def main(sza_file, biophysical_file, min_frac_green, output_file):
# Read the required data
fapar, geo_coding = su.read_snappy_product(biophysical_file, 'fapar')
fapar = fapar.astype(np.float32)
lai = su.read_snappy_product(biophysical_file, 'lai')[0].astype(np.float32)
sza = su.read_snappy_product(sza_file, 'sun_zenith')[0].astype(np.float32)
# Calculate fraction of vegetation which is green
f_g = np.ones(lai.shape, np.float32)
# Iterate until f_g converges
converged = np.zeros(lai.shape, dtype=bool)
# For pixels where LAI or FAPAR are below tolerance threshold of the S2 biophysical
# processor, assume that the soil is bare and f_g = 1
converged[np.logical_or(lai <= 0.2, fapar <= 0.1)] = True
for c in range(50):
f_g_old = f_g.copy()
fipar = TSEB.calc_F_theta_campbell(sza[~converged],
lai[~converged]/f_g[~converged],
w_C=1, Omega0=1, x_LAD=1)
f_g[~converged] = fapar[~converged] / fipar
f_g = np.clip(f_g, min_frac_green, 1.)
converged = np.logical_or(np.isnan(f_g), np.abs(f_g - f_g_old) < 0.02)
if np.all(converged):
break
su.write_snappy_product(output_file, [{'band_name': 'frac_green', 'band_data': f_g}],
'fracGreen', geo_coding)
def fg_from_fapar(fapar, LAI, sza, f_c = 1, x_LAD = 1):
# Sentinel 2 BioPhysical product estimates FPAR assuing an homogeneous canopy
# Omega0 = 1 for coherence between fapar and fipar
fipar = tseb.calc_F_theta_campbell(sza,
LAI,
w_C = 1,
Omega0 = 1,
x_LAD = x_LAD)
f_g = fapar / fipar
f_g = np.clip(f_g, 0., 1.)
return f_g
def ESVEP(Tr_K,
vza,
T_A_K,
u,
ea,
p,
Sn_C,
Sn_S,
L_dn,
LAI,
emis_C,
emis_S,
z_0M,
d_0,
z_u,
z_T,
z0_soil=0.001,
x_LAD=1,
f_c=1.0,
f_g=1.0,
w_C=1.0,
calcG_params=[[1], 0.35],
UseL=False):
'''ESVEP
Calculates soil and vegetation energy fluxes using Soil and Vegetation Energy Patrtitioning
`ESVEP` model and a single observation of composite radiometric temperature.
Parameters
----------
Tr_K : float
Radiometric composite temperature (Kelvin).
vza : float
View Zenith Angle (degrees).
T_A_K : float
Air temperature (Kelvin).
u : float
Wind speed above the canopy (m s-1).
ea : float
Water vapour pressure above the canopy (mb).
p : float
Atmospheric pressure (mb), use 1013 mb by default.
Sn_C : float
Canopy net shortwave radiation (W m-2).
Sn_S : float
Soil net shortwave radiation (W m-2).
L_dn : float
Downwelling longwave radiation (W m-2).
LAI : float
Effective Leaf Area Index (m2 m-2).
emis_C : float
Leaf emissivity.
emis_S : flaot
Soil emissivity.
z_0M : float
Aerodynamic surface roughness length for momentum transfer (m).
d_0 : float
Zero-plane displacement height (m).
z_u : float
Height of measurement of windspeed (m).
z_T : float
Height of measurement of air temperature (m).
z0_soil : float, optional
bare soil aerodynamic roughness length (m).
x_LAD : float, optional
Campbell 1990 leaf inclination distribution function chi parameter.
f_c : float, optional
Fractional cover.
f_g : float, optional
Fraction of vegetation that is green.
w_C : float, optional
Canopy width to height ratio.
calcG_params : list[list,float or array], optional
Method to calculate soil heat flux,parameters.
* [[1],G_ratio]: default, estimate G as a ratio of Rn_S, default Gratio=0.35.
* [[0],G_constant] : Use a constant G, usually use 0 to ignore the computation of G.
* [[2,Amplitude,phase_shift,shape],time] : estimate G from Santanello and Friedl with G_param list of parameters (see :func:`~TSEB.calc_G_time_diff`).
UseL : float or None, optional
If included, its value will be used to force the Moning-Obukhov stability length.
Returns
-------
flag : int
Quality flag, see Appendix for description.
T_S : float
Soil temperature (Kelvin).
T_C : float
Canopy temperature (Kelvin).
T_sd: float
End-member temperature of dry soil (Kelvin)
T_vd: float
End-member temperature of dry vegetation (Kelvin)
T_sw: float
End-member temperature of saturated soil (Kelvin)
T_vw: float
End-member temperature of well-watered vegetation (Kelvin)
T_star: float
Critical surface temperature (Kelvin)
L_nS : float
Soil net longwave radiation (W m-2)
L_nC : float
Canopy net longwave radiation (W m-2)
LE_C : float
Canopy latent heat flux (W m-2).
H_C : float
Canopy sensible heat flux (W m-2).
LE_S : float
Soil latent heat flux (W m-2).
H_S : float
Soil sensible heat flux (W m-2).
G : float
Soil heat flux (W m-2).
r_vw: float
Canopy resistance to heat transport of well-watered vegetation (s m-1)
r_vd: float
Canopy resistance to heat transport of vegetation with zero soil water avaiability (s m-1)
r_av: float
Aerodynamic resistance to heat transport of the vegetation (s m-1)
r_as: float
Aerodynamic resistance to heat transport of the soil (s m-1)
L : float
Monin-Obuhkov length (m).
n_iterations : int
number of iterations until convergence of L.
References
----------
.. [Tang2017] Tang, R., and Z. L. Li. An End-Member-Based Two-Source Approach for Estimating
Land Surface Evapotranspiration From Remote Sensing Data. IEEE Transactions on Geoscience
and Remote Sensing 55, no. 10 (October 2017): 5818–32.
https://doi.org/10.1109/TGRS.2017.2715361.
'''
# Convert input float scalars to arrays and check parameters size
Tr_K = np.asarray(Tr_K)
(vza, T_A_K, u, ea, p, Sn_C, Sn_S, L_dn, LAI, emis_C, emis_S, z_0M, d_0,
z_u, z_T, z0_soil, x_LAD, f_c, f_g, w_C,
calcG_array) = map(tseb._check_default_parameter_size, [
vza, T_A_K, u, ea, p, Sn_C, Sn_S, L_dn, LAI, emis_C, emis_S, z_0M,
d_0, z_u, z_T, z0_soil, x_LAD, f_c, f_g, w_C, calcG_params[1]
], [Tr_K] * 21)
# Create the output variables
[
flag, T_S, T_C, T_sd, T_vd, T_sw, T_vw, T_star, Ln_S, Ln_C, LE_C, H_C,
LE_S, H_S, G, r_vw, r_vd, r_av, r_as, iterations
] = [np.zeros(Tr_K.shape) + np.NaN for i in range(20)]
# iteration of the Monin-Obukhov length
if isinstance(UseL, bool):
# Initially assume stable atmospheric conditions and set variables for
L = np.asarray(np.zeros(T_S.shape) + np.inf)
max_iterations = ITERATIONS
else: # We force Monin-Obukhov lenght to the provided array/value
L = np.asarray(np.ones(T_S.shape) * UseL)
max_iterations = 1 # No iteration
# Calculate the general parameters
rho = met.calc_rho(p, ea, T_A_K) # Air density
c_p = met.calc_c_p(p, ea) # Heat capacity of air
z_0H = res.calc_z_0H(z_0M, kB=kB) # Roughness length for heat transport
z_0H_soil = res.calc_z_0H(z0_soil,
kB=kB) # Roughness length for heat transport
s = met.calc_delta_vapor_pressure(
T_A_K) * 10 # slope of the saturation pressure curve (mb C-1)
lbd = met.calc_lambda(T_A_K) # latent heat of vaporisation (MJ./kg)
gama = met.calc_psicr(p, lbd) # psychrometric constant (mb C-1)
vpd = met.calc_vapor_pressure(T_A_K) - ea # vapor pressure deficit (mb)
# Calculate LAI dependent parameters for dataset where LAI > 0
omega0 = CI.calc_omega0_Kustas(LAI, f_c, x_LAD=x_LAD, isLAIeff=True)
F = np.asarray(LAI / f_c) # Real LAI
# Fraction of vegetation observed by the sensor
f_theta = tseb.calc_F_theta_campbell(vza,
F,
w_C=w_C,
Omega0=omega0,
x_LAD=x_LAD)
# Initially assume stable atmospheric conditions and set variables for
# iteration of the Monin-Obukhov length
u_friction = MO.calc_u_star(u, z_u, L, d_0, z_0M)
u_friction = np.asarray(np.maximum(u_friction_min, u_friction))
u_friction_s = MO.calc_u_star(u, z_u, L, np.zeros(d_0.shape), z0_soil)
u_friction_s = np.asarray(np.maximum(u_friction_min, u_friction_s))
L_old = np.ones(Tr_K.shape)
L_diff = np.asarray(np.ones(Tr_K.shape) * float('inf'))
# First assume that canopy temperature equals the minumum of Air or
# radiometric T
T_C = np.asarray(np.minimum(Tr_K, T_A_K))
flag, T_S = tseb.calc_T_S(Tr_K, T_C, f_theta)
# Loop for estimating stability.
# Stops when difference in consecutives L is below a given threshold
for n_iterations in range(max_iterations):
i = flag != 255
if np.all(L_diff[i] < L_thres):
if L_diff[i].size == 0:
print("Finished iterations with no valid solution")
else:
print("Finished interations with a max. L diff: " +
str(np.max(L_diff[i])))
break
i = np.logical_and(L_diff >= L_thres, flag != 255)
print("Iteration " + str(n_iterations) + ", max. L diff: " +
str(np.max(L_diff[i])))
iterations[i] = n_iterations
# Calculate net longwave radiation with current values of T_C and T_S
Ln_C[i], Ln_S[i] = rad.calc_L_n_Kustas(T_C[i], T_S[i], L_dn[i], LAI[i],
emis_C[i], emis_S[i])
Rn_C = Sn_C + Ln_C
Rn_S = Sn_S + Ln_S
# Compute Soil Heat Flux
G[i] = tseb.calc_G([calcG_params[0], calcG_array], Rn_S, i)
# Calculate aerodynamic resistances
r_vw[i] = 100.0 / LAI[i]
r_vd[i] = np.zeros(LAI[i].shape) + 2000.0
r_av_params = {
"z_T": z_T[i],
"u_friction": u_friction[i],
"L": L[i],
"d_0": d_0[i],
"z_0H": z_0H[i]
}
r_av[i] = tseb.calc_resistances(tseb.KUSTAS_NORMAN_1999,
{"R_A": r_av_params})[0]
r_as_params = {
"z_T": z_T[i],
"u_friction": u_friction_s[i],
"L": L[i],
"d_0": np.zeros(d_0[i].shape),
"z_0H": z_0H_soil[i]
}
r_as[i] = tseb.calc_resistances(tseb.KUSTAS_NORMAN_1999,
{"R_A": r_as_params})[0]
# Estimate the surface temperatures of the end-members
# Eq 8a
T_sd[i] = r_as[i] * (Rn_S[i] - G[i]) / (rho[i] * c_p[i]) + T_A_K[i]
# Eq 8b
T_vd[i] = r_av[i] * Rn_C[i] / (rho[i] * c_p[i]) *\
gama[i] * (1 + r_vd[i] / r_av[i]) / (s[i] + gama[i] * (1 + r_vd[i] / r_av[i])) -\
vpd[i] / (s[i] + gama[i] * (1 + r_vd[i] / r_av[i])) + T_A_K[i]
# Eq 8c
T_sw[i] = r_as[i] * (Rn_S[i] - G[i]) / (rho[i] + c_p[i]) *\
gama[i] / (s[i] + gama[i]) - vpd[i] / (s[i] + gama[i]) + T_A_K[i]
# Eq 8d
T_vw[i] = r_av[i] * Rn_C[i] / (rho[i] * c_p[i]) *\
gama[i] * (1 + r_vw[i] / r_av[i]) / (s[i] + gama[i] * (1 + r_vw[i] / r_av[i])) -\
vpd[i] / (s[i] + gama[i] * (1 + r_vw[i] / r_av[i])) + T_A_K[i]
# Estimate critical surface temperature - eq 10
T_star[i] = (T_sd[i]**4 * (1 - f_theta[i]) +
T_vw[i]**4 * f_theta[i])**0.25
# Estimate latent heat fluxes when water in the top-soil is avaiable for evaporation
j = np.logical_and(Tr_K <= T_star, i)
# Eq 12a
T_C[j] = T_vw[j]
# Eq 12b
T_S[j] = ((Tr_K[j]**4 - f_theta[j] * T_C[j]**4) /
(1 - f_theta[j]))**0.25
# Eq 13a
LE_C[j] = (s[j] * Rn_C[j] + rho[j] * c_p[j] * vpd[j] / r_av[j]) /\
(s[j] + gama[j] * (1 + r_vw[j] / r_av[j]))
# Eq 13b
LE_S[j] = (T_sd[j] - T_S[j]) / (T_sd[j] - T_sw[j]) *\
((s[j] * (Rn_S[j] - G[j]) + rho[j] * c_p[j] * vpd[j] / r_as[j]) /
(s[j] + gama[j]))
# Estimate latent heat fluxes when no water in the top-soil is avaiable for evaporation
j = np.logical_and(Tr_K > T_star, i)
# Eq 14a
T_S[j] = T_sd[j]
# Eq 14b
T_C[j] = ((Tr_K[j]**4 - (1 - f_theta[j]) * T_S[j]**4) /
f_theta[j])**0.25
# Eq 15a
LE_C[j] = (T_vd[j] - T_C[j]) / (T_vd[j] - T_vw[j]) *\
((s[j] * Rn_C[j] + rho[j] * c_p[j] * vpd [j] / r_av[j]) /
(s[j] + gama[j] * (1 + r_vw[j] / r_av[j])))
# Eq 15b
LE_S[j] = 0
# Estimate sensible heat fluxes as residuals
H_C[i] = Rn_C[i] - LE_C[i]
H_S[i] = Rn_S[i] - G[i] - LE_S[i]
# Calculate total fluxes
H = np.asarray(H_C + H_S)
LE = np.asarray(LE_C + LE_S)
# Now L can be recalculated and the difference between iterations
# derived
if isinstance(UseL, bool):
L[i] = MO.calc_L(u_friction[i], T_A_K[i], rho[i], c_p[i], H[i],
LE[i])
L_diff = np.asarray(np.fabs(L - L_old) / np.fabs(L_old))
L_diff[np.isnan(L_diff)] = float('inf')
L_old = np.array(L)
L_old[L_old == 0] = 1e-36
# Calculate again the friction velocity with the new stability
# correctios
u_friction[i] = MO.calc_u_star(u[i], z_u[i], L[i], d_0[i], z_0M[i])
u_friction = np.asarray(np.maximum(u_friction_min, u_friction))
u_friction_s[i] = MO.calc_u_star(u[i], z_u[i], L[i],
np.zeros(d_0[i].shape),
np.zeros(z_0M[i].shape) + 0.005)
u_friction_s = np.asarray(np.maximum(u_friction_min, u_friction_s))
return [
flag, T_S, T_C, T_sd, T_vd, T_sw, T_vw, T_star, Ln_S, Ln_C, LE_C, H_C,
LE_S, H_S, G, r_vw, r_vd, r_av, r_as, L, n_iterations
]
