def __init__(self, lai_coef=0.1, s=0.015, omega=0.1, **kwargs):
"""
Initialize with same arguemnts as superClass 'Simulator'.
"""
super(S1_simulator, self).__init__(mission="Sentinel-1b", **kwargs)
# Setup SAR RT spectra list (Sentinel 1)
self.freq = 5.405
self.theta = np.deg2rad(37.0)
self.stype = 'turbid_rayleigh'
#self.stype='turbid_isotropic'
self.surf = 'Oh92'
# self.surf = 'Dubois95'
self.models = {'surface': self.surf, 'canopy': self.stype}
self.s = s # 0.02
self.lai_coef = lai_coef # 0.1
self.eps = 15. - 0.j
omega = omega # 0.12 single scattering albedo
self.SAR_list = [
sense_mod.SingleScatRT(
surface=sense_soil.Soil(mv=self.soilm_lst[x],
f=self.freq,
s=s,
clay=0.23,
sand=0.27,
bulk=1.65),
#surface=sense_soil.Soil(eps=self.eps, f=self.freq, s=self.s),
canopy=sense_canopy.OneLayer(
ke_h=self.lai_coef *
self.state_lst[x].lai, # extinction coefficient
ke_v=self.lai_coef * self.state_lst[x].lai,
d=self.state_lst[x].can_height,
ks_h=omega *
(self.lai_coef *
self.state_lst[x].lai), # volume scattering coeff
ks_v=omega * (self.lai_coef * self.state_lst[x].lai)),
models=self.models,
# theta=np.deg2rad(self.vza_lst[x]),
theta=self.theta,
freq=self.freq) for x in xrange(len(self.state_lst))
]
for s in self.SAR_list:
s.sigma0()
self.backscatter_keys = ['vv', 'hh', 'hv']
# Extract total backscatter (and convert to dB) in the three polarisations from SAR list
self.stot_hv = np.array([
10 * np.log10(self.SAR_list[s1c].__dict__['stot']['hv'])
for s1c in xrange(len(self.SAR_list))
])
self.stot_hh = np.array([
10 * np.log10(self.SAR_list[s1c].__dict__['stot']['hh'])
for s1c in xrange(len(self.SAR_list))
])
self.stot_vv = np.array([
10 * np.log10(self.SAR_list[s1c].__dict__['stot']['vv'])
for s1c in xrange(len(self.SAR_list))
])
def __init__(self, lai_coef=0.01, s=0.015, omega=0.1, **kwargs):
"""
Initialize with same arguemnts as superClass 'Simulator'.
"""
super(S1_simulator_testbed, self).__init__(mission='S1', **kwargs)
# Setup SAR RT spectra list (Sentinel 1)
self.freq = 5.405
self.theta = np.deg2rad(37.0)
self.stype = 'turbid_rayleigh'
# self.stype='turbid_isotropic'
self.surf = 'Oh92'
# self.surf = 'Dubois95'
self.models = {'surface': self.surf, 'canopy': self.stype}
self.s = s # 0.02
self.lai_coef = lai_coef # 0.1
ke = 1.
self.eps = 15. - 0.j
# canopy = sense_canopy.OneLayer(ke_h=ke, ke_v=ke, d=0.1*self.state_lst[x].can_height, ks_h=omega * ke, ks_v=omega * ke)
omega = omega # 0.12
self.SAR_list = [
sense_mod.SingleScatRT(
#surface=sense_soil.Soil(mv=self.state_lst[x].soil_moisture, f=self.freq, s=self.s-0.01*(self.state_lst[x].lai / 4.), clay=0.23, sand=0.27),
surface=sense_soil.Soil(mv=self.state_lst[x].soil_moisture /
100,
f=self.freq,
s=s,
clay=0.23,
sand=0.27,
bulk=1.65),
#surface=sense_soil.Soil(eps=self.eps*(1. + 0.01*(self.state_lst[x].soil_moisture/0.45)), f=self.freq, s=self.s-0.01*(self.state_lst[x].lai / 4.)),
#canopy=sense_canopy.OneLayer(ke_h=1-self.lai_coef*self.state_lst[x].lai,
# ke_v=1-self.lai_coef*self.state_lst[x].lai,
# d=self.state_lst[x].can_height,
# ks_h=omega * (1-self.lai_coef * self.state_lst[x].lai),
# ks_v=omega * (1-self.lai_coef * self.state_lst[x].lai)),
canopy=sense_canopy.OneLayer(
ke_h=self.lai_coef * np.sqrt(
self.state_lst[x].lai / self.state_lst[x].can_height),
ke_v=self.lai_coef * np.sqrt(
self.state_lst[x].lai / self.state_lst[x].can_height),
d=self.state_lst[x].can_height,
ks_h=omega * (self.lai_coef * np.sqrt(
self.state_lst[x].lai / self.state_lst[x].can_height)),
ks_v=omega *
(self.lai_coef * np.sqrt(self.state_lst[x].lai /
self.state_lst[x].can_height))),
models=self.models,
theta=self.theta,
freq=self.freq) for x in xrange(len(self.state_lst))
]
for s in self.SAR_list:
s.sigma0()
self.backscatter_keys = ['vv', 'hh', 'hv']
def run_sense(soil_m, lai, can_height, vza, freq=5.405, stype='turbid_rayleigh', surf='Oh92', s=0.015, lai_coef=0.1,
omega=0.1):
"""
Function that runs the SenSE SAR ScattEring model given some inputs
:param soil_m: soil moisture (m3 m-3)
:type soil_m: float
:param lai: leaf area index (m2 m-2)
:type lai: float
:param can_height: canopy height (m)
:type can_height: float
:param vza: view zenith angle of sensor (degrees)
:type vza: float
:param freq: frequency (GHz)
:type freq: float
:param stype: canopy scattering model
:type stype: str
:param surf: surface scattering model
:type surf: str
:param s: surface rms height (m)
:type s: float
:param lai_coef: coefficient of lai for which to calculate extinction and volume scattering coefficients
:type lai_coef: float
:param omega: coefficient for calculation of volume scattering coefficients
:type omega: float
:return: instance of sense SingleScatRT class
"""
"""Function that runs sense model, given surface biogeophysical variables and viewing geometries
"""
models = {'surface': surf, 'canopy': stype}
SAR = sense_mod.SingleScatRT(
surface=sense_soil.Soil(mv=soil_m, f=freq, s=s, clay=0.23, sand=0.27),
# surface=sense_soil.Soil(eps=self.eps, f=self.freq, s=self.s),
canopy=sense_canopy.OneLayer(ke_h=lai_coef * lai, # extinction coefficient
ke_v=lai_coef * lai,
d=can_height,
ks_h=omega * (lai_coef * lai), # volume scattering coeff
ks_v=omega * (lai_coef * lai)),
models=models,
theta=np.deg2rad(vza),
freq=freq)
SAR.sigma0()
return SAR
def __init__(self, **kwargs):
"""
Initialize with same arguemnts as superClass 'Simulator'.
"""
super(S1_simulator, self).__init__(**kwargs)
# Setup SAR RT spectra list (Sentinel 1)
self.freq = 5.
self.theta = np.deg2rad(50)
self.stype = 'turbid_rayleigh'
# stype='turbid_isotropic'
self.models = {'surface': 'Oh92', 'canopy': self.stype}
self.s = 0.02
d = 0.22
ke = 1.
# canopy = sense_canopy.OneLayer(ke_h=ke, ke_v=ke, d=0.1*self.state_lst[x].can_height, ks_h=omega * ke, ks_v=omega * ke)
omega = 0.12
self.SAR_list = [
sense_mod.SingleScatRT(
surface=sense_soil.Soil(mv=self.state_lst[x].soil_moisture,
f=self.freq,
s=self.s,
clay=0.23,
sand=0.27),
canopy=sense_canopy.OneLayer(
ke_h=0.1 * self.state_lst[x].lai,
ke_v=0.1 * self.state_lst[x].lai,
d=0.1 * self.state_lst[x].can_height,
ks_h=omega * 0.1 * self.state_lst[x].lai,
ks_v=omega * 0.1 * self.state_lst[x].lai),
models=self.models,
theta=self.theta,
freq=self.freq) for x in xrange(len(self.state_lst))
]
for s in self.SAR_list:
s.sigma0()
self.backscatter_keys = ['vv', 'hh', 'hv']