def active_microwave_rt(state, geom):
"""
Function that simulates SAR backscattering, given surface biogeophysical variables and viewing geometries
:param state: instance of StateVector class
:type state: object
:param geom: instance of SensorGeometry class
:type geom: object
:return: instance of BackScatter class
"""
backscat = bs.BackScatter()
backscat.date_sat_ob = []
backscat.date_land_ob = []
backscat.soil_moisture = []
backscat.lai = []
backscat.can_height = []
backscat.hh = []
backscat.hv = []
backscat.vv = []
for date_utc in enumerate(geom.date_utc):
idx, timedelt = sv.find_nearest_date_idx(state.date_utc, date_utc[1])
SAR = run_sense(state.soil_moisture[idx], state.lai[idx], state.can_height[idx], geom.vza[date_utc[0]])
backscat.date_sat_ob.append(date_utc[1])
backscat.date_land_ob.append(state.date_utc[idx])
backscat.soil_moisture.append(state.soil_moisture[idx])
backscat.lai.append(state.lai[idx])
backscat.can_height.append(state.can_height[idx])
backscat.hh.append(10 * np.log10(SAR.__dict__['stot']['hh']))
backscat.hv.append(10 * np.log10(SAR.__dict__['stot']['hv']))
backscat.vv.append(10 * np.log10(SAR.__dict__['stot']['vv']))
return backscat
def passive_optical_rt(state,
geom,
mode='fast',
rsl1=0.2,
sm_coeff=0.5,
cab=75.0,
cw=0.01):
"""Function that simulates reflectances given surface biogeophysical variables and viewing geometries.
:param state: Instance of the StateVector class.
:type state: instance
:param geom: Instance of the SensorGeometry class.
:type geom: instance
:param mode: Run semiDiscrete in either fast ('fast') or slow ('slow') mode [optional].
:type resln: str
:param rsl1: weight of the first soil vector
:type rsl1: float
:param sm_coeff: weighting of soil moisture impact, bound between (0,1)
:type sm_coeff: float
:param cab: leaf chlorophyl concentration
:type cab: float
:param cw: equivelant leaf water thickness
:type cw: float
:return: Instance of the spectra class.
:rtype: instance
"""
spect = sp.spectra()
spect.date_sat_ob = []
spect.date_land_ob = []
spect.soil_moisture = []
spect.lai = []
spect.can_height = []
spect.refl = []
for date_utc in enumerate(geom.date_utc):
idx, timedelt = sv.find_nearest_date_idx(state.date_utc, date_utc[1])
reflectance = run_semidiscrete(state.soil_moisture[idx],
state.lai[idx],
state.can_height[idx],
geom.vza[date_utc[0]],
geom.vaa[date_utc[0]],
geom.sza[date_utc[0]],
geom.saa[date_utc[0]],
mode=mode,
rsl1=rsl1,
sm_coeff=sm_coeff,
cab=cab,
cw=cw)
spect.date_sat_ob.append(date_utc[1])
spect.date_land_ob.append(state.date_utc[idx])
spect.soil_moisture.append(state.soil_moisture[idx])
spect.lai.append(state.lai[idx])
spect.can_height.append(state.can_height[idx])
spect.refl.append(reflectance)
return spect
def passive_optical_rt(state, geom):
"""A python wrapper to the SemiDiscrete optical
canopy RT model of Nadine Gobron. Runs the
model for the the whole of its valid spectra
range at a resolution set by resln.
:param state: Instance of the StateVector class.
:type state: instance
:param geom: Instance of the SensorGeometry class.
:type geom: instance
:param mode: Run semiDiscrete in either fast ('fast') or slow ('slow') mode [optional].
:type resln: str
:return: Instance of the spectra class.
:rtype: instance
"""
spect = sp.spectra()
spect.date_sat_ob = []
spect.date_land_ob = []
spect.soil_moisture = []
spect.lai = []
spect.can_height = []
spect.refl = []
for date_utc in enumerate(geom.date_utc):
idx, timedelt = sv.find_nearest_date_idx(state.date_utc, date_utc[1])
reflectance = run_semidiscrete(state.soil_moisture[idx],
state.lai[idx], state.can_height[idx],
geom.vza[date_utc[0]],
geom.vaa[date_utc[0]],
geom.sza[date_utc[0]],
geom.saa[date_utc[0]])
spect.date_sat_ob.append(date_utc[1])
spect.date_land_ob.append(state.date_utc[idx])
spect.soil_moisture.append(state.soil_moisture[idx])
spect.lai.append(state.lai[idx])
spect.can_height.append(state.can_height[idx])
spect.refl.append(reflectance)
return spect
def active_microwave_rt(state,
geom,
freq=5.405,
s=0.015,
lai_coeff=0.1,
omega=0.1,
clay=0.23,
sand=0.27,
bulk=1.65):
"""
Function that simulates SAR backscattering, given surface biogeophysical variables and viewing geometries
:param state: instance of StateVector class
:type state: object
:param geom: instance of SensorGeometry class
:type geom: object
:param freq: frequency (GHz)
:type freq: float
:param s: surface rms height (m)
:type s: float
:param lai_coeff: coefficient of lai for which to calculate extinction and volume scattering coefficients
:type lai_coeff: float
:param omega: coefficient for calculation of volume scattering coefficients
:type omega: float
:param clay: soil texture clay fraction
:type clay: float
:param sand: soil texture sand fraction
:type sand: float
:param bulk: soil bulk density (g cm-3)
:type bulk: float
:return: instance of BackScatter class
"""
backscat = bs.BackScatter()
backscat.date_sat_ob = []
backscat.date_land_ob = []
backscat.soil_moisture = []
backscat.lai = []
backscat.can_height = []
backscat.hh = []
backscat.hv = []
backscat.vv = []
for date_utc in enumerate(geom.date_utc):
idx, timedelt = sv.find_nearest_date_idx(state.date_utc, date_utc[1])
SAR = run_sense(state.soil_moisture[idx],
state.lai[idx],
state.can_height[idx],
geom.vza[date_utc[0]],
freq=freq,
s=s,
lai_coeff=lai_coeff,
omega=omega,
clay=clay,
sand=sand,
bulk=bulk)
backscat.date_sat_ob.append(date_utc[1])
backscat.date_land_ob.append(state.date_utc[idx])
backscat.soil_moisture.append(state.soil_moisture[idx])
backscat.lai.append(state.lai[idx])
backscat.can_height.append(state.can_height[idx])
backscat.hh.append(10 * np.log10(SAR.__dict__['stot']['hh']))
backscat.hv.append(10 * np.log10(SAR.__dict__['stot']['hv']))
backscat.vv.append(10 * np.log10(SAR.__dict__['stot']['vv']))
return backscat