091470e-01,
7.18240007e-01,
7.58295756e-01,
7.96838100e-01,
8.03464289e-01,
7.89203043e-01,
7.58190619e-01,
7.22623995e-01,
6.74426662e-01,
6.30051247e-01,
5.96192744e-01,
5.71365058e-01,
5.49016706e-01,
5.35102151e-01,
5.30005852e-01,
5.32114628e-01,
5.32889818e-01,
5.30510552e-01,
5.31251103e-01,
5.42353442e-01,
5.57670949e-01,
5.48219844e-01,
4.92873763e-01,
4.27984659e-01,
4.00632204e-01,
4.20865558e-01,
4.07967544e-01,
2.57205092e-01,
9.34112378e-02,
2.59315420e-02,
1.97162898e-03,
]
),
)
S2A_MSI_8A = (
61,
0.8370,
0.8820,
np.array(
[
3.00966201e-04,
0.00000000e00,
0.00000000e00,
0.00000000e00,
1.57217434e-03,
1.97345125e-02,
1.07797093e-01,
5.03542566e-01,
9.32244502e-01,
9.75417653e-01,
9.76078281e-01,
9.88891962e-01,
9.99846733e-01,
9.95645780e-01,
9.96407621e-01,
6.15518275e-01,
1.12212459e-01,
1.67962607e-02,
1.08051589e-15,
]
),
)
S2A_MSI_09 = (
62,
0.9320,
0.9595,
np.array(
[
0.01662953,
0.28176774,
0.87454114,
0.97842462,
1.0,
0.98646234,
0.99428313,
0.9642169,
0.92240308,
0.58699885,
0.06534121,
0.0,
]
),
)
S2A_MSI_10 = (
63,
1.3370,
1.4120,
np.array(
[
2.38032000e-04,
2.95805000e-05,
1.09542000e-04,
1.50071000e-04,
3.71868000e-04,
8.73727500e-04,
3.73628100e-03,
3.17506340e-02,
2.00854803e-01,
5.84484875e-01,
8.61210463e-01,
9.52070852e-01,
9.81720971e-01,
9.94644797e-01,
1.00000000e00,
9.93502876e-01,
9.78085355e-01,
9.54744576e-01,
9.15013686e-01,
8.49689704e-01,
6.55300550e-01,
2.85381533e-01,
6.83716420e-02,
9.59380350e-03,
1.12529200e-03,
2.63485500e-04,
1.96998000e-04,
5.54815000e-05,
5.61960000e-05,
7.65570000e-05,
2.54510000e-05,
]
),
)
S2A_MSI_11 = (
64,
1.5390,
1.6840,
np.array(
[
6.93400000e-06,
1.81645000e-05,
9.07460000e-05,
3.65801000e-04,
6.62261000e-04,
1.42506250e-03,
3.56747000e-03,
1.04731085e-02,
2.77806180e-02,
6.48974280e-02,
1.35475748e-01,
2.89005843e-01,
5.37634456e-01,
7.79371857e-01,
8.79668577e-01,
8.90257398e-01,
9.06630520e-01,
9.30950552e-01,
9.47506732e-01,
9.55341795e-01,
9.60687866e-01,
9.66988645e-01,
9.73379886e-01,
9.79229624e-01,
9.80912334e-01,
9.80974468e-01,
9.81066319e-01,
9.86464474e-01,
9.92015684e-01,
9.91470684e-01,
9.83502057e-01,
9.74371426e-01,
9.67709002e-01,
9.65949699e-01,
9.69869577e-01,
9.81077488e-01,
9.91875079e-01,
9.98077305e-01,
9.99234469e-01,
9.99420043e-01,
1.00000000e00,
9.94156469e-01,
9.78569228e-01,
9.57091545e-01,
9.41712152e-01,
9.31801621e-01,
8.80597830e-01,
6.92504394e-01,
4.26963072e-01,
1.99127837e-01,
7.24778960e-02,
2.43795985e-02,
9.31685100e-03,
4.13662100e-03,
1.95831500e-03,
7.76503500e-04,
1.59180000e-04,
2.89595000e-05,
0.00000000e00,
]
),
)
S2A_MSI_12 = (
65,
2.0780,
2.3205,
np.array(
[
6.39264000e-04,
3.44108750e-03,
7.65982600e-03,
8.92000050e-03,
1.09721780e-02,
1.41907265e-02,
1.97803200e-02,
3.04417690e-02,
4.88826620e-02,
8.18549335e-02,
1.36902990e-01,
2.23166080e-01,
3.27177957e-01,
4.26390454e-01,
5.03106894e-01,
5.50302429e-01,
5.79029932e-01,
6.04436146e-01,
6.34282516e-01,
6.71876803e-01,
7.11964139e-01,
7.49353252e-01,
7.85580740e-01,
8.14689405e-01,
8.36721896e-01,
8.52179769e-01,
8.56174190e-01,
8.60495823e-01,
8.65138808e-01,
8.71999165e-01,
8.82438825e-01,
8.91349430e-01,
8.99896619e-01,
9.05177679e-01,
9.05502587e-01,
9.04432342e-01,
9.02760831e-01,
9.02703264e-01,
9.05315323e-01,
9.09107627e-01,
9.15584774e-01,
9.22819789e-01,
9.29740263e-01,
9.35024139e-01,
9.37641167e-01,
9.39046421e-01,
9.43259037e-01,
9.41374377e-01,
9.36660033e-01,
9.29951119e-01,
9.15482617e-01,
9.07788182e-01,
9.11168883e-01,
9.21729901e-01,
9.19489205e-01,
9.26129478e-01,
9.36769555e-01,
9.49002300e-01,
9.57046915e-01,
9.63698163e-01,
9.66647037e-01,
9.65150671e-01,
9.61698492e-01,
9.56233764e-01,
9.52731790e-01,
9.51938341e-01,
9.54056643e-01,
9.62430392e-01,
9.72691041e-01,
9.85021423e-01,
9.93982826e-01,
9.99376452e-01,
9.99151921e-01,
9.91784832e-01,
9.76212761e-01,
9.53391519e-01,
9.27724853e-01,
8.99933858e-01,
8.68647160e-01,
8.33317814e-01,
7.91866591e-01,
7.34726119e-01,
6.51282733e-01,
5.43500424e-01,
4.29259293e-01,
3.21385902e-01,
2.27703987e-01,
1.56384870e-01,
1.08237331e-01,
7.69315535e-02,
5.49854480e-02,
4.07311725e-02,
3.19988480e-02,
2.64975445e-02,
2.16716920e-02,
1.36598615e-02,
6.51060100e-03,
1.03255350e-03,
]
),
)
# Sentinel-3A OLCI spectral response functions
# changed to center at the max value of each
# band rsr from https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/olci-instrument/spectral-response-function-data
# the mean dataset
S3A_OLCI_01 = (
66,
0.3850,
0.4125,
np.array(
[
0.00000000e00,
0.00000000e00,
1.44698925e-03,
3.90075197e-01,
6.64325016e-01,
7.79951131e-01,
9.02794193e-01,
9.59328741e-01,
9.98980457e-01,
2.15052943e-01,
6.91049895e-05,
0.00000000e00,
]
),
)
S3A_OLCI_02 = (
67,
0.4000,
0.4225,
np.array(
[
0.00000000e00,
0.00000000e00,
5.67130629e-03,
7.80520608e-01,
9.92336528e-01,
9.98163685e-01,
9.64577067e-01,
1.62142192e-01,
3.65144365e-05,
0.00000000e00,
]
),
)
S3A_OLCI_03 = (
68,
0.4300,
0.4525,
np.array(
[
0.00000000e00,
0.00000000e00,
8.82327515e-05,
2.50507297e-01,
9.84845256e-01,
9.98648197e-01,
9.95748807e-01,
7.25352741e-01,
3.29665330e-03,
0.00000000e00,
]
),
)
S3A_OLCI_04 = (
69,
0.4775,
0.5000,
np.array(
[
0.00000000e00,
0.00000000e00,
8.88119025e-05,
2.58959751e-01,
9.67644787e-01,
9.78045026e-01,
9.96403711e-01,
7.45344017e-01,
3.57228954e-03,
0.00000000e00,
]
),
)
S3A_OLCI_05 = (
70,
0.4975,
0.5200,
np.array(
[
0.00000000e00,
0.00000000e00,
9.03196596e-05,
2.61921725e-01,
9.68992890e-01,
9.86484682e-01,
9.97900957e-01,
7.26184125e-01,
3.04349960e-03,
0.00000000e00,
]
),
)
S3A_OLCI_06 = (
71,
0.5475,
0.5700,
np.array(
[
0.00000000e00,
0.00000000e00,
9.12138310e-05,
2.71055434e-01,
9.85744910e-01,
9.93709995e-01,
9.99188281e-01,
7.36202217e-01,
3.20926544e-03,
0.00000000e00,
]
),
)
S3A_OLCI_07 = (
72,
0.6075,
0.6300,
np.array(
[
0.00000000e00,
0.00000000e00,
9.50077324e-05,
2.77317100e-01,
9.76962724e-01,
9.87228638e-01,
9.97972350e-01,
6.98892119e-01,
2.26772837e-03,
0.00000000e00,
]
),
)
S3A_OLCI_08 = (
73,
0.6525,
0.6750,
np.array(
[
0.00000000e00,
0.00000000e00,
1.70652583e-04,
3.53389797e-01,
9.80447862e-01,
9.91507321e-01,
9.97394347e-01,
6.44129999e-01,
1.36300229e-03,
0.00000000e00,
]
),
)
S3A_OLCI_09 = (
74,
0.6625,
0.6825,
np.array(
[
0.00000000e00,
0.00000000e00,
1.66013218e-04,
3.54497132e-01,
9.91183060e-01,
9.96152868e-01,
6.60733265e-01,
1.52801384e-03,
0.00000000e00,
]
),
)
S3A_OLCI_10 = (
75,
0.6700,
0.6900,
np.array(
[
0.00000000e00,
0.00000000e00,
1.38022478e-04,
3.33541643e-01,
9.85871946e-01,
9.97184790e-01,
6.83203266e-01,
1.78238692e-03,
0.00000000e00,
]
),
)
S3A_OLCI_11 = (
76,
0.6950,
0.7175,
np.array(
[
0.00000000e00,
0.00000000e00,
1.29485319e-07,
1.33565896e-02,
8.95928951e-01,
9.96556200e-01,
9.97534770e-01,
9.86969653e-01,
1.02877809e-01,
6.99836795e-06,
]
),
)
S3A_OLCI_12 = (
77,
0.7425,
0.7625,
np.array(
[
0.00000000e00,
0.00000000e00,
5.73078846e-05,
2.62775426e-01,
9.96285919e-01,
9.94959949e-01,
7.44938946e-01,
2.43649259e-03,
0.00000000e00,
]
),
)
S3A_OLCI_13 = (
78,
0.7550,
0.7700,
np.array(
[
0.00000000e00,
4.89582545e-05,
2.62646899e-01,
8.24031122e-01,
3.06534953e-03,
0.00000000e00,
0.00000000e00,
]
),
)
S3A_OLCI_14 = (
79,
0.7575,
0.7725,
np.array(
[
0.00000000e00,
4.38488689e-05,
2.43677851e-01,
9.97014247e-01,
1.17088589e-01,
7.61572220e-06,
0.00000000e00,
]
),
)
S3A_OLCI_15 = (
80,
0.7600,
0.7750,
np.array(
[
0.00000000e00,
5.87943065e-08,
1.02946446e-02,
9.49694946e-01,
1.19850202e-01,
7.39186874e-06,
0.00000000e00,
]
),
)
S3A_OLCI_16 = (
81,
0.7625,
0.7900,
np.array(
[
0.00000000e00,
0.00000000e00,
4.54725243e-08,
8.66502455e-03,
8.54786953e-01,
9.76876743e-01,
9.86449152e-01,
9.97465390e-01,
9.97967879e-01,
9.87477514e-01,
1.31193176e-01,
9.42330409e-06,
]
),
)
S3A_OLCI_17 = (
82,
0.8475,
0.8800,
np.array(
[
0.00000000e00,
0.00000000e00,
1.98596616e-05,
1.94106940e-01,
9.98422526e-01,
9.92162445e-01,
9.82368240e-01,
9.72067967e-01,
9.59774999e-01,
9.47635726e-01,
9.35168865e-01,
7.55614274e-01,
3.37197717e-03,
0.00000000e00,
]
),
)
S3A_OLCI_18 = (
83,
0.8725,
0.8950,
np.array(
[
0.00000000e00,
1.20008056e-08,
4.44776154e-03,
8.44464399e-01,
9.95280744e-01,
9.81079588e-01,
9.61809761e-01,
1.58916299e-01,
1.48886924e-05,
0.00000000e00,
]
),
)
S3A_OLCI_19 = (
84,
0.8875,
0.9100,
np.array(
[
0.00000000e00,
1.21562504e-08,
4.26317347e-03,
8.40394746e-01,
9.94028495e-01,
9.76434111e-01,
9.53033360e-01,
1.61291816e-01,
1.75069782e-05,
0.00000000e00,
]
),
)
S3A_OLCI_20 = (
85,
0.9225,
0.9550,
np.array(
[
0.00000000e00,
0.00000000e00,
4.38829991e-03,
8.39806175e-01,
9.85099657e-01,
9.48581070e-01,
9.12425617e-01,
8.77435250e-01,
8.52489956e-01,
8.32325676e-01,
8.08894612e-01,
1.56714011e-01,
3.28157749e-05,
0.00000000e00,
]
),
)
S3A_OLCI_21 = (
86,
0.9925,
1.0450,
np.array(
[
0.00000000e00,
0.00000000e00,
5.95267671e-03,
8.29963638e-01,
9.76125513e-01,
9.17196169e-01,
8.59092046e-01,
8.01654231e-01,
7.43763056e-01,
6.86917212e-01,
6.30841661e-01,
5.78254852e-01,
5.46055402e-01,
5.16377240e-01,
4.87388894e-01,
4.59188029e-01,
4.31652996e-01,
4.04096076e-01,
3.73472735e-01,
6.44893266e-02,
8.19798435e-05,
0.00000000e00,
]
),
)
# Sentinel-3A SLSTR spectral response functions
# (PREFLIGHT)
# Code used to create these things in
# https://github.com/jgomezdans/sentinel_SRF
S3A_SLSTR_01 = (
87,
0.54000,
0.57000,
np.array(
[
0.01463,
0.14031,
0.57941,
0.95383,
0.99927,
0.98361,
0.98065,
0.99224,
0.86356,
0.74371,
0.25806,
0.05162,
0.00906,
]
),
)
S3A_SLSTR_02 = (
88,
0.64500,
0.67500,
np.array(
[
0.00868,
0.10051,
0.54166,
0.94638,
0.97952,
0.99970,
0.99593,
0.98108,
0.98249,
0.81191,
0.28596,
0.03422,
0.00557,
]
),
)
S3A_SLSTR_03 = (
89,
0.85250,
0.88500,
np.array(
[
0.01457,
0.09982,
0.45901,
0.94725,
0.99882,
0.99531,
0.97779,
0.89514,
0.91546,
0.92751,
0.67702,
0.17794,
0.02665,
0.00552,
]
),
)
S3A_SLSTR_04 = (
90,
1.36250,
1.39000,
np.array(
[
0.08117,
0.62916,
0.97652,
0.96779,
0.98766,
0.99534,
0.99847,
0.99996,
0.95454,
0.51479,
0.08519,
0.00576,
]
),
)
S3A_SLSTR_05 = (
91,
1.56500,
1.66250,
np.array(
[
0.00951,
0.02064,
0.04194,
0.08240,
0.16215,
0.30042,
0.49672,
0.70549,
0.85567,
0.93327,
0.96306,
0.97290,
0.97634,
0.98050,
0.98407,
0.98816,
0.99064,
0.99422,
0.99715,
0.99887,
0.99974,
0.99874,
0.99389,
0.98591,
0.97221,
0.95534,
0.93706,
0.92338,
0.92452,
0.93923,
0.95034,
0.89146,
0.68978,
0.41600,
0.21213,
0.10306,
0.05085,
0.02604,
0.01343,
0.00755,
]
),
)
S3A_SLSTR_06 = (
92,
2.21750,
2.29750,
np.array(
[
0.01257,
0.03348,
0.07603,
0.15844,
0.30026,
0.50042,
0.71448,
0.87789,
0.95880,
0.97991,
0.97956,
0.97917,
0.98339,
0.98979,
0.99621,
0.99969,
0.99941,
0.99572,
0.99031,
0.98586,
0.98433,
0.98171,
0.96554,
0.91002,
0.78868,
0.60765,
0.41211,
0.25045,
0.14086,
0.07530,
0.03827,
0.01809,
0.00721,
]
),
)
# Sentinel-2B MSI spectral response functions
# Taken from https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/document-library/-/asset_publisher/Wk0TKajiISaR/content/sentinel-2a-spectral-responses
S2B_MSI_01 = (
93,
0.4110,
0.4585,
np.array(
[
6.79779800e-03,
5.38819850e-03,
7.13011000e-03,
5.32861200e-03,
6.21707000e-04,
4.19444050e-03,
2.58671000e-03,
8.79308600e-03,
1.88722410e-01,
7.88710046e-01,
8.81448354e-01,
9.26045092e-01,
9.50410310e-01,
9.84288926e-01,
9.76368067e-01,
9.38960136e-01,
8.09016274e-01,
2.07646870e-01,
8.93513100e-03,
0.00000000e00,
]
),
)
S2B_MSI_02 = (
94,
0.4380,
0.5330,
np.array(
[
0.00189331,
0.01708433,
0.01544887,
0.0138867,
0.01450069,
0.01344492,
0.01471763,
0.0430435,
0.25226095,
0.71496001,
0.8320771,
0.85144357,
0.90569382,
0.9338208,
0.91662274,
0.90050851,
0.92085944,
0.91341249,
0.88735796,
0.85194722,
0.85792477,
0.91177803,
0.96940661,
0.99966032,
0.98416305,
0.97523217,
0.95355824,
0.917184,
0.8922514,
0.89550827,
0.9246363,
0.96267905,
0.99679122,
0.97030435,
0.7360636,
0.26382961,
0.07513881,
0.02364449,
0.0,
]
),
)
S2B_MSI_03 = (
95,
0.5360,
0.5835,
np.array(
[
0.00184908,
0.05892162,
0.33349948,
0.86269252,
0.94917532,
0.92057652,
0.90085865,
0.93279058,
0.98347041,
0.99597786,
0.95115183,
0.88000476,
0.83920859,
0.83301579,
0.86656355,
0.86381757,
0.67746396,
0.18777902,
0.02572436,
0.0,
]
),
)
S2B_MSI_04 = (
96,
0.6460,
0.6860,
np.array(
[
5.09031200e-03,
2.29151295e-01,
8.10967523e-01,
9.23442650e-01,
9.02005739e-01,
8.99869183e-01,
9.06886173e-01,
9.34405970e-01,
9.66908428e-01,
9.95809639e-01,
9.94046577e-01,
9.63150260e-01,
9.25809770e-01,
7.68490499e-01,
2.76900547e-01,
1.86252340e-02,
7.44413212e-16,
]
),
)
S2B_MSI_05 = (
97,
0.6940,
0.7140,
np.array(
[
0.01047183,
0.38095721,
0.96844659,
0.99754666,
0.9794087,
0.95094835,
0.89455748,
0.34237254,
0.00243694,
]
),
)
S2B_MSI_06 = (
98,
0.7300,
0.7500,
np.array(
[
0.01743381,
0.58418185,
0.91641661,
0.93978171,
0.97855276,
0.99997792,
0.81917496,
0.05197745,
0.0,
]
),
)
S2B_MSI_07 = (
99,
0.7660,
0.7960,
np.array(
[
0.01028203,
0.1849904,
0.73021678,
0.96816857,
0.96492559,
0.99902331,
0.96569621,
0.91842469,
0.85951719,
0.72377841,
0.23625194,
0.00734482,
0.0,
]
),
)
S2B_MSI_08 = (
100,
0.7740,
0.9090,
np.array(
[
3.86696000e-04,
2.25160330e-02,
8.62638800e-02,
3.41768486e-01,
8.03109115e-01,
9.42316692e-01,
9.63880684e-01,
9.93998947e-01,
9.98257939e-01,
9.86097031e-01,
9.58222106e-01,
9.45610993e-01,
9.46461415e-01,
9.22310559e-01,
8.73037871e-01,
8.30374120e-01,
8.10955964e-01,
7.87625180e-01,
7.54167357e-01,
7.23279542e-01,
7.18941021e-01,
7.44267400e-01,
7.69900245e-01,
7.83440215e-01,
7.81644143e-01,
7.81572121e-01,
7.80064535e-01,
7.67703381e-01,
7.43324604e-01,
7.17699319e-01,
6.88575227e-01,
6.50641275e-01,
6.14005803e-01,
5.91537372e-01,
5.82889219e-01,
5.77470921e-01,
5.72399696e-01,
5.70158326e-01,
5.72213154e-01,
5.78703807e-01,
5.78302049e-01,
5.65806515e-01,
5.49385012e-01,
5.42215679e-01,
5.37997281e-01,
5.13924174e-01,
4.69610434e-01,
4.44484344e-01,
4.47532506e-01,
3.93848039e-01,
2.35014942e-01,
1.02922870e-01,
3.80060080e-02,
1.04894310e-02,
0.00000000e00,
]
),
)
S2B_MSI_8A = (
101,
0.8480,
0.8805,
np.array(
[
1.66175100e-03,
5.28325840e-02,
3.45506138e-01,
8.66965557e-01,
9.87241334e-01,
9.99376033e-01,
9.94482469e-01,
9.81491925e-01,
9.72818786e-01,
9.50032949e-01,
7.78588669e-01,
2.81014567e-01,
3.33933370e-02,
8.12020000e-04,
]
),
)
S2B_MSI_09 = (
102,
0.9300,
0.9575,
np.array(
[
0.01212112,
0.2550252,
0.83734232,
0.94936094,
0.99744452,
0.980285,
0.97377663,
0.9278809,
0.85513588,
0.62111363,
0.09480081,
0.00171671,
]
),
)
S2B_MSI_10 = (
103,
1.3390,
1.4165,
np.array(
[
2.45190000e-05,
1.06782500e-04,
9.93550000e-05,
1.74974500e-04,
3.22212000e-04,
4.58060500e-04,
2.18105000e-03,
1.89617750e-02,
1.11412066e-01,
3.87289252e-01,
7.70430827e-01,
9.59359012e-01,
9.93032255e-01,
9.99720196e-01,
9.90856204e-01,
9.73640319e-01,
9.57577366e-01,
9.43105961e-01,
9.36274454e-01,
9.23628129e-01,
8.31894981e-01,
5.67223531e-01,
2.13253720e-01,
3.33423315e-02,
3.16376500e-03,
6.91521500e-04,
1.94334000e-04,
2.03841500e-04,
1.54509000e-04,
1.61088500e-04,
1.19559000e-04,
0.00000000e00,
]
),
)
S2B_MSI_11 = (
104,
1.5380,
1.6805,
np.array(
[
1.15000000e-05,
1.25332000e-04,
3.43776000e-04,
7.36988500e-04,
1.42877600e-03,
3.17141100e-03,
7.57268400e-03,
2.19172390e-02,
6.29047140e-02,
1.70251784e-01,
3.80879812e-01,
6.60381371e-01,
8.69866550e-01,
9.55250846e-01,
9.77057349e-01,
9.76114190e-01,
9.71480798e-01,
9.69396227e-01,
9.72736269e-01,
9.79977731e-01,
9.84837133e-01,
9.83646395e-01,
9.79391949e-01,
9.75827865e-01,
9.76805245e-01,
9.83603737e-01,
9.92331977e-01,
9.95900604e-01,
9.92101664e-01,
9.83005073e-01,
9.73545660e-01,
9.70486171e-01,
9.75244492e-01,
9.85528272e-01,
9.96435703e-01,
9.99518223e-01,
9.93293536e-01,
9.83975876e-01,
9.79473331e-01,
9.84232683e-01,
9.91723341e-01,
9.85747311e-01,
9.53186779e-01,
8.96044076e-01,
8.50889881e-01,
8.45236530e-01,
8.40540222e-01,
7.13618196e-01,
4.57566037e-01,
2.16596874e-01,
7.35127680e-02,
2.38199245e-02,
8.78944000e-03,
3.97113050e-03,
1.99073900e-03,
9.53045500e-04,
2.81204000e-04,
0.00000000e00,
]
),
)
S2B_MSI_12 = (
105,
2.0650,
2.3050,
np.array(
[
2.27567000e-04,
2.33818100e-03,
5.92493500e-03,
9.50674450e-03,
1.45934790e-02,
2.32456505e-02,
3.72344570e-02,
6.31897490e-02,
1.10694417e-01,
1.93831191e-01,
3.17227795e-01,
4.86127891e-01,
6.72128213e-01,
8.14294745e-01,
8.86329697e-01,
9.01154288e-01,
8.92352598e-01,
8.83263626e-01,
8.82697820e-01,
8.95818268e-01,
9.12841006e-01,
9.30003885e-01,
9.43377038e-01,
9.47987900e-01,
9.48111791e-01,
9.45498813e-01,
9.41894052e-01,
9.43759006e-01,
9.46212496e-01,
9.48440897e-01,
9.50676414e-01,
9.51929392e-01,
9.52552047e-01,
9.52808031e-01,
9.53779111e-01,
9.55188146e-01,
9.57986198e-01,
9.60932208e-01,
9.64122014e-01,
9.62929248e-01,
9.60730243e-01,
9.57674768e-01,
9.53076144e-01,
9.55447935e-01,
9.55356546e-01,
9.52789244e-01,
9.48594073e-01,
9.48422211e-01,
9.46620434e-01,
9.42809458e-01,
9.38918576e-01,
9.44765225e-01,
9.48481875e-01,
9.51379486e-01,
9.55262594e-01,
9.50105202e-01,
9.49712169e-01,
9.57395090e-01,
9.73886163e-01,
9.68734909e-01,
9.67555279e-01,
9.68732559e-01,
9.69967732e-01,
9.67903993e-01,
9.65459532e-01,
9.62297699e-01,
9.59368085e-01,
9.61002221e-01,
9.63700810e-01,
9.65931017e-01,
9.68868150e-01,
9.73363783e-01,
9.77203216e-01,
9.80985309e-01,
9.83336508e-01,
9.87941136e-01,
9.93184480e-01,
9.97493542e-01,
1.00000000e00,
9.93179265e-01,
9.68715091e-01,
9.15824428e-01,
8.31947776e-01,
7.09102747e-01,
5.64024643e-01,
4.25931844e-01,
3.09839746e-01,
2.18177603e-01,
1.49356419e-01,
1.04215226e-01,
7.40635240e-02,
5.28513985e-02,
3.79860860e-02,
2.70345635e-02,
1.38379210e-02,
2.64583650e-03,
0.00000000e00,
]
),
)
# Sentinel-3B OLCI spectral response functions
# changed to center at the max value of each
# band rsr from https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/olci-instrument/spectral-response-function-data
# the mean dataset
S3B_OLCI_01 = (
106,
0.3850,
0.4125,
np.array(
[
0.00000000e00,
0.00000000e00,
9.34810332e-05,
3.01161089e-01,
5.79783356e-01,
7.28688965e-01,
8.90776697e-01,
9.53314424e-01,
9.97316285e-01,
2.06989277e-01,
4.47201260e-06,
0.00000000e00,
]
),
)
S3B_OLCI_02 = (
107,
0.4000,
0.4225,
np.array(
[
0.00000000e00,
0.00000000e00,
1.19249516e-03,
7.79853543e-01,
9.87822759e-01,
9.98838473e-01,
9.87925591e-01,
1.65533011e-01,
3.38914146e-06,
0.00000000e00,
]
),
)
S3B_OLCI_03 = (
108,
0.4300,
0.4525,
np.array(
[
0.00000000e00,
0.00000000e00,
9.30282695e-06,
2.06052543e-01,
9.98428589e-01,
9.97182974e-01,
9.85843591e-01,
7.51246618e-01,
1.85549607e-03,
0.00000000e00,
]
),
)
S3B_OLCI_04 = (
109,
0.4775,
0.5000,
np.array(
[
0.00000000e00,
0.00000000e00,
4.06074021e-05,
2.85736497e-01,
9.80498411e-01,
9.87358384e-01,
9.97301845e-01,
7.10387496e-01,
1.51227169e-03,
0.00000000e00,
]
),
)
S3B_OLCI_05 = (
110,
0.4975,
0.5200,
np.array(
[
0.00000000e00,
0.00000000e00,
4.63640882e-05,
2.88108060e-01,
9.70176583e-01,
9.82820364e-01,
9.96720776e-01,
7.03952046e-01,
1.49862288e-03,
0.00000000e00,
]
),
)
S3B_OLCI_06 = (
111,
0.5475,
0.5700,
np.array(
[
0.00000000e00,
0.00000000e00,
5.04591202e-05,
3.02121393e-01,
9.87815717e-01,
9.94139281e-01,
9.99052182e-01,
6.99925598e-01,
1.38906899e-03,
0.00000000e00,
]
),
)
S3B_OLCI_07 = (
112,
0.6075,
0.6300,
np.array(
[
0.00000000e00,
0.00000000e00,
5.24770401e-05,
3.28445148e-01,
9.78249177e-01,
9.86453480e-01,
9.96904960e-01,
6.37481108e-01,
6.57750246e-04,
0.00000000e00,
]
),
)
S3B_OLCI_08 = (
113,
0.6525,
0.6750,
np.array(
[
0.00000000e00,
0.00000000e00,
1.02038317e-04,
4.23731626e-01,
9.80510445e-01,
9.89802816e-01,
9.95992727e-01,
5.64721221e-01,
2.87047474e-04,
0.00000000e00,
]
),
)
S3B_OLCI_09 = (
114,
0.6650,
0.6850,
np.array(
[
0.00000000e00,
1.01088488e-04,
4.30582345e-01,
9.90586594e-01,
9.95627137e-01,
5.70430795e-01,
2.99212934e-04,
0.00000000e00,
0.00000000e00,
]
),
)
S3B_OLCI_10 = (
115,
0.6700,
0.6900,
np.array(
[
0.00000000e00,
0.00000000e00,
9.20443879e-05,
4.24395382e-01,
9.93044213e-01,
9.96732724e-01,
5.87914764e-01,
3.33714303e-04,
0.00000000e00,
]
),
)
S3B_OLCI_11 = (
116,
0.6950,
0.7175,
np.array(
[
0.00000000e00,
0.00000000e00,
2.27527154e-08,
1.43013420e-02,
9.30039754e-01,
9.89869809e-01,
9.95153682e-01,
9.85467850e-01,
6.04404649e-02,
4.07944824e-07,
]
),
)
S3B_OLCI_12 = (
117,
0.7425,
0.7625,
np.array(
[
0.00000000e00,
0.00000000e00,
3.32680058e-05,
3.35481137e-01,
9.96134240e-01,
9.95335488e-01,
6.74603784e-01,
5.81641718e-04,
0.00000000e00,
]
),
)
S3B_OLCI_13 = (
118,
0.7550,
0.7700,
np.array(
[
0.00000000e00,
2.85560325e-05,
3.35218844e-01,
7.33046075e-01,
6.97655972e-04,
0.00000000e00,
0.00000000e00,
]
),
)
S3B_OLCI_14 = (
119,
0.7550,
0.7725,
np.array(
[
0.00000000e00,
0.00000000e00,
2.51718059e-05,
3.10743254e-01,
9.93217466e-01,
7.62547197e-02,
6.40188179e-07,
0.00000000e00,
]
),
)
S3B_OLCI_15 = (
120,
0.7600,
0.7750,
np.array(
[
0.00000000e00,
9.44449935e-09,
9.67829515e-03,
9.72499368e-01,
8.17682936e-02,
7.17736029e-07,
0.00000000e00,
]
),
)
S3B_OLCI_16 = (
121,
0.7650,
0.7925,
np.array(
[
0.00000000e00,
8.64377579e-09,
8.84274122e-03,
9.17740240e-01,
9.96150751e-01,
9.96332905e-01,
9.96195066e-01,
9.94333040e-01,
9.83695296e-01,
8.05458528e-02,
7.66316013e-07,
0.00000000e00,
]
),
)
S3B_OLCI_17 = (
122,
0.8475,
0.8800,
np.array(
[
0.00000000e00,
0.00000000e00,
1.47858085e-05,
2.54914009e-01,
9.99170692e-01,
9.92365948e-01,
9.84295400e-01,
9.75537469e-01,
9.62032596e-01,
9.47783211e-01,
9.33310423e-01,
6.88572549e-01,
1.08104259e-03,
0.00000000e00,
]
),
)
S3B_OLCI_18 = (
123,
0.8700,
0.8925,
np.array(
[
0.00000000e00,
0.00000000e00,
5.01495276e-09,
6.26933249e-03,
9.02104161e-01,
9.91985752e-01,
9.75609451e-01,
9.53379357e-01,
1.00137591e-01,
1.48780466e-06,
]
),
)
S3B_OLCI_19 = (
124,
0.8850,
0.9075,
np.array(
[
0.00000000e00,
0.00000000e00,
4.91117993e-09,
6.14792354e-03,
9.01922678e-01,
9.90850645e-01,
9.72235276e-01,
9.44141158e-01,
1.00942430e-01,
1.52794114e-06,
]
),
)
S3B_OLCI_20 = (
125,
0.9225,
0.9550,
np.array(
[
0.00000000e00,
3.42848643e-09,
5.17534750e-03,
8.95989159e-01,
9.82463232e-01,
9.47765152e-01,
9.13370826e-01,
8.80142365e-01,
8.52126137e-01,
8.28556200e-01,
8.01942644e-01,
1.01521422e-01,
1.74331314e-06,
0.00000000e00,
]
),
)
S3B_OLCI_21 = (
126,
0.9925,
1.0450,
np.array(
[
0.00000000e00,
0.00000000e00,
2.16191906e-03,
8.62689484e-01,
9.74315053e-01,
9.15567438e-01,
8.57471704e-01,
7.98987260e-01,
7.41776209e-01,
6.85363224e-01,
6.29509263e-01,
5.76679778e-01,
5.44270496e-01,
5.14648842e-01,
4.85713953e-01,
4.57540874e-01,
4.29559156e-01,
4.02004281e-01,
3.74596536e-01,
4.46248175e-02,
4.02350000e-07,
0.00000000e00,
]
),
)
# Sentinel-3B SLSTR spectral response functions
# (PREFLIGHT)
# Code used to create these things in
# https://github.com/jgomezdans/sentinel_SRF
S3B_SLSTR_01 = (
114,
0.54250,
0.57000,
np.array(
[
0.01206,
0.27425,
0.80178,
0.93965,
0.95802,
0.96983,
0.98111,
0.93155,
0.98239,
0.51100,
0.12423,
0.01377,
]
),
)
S3B_SLSTR_02 = (
115,
0.645000,
0.67500,
np.array(
[
0.00923,
0.03648,
0.39244,
0.98581,
0.98523,
0.99845,
0.98163,
0.94989,
0.92945,
0.88710,
0.46271,
0.05538,
0.00904,
]
),
)
S3B_SLSTR_03 = (
116,
0.84750,
0.887500,
np.array(
[
0.00812,
0.00967,
0.02361,
0.10105,
0.58329,
0.96260,
0.96818,
0.94752,
0.89536,
0.96613,
0.98339,
0.99422,
0.63677,
0.12622,
0.02013,
0.00702,
0.00674,
]
),
)
S3B_SLSTR_04 = (
117,
1.36250,
1.39000,
np.array(
[
0.06640,
0.55261,
0.98999,
0.97825,
0.99820,
0.99424,
0.99060,
0.99198,
0.96408,
0.59000,
0.11305,
0.01122,
]
),
)
S3B_SLSTR_05 = (
118,
1.56500,
1.65750,
np.array(
[
0.01293,
0.03096,
0.06178,
0.12220,
0.23370,
0.40437,
0.61348,
0.80127,
0.91617,
0.97052,
0.98583,
0.99257,
0.99218,
0.99289,
0.99469,
0.99441,
0.99995,
0.99743,
0.99660,
0.99717,
0.99956,
0.99335,
0.98563,
0.97131,
0.95391,
0.93399,
0.91583,
0.90968,
0.91672,
0.93473,
0.92294,
0.79910,
0.54450,
0.29666,
0.14590,
0.06893,
0.03427,
0.01641,
]
),
)
S3B_SLSTR_06 = (
119,
2.21750,
2.29250,
np.array(
[
0.01990,
0.05259,
0.12066,
0.25159,
0.45970,
0.70491,
0.89585,
0.98337,
1.00001,
0.99366,
0.98682,
0.98293,
0.98233,
0.98105,
0.97767,
0.97383,
0.97063,
0.96946,
0.97025,
0.97293,
0.97185,
0.94923,
0.87905,
0.74452,
0.56139,
0.37587,
0.22824,
0.12957,
0.07039,
0.03621,
0.01760,
]
),
)
# Redefined MODIS TERRA and AQUA bands to a more accurate spectral
# sampling actually measured from each satellite
# and also added the ocean bands
# I'm using the spectra found on
# * TERRA: http://oceancolor.gsfc.nasa.gov/DOCS/RSR/HMODIST_RSRs.txt
# * AQUA: http://oceancolor.gsfc.nasa.gov/DOCS/RSR/HMODISA_RSRs.txt
# And the code that creates the bands is available here:
# https://gist.github.com/jgomezdans/0cd6fc1537e5a76e5d3971ad167badd6
ACCURATE_MODIS_TERRA_1 = (
20,
0.61500,
0.68000,
np.array(
[
0.02807,
0.14457,
0.37699,
0.63749,
0.76990,
0.78074,
0.78695,
0.79042,
0.79757,
0.81003,
0.82398,
0.83735,
0.85865,
0.89204,
0.91952,
0.96325,
0.99380,
0.99782,
0.95684,
0.83402,
0.72271,
0.57700,
0.39879,
0.22151,
0.09847,
0.04423,
0.01564,
]
),
)
ACCURATE_MODIS_TERRA_2 = (
21,
0.82000,
0.89750,
np.array(
[
0.01046,
0.02323,
0.04527,
0.08294,
0.13231,
0.20333,
0.29725,
0.45232,
0.64668,
0.82575,
0.96775,
0.99464,
0.98512,
0.97220,
0.96665,
0.96893,
0.96614,
0.98157,
0.98110,
0.96206,
0.91074,
0.79448,
0.63300,
0.40130,
0.20118,
0.10805,
0.05918,
0.04157,
0.03105,
0.02474,
0.01912,
0.01369,
]
),
)
ACCURATE_MODIS_TERRA_3 = (
22,
0.45250,
0.48000,
np.array(
[
0.02715,
0.19656,
0.66544,
0.98628,
0.93702,
0.85392,
0.87385,
0.92127,
0.85865,
0.59029,
0.21396,
0.02998,
]
),
)
ACCURATE_MODIS_TERRA_4 = (
23,
0.54000,
0.56750,
np.array(
[
0.04011,
0.24383,
0.65195,
0.94575,
0.99606,
0.99064,
0.98572,
0.97833,
0.93210,
0.70581,
0.31567,
0.05180,
]
),
)
ACCURATE_MODIS_TERRA_5 = (
24,
1.21500,
1.27000,
np.array(
[
0.01581,
0.03020,
0.05587,
0.10725,
0.19486,
0.33641,
0.50475,
0.70053,
0.84740,
0.94598,
0.98540,
0.99393,
0.97463,
0.88957,
0.76061,
0.56817,
0.34327,
0.20250,
0.10511,
0.05389,
0.03478,
0.01910,
0.01206,
]
),
)
ACCURATE_MODIS_TERRA_6 = (
25,
1.59750,
1.66000,
np.array(
[
0.01510,
0.02596,
0.04589,
0.08177,
0.14737,
0.25125,
0.39260,
0.55170,
0.72028,
0.88082,
0.98416,
0.99335,
0.97750,
0.96290,
0.92923,
0.86426,
0.74879,
0.60357,
0.45702,
0.33580,
0.22364,
0.11741,
0.06227,
0.04009,
0.02624,
0.01373,
]
),
)
ACCURATE_MODIS_TERRA_7 = (
26,
2.06000,
2.17500,
np.array(
[
0.01090,
0.01563,
0.02103,
0.03128,
0.04292,
0.06585,
0.09211,
0.14750,
0.21146,
0.31098,
0.42048,
0.54758,
0.67979,
0.79133,
0.89692,
0.94566,
0.97822,
0.99110,
0.99837,
0.96789,
0.92654,
0.89933,
0.87611,
0.84122,
0.80300,
0.76182,
0.71977,
0.68366,
0.64921,
0.61062,
0.57081,
0.53225,
0.49405,
0.44690,
0.39718,
0.34026,
0.28131,
0.22290,
0.16465,
0.12282,
0.08566,
0.06288,
0.04430,
0.03179,
0.02100,
0.01462,
0.01036,
]
),
)
ACCURATE_MODIS_TERRA_8 = (
27,
0.40000,
0.42250,
np.array(
[
0.01396,
0.13170,
0.59932,
0.67268,
0.79013,
0.48200,
0.89280,
0.82045,
0.24315,
0.03272,
]
),
)
ACCURATE_MODIS_TERRA_9 = (
28,
0.43500,
0.45000,
np.array(
[
0.11465,
0.52805,
0.94626,
0.99552,
0.91925,
0.37027,
0.03831,
]
),
)
ACCURATE_MODIS_TERRA_10 = (
29,
0.47750,
0.49500,
np.array(
[
0.01416,
0.15956,
0.66826,
0.94806,
0.99412,
0.95485,
0.46874,
0.02232,
]
),
)
ACCURATE_MODIS_TERRA_11 = (
30,
0.52000,
0.54000,
np.array(
[
0.01480,
0.24145,
0.76250,
0.97399,
0.99901,
0.90343,
0.63643,
0.18019,
0.01435,
]
),
)
ACCURATE_MODIS_TERRA_12 = (
31,
0.53750,
0.55500,
np.array(
[
0.01470,
0.17968,
0.64797,
0.98082,
0.99460,
0.86391,
0.42490,
0.04215,
]
),
)
ACCURATE_MODIS_TERRA_13 = (
32,
0.65750,
0.67500,
np.array(
[
0.06338,
0.37724,
0.81862,
0.99604,
0.88368,
0.62629,
0.25126,
0.01371,
]
),
)
ACCURATE_MODIS_TERRA_14 = (
33,
0.66750,
0.68750,
np.array(
[
0.05618,
0.27689,
0.68142,
0.97341,
0.98489,
0.86696,
0.53632,
0.14128,
0.01578,
]
),
)
ACCURATE_MODIS_TERRA_15 = (
34,
0.73750,
0.75500,
np.array(
[
0.04077,
0.23050,
0.62169,
0.94550,
0.97978,
0.75860,
0.35374,
0.05302,
]
),
)
ACCURATE_MODIS_TERRA_16 = (
35,
0.85250,
0.88000,
np.array(
[
0.02770,
0.11693,
0.34382,
0.66672,
0.91719,
0.99906,
0.98123,
0.91350,
0.70598,
0.41804,
0.10558,
0.02056,
]
),
)
ACCURATE_MODIS_AQUA_1 = (
36,
0.61500,
0.68000,
np.array(
[
0.02807,
0.14457,
0.37699,
0.63749,
0.76990,
0.78074,
0.78695,
0.79042,
0.79757,
0.81003,
0.82398,
0.83735,
0.85865,
0.89204,
0.91952,
0.96325,
0.99380,
0.99782,
0.95684,
0.83402,
0.72271,
0.57700,
0.39879,
0.22151,
0.09847,
0.04423,
0.01564,
]
),
)
ACCURATE_MODIS_AQUA_2 = (
37,
0.82000,
0.89750,
np.array(
[
0.01046,
0.02323,
0.04527,
0.08294,
0.13231,
0.20333,
0.29725,
0.45232,
0.64668,
0.82575,
0.96775,
0.99464,
0.98512,
0.97220,
0.96665,
0.96893,
0.96614,
0.98157,
0.98110,
0.96206,
0.91074,
0.79448,
0.63300,
0.40130,
0.20118,
0.10805,
0.05918,
0.04157,
0.03105,
0.02474,
0.01912,
0.01369,
]
),
)
ACCURATE_MODIS_AQUA_3 = (
38,
0.45250,
0.48000,
np.array(
[
0.02715,
0.19656,
0.66544,
0.98628,
0.93702,
0.85392,
0.87385,
0.92127,
0.85865,
0.59029,
0.21396,
0.02998,
]
),
)
ACCURATE_MODIS_AQUA_4 = (
39,
0.54000,
0.56750,
np.array(
[
0.04011,
0.24383,
0.65195,
0.94575,
0.99606,
0.99064,
0.98572,
0.97833,
0.93210,
0.70581,
0.31567,
0.05180,
]
),
)
ACCURATE_MODIS_AQUA_5 = (
40,
1.21500,
1.27000,
np.array(
[
0.01581,
0.03020,
0.05587,
0.10725,
0.19486,
0.33641,
0.50475,
0.70053,
0.84740,
0.94598,
0.98540,
0.99393,
0.97463,
0.88957,
0.76061,
0.56817,
0.34327,
0.20250,
0.10511,
0.05389,
0.03478,
0.01910,
0.01206,
]
),
)
ACCURATE_MODIS_AQUA_6 = (
41,
1.59750,
1.66000,
np.array(
[
0.01510,
0.02596,
0.04589,
0.08177,
0.14737,
0.25125,
0.39260,
0.55170,
0.72028,
0.88082,
0.98416,
0.99335,
0.97750,
0.96290,
0.92923,
0.86426,
0.74879,
0.60357,
0.45702,
0.33580,
0.22364,
0.11741,
0.06227,
0.04009,
0.02624,
0.01373,
]
),
)
ACCURATE_MODIS_AQUA_7 = (
42,
2.06000,
2.17500,
np.array(
[
0.01090,
0.01563,
0.02103,
0.03128,
0.04292,
0.06585,
0.09211,
0.14750,
0.21146,
0.31098,
0.42048,
0.54758,
0.67979,
0.79133,
0.89692,
0.94566,
0.97822,
0.99110,
0.99837,
0.96789,
0.92654,
0.89933,
0.87611,
0.84122,
0.80300,
0.76182,
0.71977,
0.68366,
0.64921,
0.61062,
0.57081,
0.53225,
0.49405,
0.44690,
0.39718,
0.34026,
0.28131,
0.22290,
0.16465,
0.12282,
0.08566,
0.06288,
0.04430,
0.03179,
0.02100,
0.01462,
0.01036,
]
),
)
ACCURATE_MODIS_AQUA_8 = (
43,
0.40250,
0.4225,
np.array(
[
0.08730698,
0.5009796,
0.62325783,
0.73623119,
0.52280358,
0.83059659,
0.91752085,
0.32830619,
0.0332021,
]
),
)
ACCURATE_MODIS_AQUA_9 = (
44,
0.43250,
0.45000,
np.array(
[
0.01902,
0.09698,
0.51329,
0.92505,
0.99995,
0.94153,
0.39117,
0.03671,
]
),
)
ACCURATE_MODIS_AQUA_10 = (
45,
0.47750,
0.49500,
np.array(
[
0.01964,
0.09723,
0.58592,
0.96018,
0.98957,
0.98879,
0.59678,
0.03983,
]
),
)
ACCURATE_MODIS_AQUA_11 = (
46,
0.52000,
0.54000,
np.array(
[
0.01215,
0.19041,
0.68607,
0.98495,
0.99800,
0.93087,
0.70338,
0.25414,
0.02052,
]
),
)
ACCURATE_MODIS_AQUA_12 = (
47,
0.53750,
0.55500,
np.array(
[
0.02111,
0.16232,
0.57148,
0.97131,
0.99150,
0.87360,
0.50867,
0.07715,
]
),
)
ACCURATE_MODIS_AQUA_13 = (
48,
0.65750,
0.67500,
np.array(
[
0.04175,
0.32634,
0.79150,
0.99988,
0.89403,
0.68671,
0.28090,
0.01440,
]
),
)
ACCURATE_MODIS_AQUA_14 = (
49,
0.66750,
0.68750,
np.array(
[
0.03422,
0.18745,
0.58587,
0.93366,
0.99127,
0.93329,
0.62790,
0.22864,
0.02571,
]
),
)
ACCURATE_MODIS_AQUA_15 = (
50,
0.73500,
0.75500,
np.array(
[
0.01266,
0.02854,
0.19037,
0.58331,
0.92908,
0.99234,
0.77888,
0.37822,
0.05551,
]
),
)
ACCURATE_MODIS_AQUA_16 = (
51,
0.85250,
0.88000,
np.array(
[
0.01859,
0.08363,
0.28236,
0.59771,
0.87955,
0.99942,
0.97670,
0.94504,
0.75962,
0.47919,
0.14822,
0.02755,
]
),
)
# PROBAV_CAMERA_BAND
PROBAV_1_01 = (
127,
0.43250,
0.49000,
np.array(
[
0.00701,
0.01403,
0.29759,
0.76866,
0.85090,
0.85261,
0.86397,
0.86315,
0.87876,
0.89403,
0.90585,
0.91606,
0.86274,
0.87280,
0.89960,
0.92657,
0.90944,
0.91771,
0.92900,
0.95977,
0.98987,
0.93182,
0.23845,
0.01315,
]
),
)
PROBAV_2_01 = (
128,
0.43750,
0.49000,
np.array(
[
0.11753,
0.68816,
0.83312,
0.86384,
0.85535,
0.84936,
0.91241,
0.94894,
0.96350,
0.97903,
0.93099,
0.93834,
0.95654,
0.98125,
0.94097,
0.94453,
0.96126,
0.97506,
0.98012,
0.97479,
0.35092,
0.02275,
]
),
)
PROBAV_3_01 = (
129,
0.43250,
0.49000,
np.array(
[
0.00831,
0.01630,
0.26908,
0.75646,
0.84782,
0.85869,
0.87579,
0.87686,
0.92334,
0.94334,
0.97579,
0.98704,
0.92646,
0.93014,
0.95685,
0.97380,
0.94210,
0.94585,
0.95847,
0.97944,
0.99017,
0.94958,
0.26846,
0.01759,
]
),
)
PROBAV_1_02 = (
130,
0.61250,
0.70000,
np.array(
[
0.09219,
0.79037,
0.93792,
0.97300,
0.95793,
0.94941,
0.94262,
0.94200,
0.94969,
0.94499,
0.94095,
0.88377,
0.88352,
0.88640,
0.90464,
0.88917,
0.86092,
0.85622,
0.84943,
0.87536,
0.92689,
0.94288,
0.93296,
0.90110,
0.91734,
0.94771,
0.97572,
0.93463,
0.93636,
0.94238,
0.92302,
0.95991,
0.99355,
0.66343,
0.16410,
0.02510,
]
),
)
PROBAV_2_02 = (
131,
0.61250,
0.70250,
np.array(
[
0.05112,
0.63546,
0.94863,
0.99405,
0.98435,
0.98583,
0.97631,
0.97432,
0.99088,
0.99118,
0.97510,
0.94569,
0.92452,
0.93961,
0.94746,
0.92908,
0.91461,
0.88604,
0.88331,
0.90463,
0.94046,
0.96590,
0.95591,
0.91342,
0.91923,
0.94040,
0.95123,
0.94757,
0.93538,
0.93531,
0.92470,
0.95749,
0.98006,
0.65677,
0.16783,
0.02719,
0.00596,
]
),
)
PROBAV_3_02 = (
132,
0.61000,
0.70250,
np.array(
[
0.00693,
0.11182,
0.84741,
0.95372,
0.99692,
0.98211,
0.98042,
0.97529,
0.98737,
0.98246,
0.99340,
0.99401,
0.94669,
0.92033,
0.93551,
0.95167,
0.92947,
0.90131,
0.89177,
0.87599,
0.91253,
0.94400,
0.96551,
0.93722,
0.91432,
0.90637,
0.93886,
0.95319,
0.93674,
0.93029,
0.92951,
0.91037,
0.94481,
0.97393,
0.67624,
0.17566,
0.02900,
0.00671,
]
),
)
PROBAV_1_03 = (
133,
0.75750,
0.92250,
np.array(
[
0.00745,
0.01299,
0.02989,
0.07570,
0.18032,
0.38076,
0.67196,
0.90632,
0.99224,
0.98506,
0.97271,
0.96036,
0.95169,
0.94302,
0.93462,
0.92623,
0.91625,
0.90627,
0.90337,
0.90048,
0.89904,
0.89761,
0.88804,
0.87848,
0.87390,
0.86933,
0.86619,
0.86305,
0.85216,
0.84127,
0.83236,
0.82345,
0.81794,
0.81244,
0.80895,
0.80547,
0.79105,
0.77663,
0.76090,
0.74517,
0.74453,
0.74390,
0.72234,
0.70079,
0.68596,
0.67113,
0.65512,
0.63912,
0.62617,
0.61322,
0.60063,
0.58805,
0.58343,
0.57882,
0.56859,
0.55837,
0.53969,
0.52101,
0.50665,
0.49770,
0.48863,
0.47636,
0.42195,
0.28948,
0.13073,
0.04062,
0.01252,
]
),
)
PROBAV_2_03 = (
134,
0.75750,
0.92500,
np.array(
[
0.00675,
0.01079,
0.02156,
0.04730,
0.10797,
0.23157,
0.46667,
0.74601,
0.94127,
1.00000,
0.98746,
0.97493,
0.98095,
0.98698,
0.97258,
0.95819,
0.95082,
0.94345,
0.93537,
0.92730,
0.91901,
0.91072,
0.90696,
0.90321,
0.89724,
0.89127,
0.88293,
0.87458,
0.86800,
0.86141,
0.85505,
0.84869,
0.83384,
0.81899,
0.81435,
0.80971,
0.79835,
0.78699,
0.78169,
0.77639,
0.76419,
0.75199,
0.73214,
0.71230,
0.69639,
0.68048,
0.67196,
0.66344,
0.64948,
0.63552,
0.62135,
0.60718,
0.59919,
0.59120,
0.58136,
0.57152,
0.55307,
0.53462,
0.52212,
0.50539,
0.49683,
0.48331,
0.45572,
0.35616,
0.19922,
0.07619,
0.02540,
0.00794,
]
),
)
PROBAV_3_03 = (
135,
0.75750,
0.92250,
np.array(
[
0.01412,
0.02358,
0.04875,
0.11377,
0.24868,
0.48046,
0.76516,
0.95847,
0.99996,
0.97437,
0.96656,
0.95875,
0.94469,
0.93063,
0.92043,
0.91024,
0.90635,
0.90246,
0.90033,
0.89819,
0.88361,
0.86903,
0.86105,
0.85306,
0.84976,
0.84645,
0.83716,
0.82787,
0.82268,
0.81750,
0.80487,
0.79225,
0.78843,
0.78460,
0.78373,
0.78285,
0.76894,
0.75503,
0.75029,
0.74555,
0.73365,
0.72175,
0.70233,
0.68292,
0.67326,
0.66361,
0.65092,
0.63823,
0.62028,
0.60233,
0.59043,
0.57853,
0.57334,
0.56815,
0.55428,
0.54040,
0.52628,
0.51217,
0.49737,
0.48644,
0.48074,
0.46443,
0.38751,
0.24328,
0.09891,
0.02944,
0.00937,
]
),
)
PROBAV_1_04 = (
136,
1.53250,
1.67250,
np.array(
[
0.00534,
0.00696,
0.01016,
0.01336,
0.01887,
0.02438,
0.03556,
0.04675,
0.06724,
0.08774,
0.12937,
0.17099,
0.25417,
0.33734,
0.45779,
0.57823,
0.69640,
0.81458,
0.88284,
0.95110,
0.97094,
0.99078,
0.99461,
0.99845,
0.99841,
0.99838,
0.99286,
0.98735,
0.97800,
0.96866,
0.95989,
0.95112,
0.95260,
0.95408,
0.96743,
0.98078,
0.99039,
1.00000,
0.94894,
0.89787,
0.76385,
0.62983,
0.48925,
0.34868,
0.26464,
0.18059,
0.13647,
0.09235,
0.07131,
0.05027,
0.03929,
0.02830,
0.02209,
0.01588,
0.01235,
0.00882,
0.00660,
]
),
)
PROBAV_2_04 = (
137,
1.53250,
1.67250,
np.array(
[
0.00509,
0.00663,
0.00939,
0.01215,
0.01793,
0.02370,
0.03610,
0.04850,
0.07042,
0.09233,
0.13681,
0.18130,
0.26779,
0.35429,
0.47954,
0.60479,
0.72485,
0.84490,
0.90797,
0.97104,
0.98552,
1.00000,
0.99940,
0.99879,
0.99606,
0.99333,
0.98735,
0.98138,
0.97169,
0.96201,
0.95450,
0.94698,
0.94917,
0.95135,
0.96560,
0.97984,
0.98773,
0.99562,
0.93668,
0.87774,
0.73997,
0.60221,
0.46463,
0.32705,
0.24772,
0.16839,
0.12672,
0.08505,
0.06463,
0.04421,
0.03413,
0.02405,
0.01846,
0.01287,
0.01005,
0.00724,
0.00559,
]
),
)
PROBAV_3_04 = (
138,
1.53750,
1.66750,
np.array(
[
0.00743,
0.01027,
0.01657,
0.02287,
0.03624,
0.04961,
0.07428,
0.09896,
0.14959,
0.20021,
0.29657,
0.39293,
0.52013,
0.64734,
0.75301,
0.85867,
0.90658,
0.95449,
0.96447,
0.97445,
0.97526,
0.97606,
0.97496,
0.97385,
0.96927,
0.96469,
0.95776,
0.95082,
0.94890,
0.94698,
0.95556,
0.96413,
0.98207,
1.00000,
0.99839,
0.99678,
0.91467,
0.83256,
0.68482,
0.53707,
0.41053,
0.28399,
0.21417,
0.14435,
0.10815,
0.07194,
0.05424,
0.03654,
0.02704,
0.01755,
0.01287,
0.00819,
0.00619,
]
),
)
# All of the original predefined wavelengths from 6S
# CONSTANT_NAME = (ID for Constant, Start Wavelength, End Wavelength)
METEOSAT_VISIBLE = (-2, 0.35, 1.11)
GOES_EAST_VISIBLE = (-3, 0.49, 0.9)
GOES_WEST_VISIBLE = (-4, 0.49, 0.9)
AVHRR_NOAA6_B1 = (-5, 0.55, 0.75)
AVHRR_NOAA6_B2 = (-6, 0.69, 1.12)
AVHRR_NOAA7_B1 = (-7, 0.5, 0.8)
AVHRR_NOAA7_B2 = (-8, 0.64, 1.17)
AVHRR_NOAA8_B1 = (-9, 0.54, 1.01)
AVHRR_NOAA8_B2 = (-10, 0.68, 1.12)
AVHRR_NOAA9_B1 = (-11, 0.53, 0.81)
AVHRR_NOAA9_B2 = (-12, 0.68, 1.17)
AVHRR_NOAA10_B1 = (-13, 0.53, 0.78)
AVHRR_NOAA10_B2 = (-14, 0.6, 1.19)
AVHRR_NOAA11_B1 = (-15, 0.54, 0.82)
AVHRR_NOAA11_B2 = (-16, 0.6, 1.12)
SPOT_HRV1_B1 = (-17, 0.47, 0.65)
SPOT_HRV1_B2 = (-18, 0.6, 0.72)
SPOT_HRV1_B3 = (-19, 0.73, 0.93)
SPOT_HRV1_PAN = (-20, 0.47, 0.79)
SPOT_HRV2_B1 = (-21, 0.47, 0.65)
SPOT_HRV2_B2 = (-22, 0.59, 0.73)
SPOT_HRV2_B3 = (-23, 0.74, 0.94)
SPOT_HRV2_PAN = (-24, 0.47, 0.79)
LANDSAT_TM_B1 = (-25, 0.43, 0.56)
LANDSAT_TM_B2 = (-26, 0.5, 0.65)
LANDSAT_TM_B3 = (-27, 0.58, 0.74)
LANDSAT_TM_B4 = (-28, 0.73, 0.95)
LANDSAT_TM_B5 = (-29, 1.5025, 1.89)
LANDSAT_TM_B7 = (-30, 1.95, 2.41)
LANDSAT_MSS_B1 = (-31, 0.475, 0.64)
LANDSAT_MSS_B2 = (-32, 0.58, 0.75)
LANDSAT_MSS_B3 = (-33, 0.655, 0.855)
LANDSAT_MSS_B4 = (-34, 0.785, 1.1)
ER2_MAS_B1 = (-35, 0.5025, 0.5875)
ER2_MAS_B2 = (-36, 0.6075, 0.7)
ER2_MAS_B3 = (-37, 0.83, 0.9125)
ER2_MAS_B4 = (-38, 0.9, 0.9975)
ER2_MAS_B5 = (-39, 1.82, 1.9575)
ER2_MAS_B6 = (-40, 2.0950, 2.1925)
ER2_MAS_B7 = (-41, 3.58, 3.87)
MODIS_B1 = (-42, 0.61, 0.685)
MODIS_B2 = (-43, 0.82, 0.9025)
MODIS_B3 = (-44, 0.45, 0.4825)
MODIS_B4 = (-45, 0.54, 0.57)
MODIS_B5 = (-46, 1.2150, 1.27)
MODIS_B6 = (-47, 1.6, 1.665)
MODIS_B7 = (-48, 2.0575, 2.1825)
MODIS_B8 = (-49, 0.4025, 0.4225)
AVHRR_NOAA12_B1 = (-50, 0.5, 1.0)
AVHRR_NOAA12_B2 = (-51, 0.65, 1.12)
AVHRR_NOAA14_B1 = (-52, 0.5, 1.11)
AVHRR_NOAA14_B2 = (-53, 0.68, 1.1)
POLDER_B1 = (-54, 0.4125, 0.4775)
POLDER_B2 = (-55, 0.41, 0.5225)
POLDER_B3 = (-56, 0.5325, 0.595)
POLDER_B4 = (-57, 0.63, 0.7025)
POLDER_B5 = (-58, 0.745, 0.78)
POLDER_B6 = (-59, 0.7, 0.83)
POLDER_B7 = (-60, 0.81, 0.92)
POLDER_B8 = (-61, 0.8650, 0.94)
SEAWIFS_B1 = (-62, 0.3825, 0.7)
SEAWIFS_B2 = (-63, 0.38, 0.58)
SEAWIFS_B3 = (-64, 0.38, 1.02)
SEAWIFS_B4 = (-65, 0.38, 1.02)
SEAWIFS_B5 = (-66, 0.3825, 1.15)
SEAWIFS_B6 = (-67, 0.3825, 1.05)
SEAWIFS_B7 = (-68, 0.38, 1.15)
SEAWIFS_B8 = (-69, 0.38, 1.15)
AATSR_B1 = (-70, 0.525, 0.5925)
AATSR_B2 = (-71, 0.6275, 0.6975)
AATSR_B3 = (-72, 0.8325, 0.9025)
AATSR_B4 = (-73, 1.4475, 1.7775)
MERIS_B1 = (-74, 0.412, 0.412 + 0.00998)
MERIS_B2 = (-75, 0.442, 0.442 + 0.00997)
MERIS_B3 = (-76, 0.489, 0.489 + 0.00997)
MERIS_B4 = (-77, 0.509, 0.509 + 0.00997)
MERIS_B5 = (-78, 0.559, 0.559 + 0.00997)
MERIS_B6 = (-79, 0.619, 0.619 + 0.00997)
MERIS_B7 = (-80, 0.664, 0.664 + 0.00998)
MERIS_B8 = (-81, 0.681, 0.681 + 0.00749)
MERIS_B9 = (-82, 0.708, 0.708 + 0.00999)
MERIS_B10 = (-83, 0.753, 0.753 + 0.00749)
MERIS_B11 = (-84, 0.760, 0.760 + 0.00374)
MERIS_B12 = (-85, 0.778, 0.778 + 0.00150)
MERIS_B13 = (-86, 0.865, 0.865 + 0.002)
MERIS_B14 = (-87, 0.885, 0.885 + 0.001)
MERIS_B15 = (-88, 0.9, 0.9 + 0.001)
GLI_B1 = (-89, 0.37, 0.3925)
GLI_B2 = (-90, 0.3875, 0.4125)
GLI_B3 = (-91, 0.3975, 0.4275)
GLI_B4 = (-92, 0.4475, 0.505)
GLI_B5 = (-93, 0.4475, 0.46)
GLI_B6 = (-94, 0.475, 0.505)
GLI_B7 = (-95, 0.5075, 0.5325)
GLI_B8 = (-96, 0.5265, 0.56)
GLI_B9 = (-97, 0.5475, 0.5825)
GLI_B10 = (-98, 0.61, 0.64)
GLI_B11 = (-99, 0.6525, 0.6825)
GLI_B12 = (-100, 0.665, 0.695)
GLI_B13 = (-101, 0.6625, 0.6975)
GLI_B14 = (-102, 0.6925, 0.7275)
GLI_B15 = (-103, 0.6925, 0.7275)
GLI_B16 = (-104, 0.7325, 0.7675)
GLI_B17 = (-105, 0.75, 0.775)
GLI_B18 = (-106, 0.84, 0.8925)
GLI_B19 = (-107, 0.85, 0.88)
GLI_B20 = (-108, 0.415, 0.5075)
GLI_B21 = (-109, 0.505, 0.58)
GLI_B22 = (-110, 0.6075, 0.715)
GLI_B23 = (-111, 0.745, 0.9075)
GLI_B24 = (-112, 1.03, 1.07)
GLI_B25 = (-113, 1.085, 1.19)
GLI_B26 = (-114, 1.22, 1.2625)
GLI_B27 = (-115, 1.3475, 1.415)
GLI_B28 = (-116, 1.515, 1.77)
GLI_B29 = (-117, 2.055, 2.345)
GLI_B30 = (-118, 3.22, 4.0)
ALI_B1P = (-119, 0.4225, 0.4625)
ALI_B1 = (-120, 0.4325, 0.550)
ALI_B2 = (-121, 0.5, 0.63)
ALI_B3 = (-122, 0.5775, 0.750)
ALI_B4 = (-123, 0.7525, 0.8375)
ALI_B4P = (-124, 0.8025, 0.935)
ALI_B5P = (-125, 1.130, 1.345)
ALI_B5 = (-126, 1.47, 1.820)
ALI_B7 = (-127, 1.98, 2.53)
ASTER_B1 = (-128, 0.485, 0.6425)
ASTER_B2 = (-129, 0.590, 0.730)
ASTER_B3N = (-130, 0.720, 0.9075)
ASTER_B3B = (-131, 0.720, 0.9225)
ASTER_B4 = (-132, 1.57, 1.7675)
ASTER_B5 = (-133, 2.120, 2.2825)
ASTER_B6 = (-134, 2.150, 2.295)
ASTER_B7 = (-135, 2.210, 2.39)
ASTER_B8 = (-136, 2.250, 2.244)
ASTER_B9 = (-137, 2.2975, 2.4875)
LANDSAT_ETM_B1 = (-138, 0.435, 0.52)
LANDSAT_ETM_B2 = (-139, 0.5, 0.6225)
LANDSAT_ETM_B3 = (-140, 0.615, 0.7025)
LANDSAT_ETM_B4 = (-141, 0.74, 0.9125)
LANDSAT_ETM_B5 = (-142, 1.51, 1.7875)
LANDSAT_ETM_B7 = (-143, 2.015, 2.3775)
HYPBLUE_B1 = (-144, 0.4375, 0.5)
HYPBLUE_B2 = (-145, 0.435, 0.52)
SPOT_VGT_B1 = (-146, 0.4175, 0.5)
SPOT_VGT_B2 = (-147, 0.5975, 0.7675)
SPOT_VGT_B3 = (-148, 0.7325, 0.9575)
SPOT_VGT_B4 = (-149, 1.5225, 1.8)
VIIRS_BM1 = (-150, 0.4025, 0.4225)
VIIRS_BM2 = (-151, 0.4350, 0.4550)
VIIRS_BM3 = (-152, 0.4775, 0.4975)
VIIRS_BM4 = (-153, 0.5450, 0.565)
VIIRS_BM5 = (-154, 0.6625, 0.6825)
VIIRS_BM6 = (-155, 0.7375, 0.7525)
VIIRS_BM7 = (-156, 0.8450, 0.8850)
VIIRS_BM8 = (-157, 1.23, 1.25)
VIIRS_BM9 = (-158, 1.37, 1.385)
VIIRS_BM10 = (-159, 1.58, 1.64)
VIIRS_BM11 = (-160, 2.225, 2.275)
VIIRS_BM12 = (-161, 3.61, 3.79)
VIIRS_BI1 = (-162, 0.6, 0.68)
VIIRS_BI2 = (-163, 0.845, 0.885)
VIIRS_BI3 = (-164, 1.58, 1.64)
VIIRS_BI4 = (-165, 3.55, 3.93)