Leaf and canopy radiative transfer modeling tools built on PROSPECT-D and SAIL.
🌳 xleaf is a python package for running leaf and canopy simulation models using PROSAIL. It provides python bindings to the PROSPECT-D & 4SAIL Fortran code.
🌿 It includes sensible defaults that make it easy to get up and running quickly, and clear code documentation in the form of docstrings and type hints.
📚 All credit for the fundamental modeling code and for the underlying science belongs to the original researchers. xleaf is mostly a wrapper. Please cite their most recent research:
@article{feret2017prospect,
title={PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle},
author={Feret, J-B and Gitelson, AA and Noble, SD and Jacquemoud, S},
journal={Remote Sensing of Environment},
volume={193},
pages={204--215},
year={2017},
publisher={Elsevier}
}
🧙 Shout out to my man JB.
pip install xleaf
🖥️ Depending on your OS, you may need a FORTRAN compiler. So on ubuntu you could run sudo apt install gcc. On macos you'd run brew install gcc.
import xleaf
import matplotlib.pyplot as plt
# run with off-the-shelf defaults
leaf = xleaf.simulate_leaf()
# or specify detailed parameters
canopy = xleaf.simulate_canopy(
chl = 40, # ug/cm2
car = 8, # ug/cm2
antho = 0.5, # ug/cm2
ewt = 0.01, # cm
lma = 0.009, # g/cm2
N = 1.5, # unitless
lai = 3.0, # m2/m2
lidf = 30, # degrees
soil_dryness = 0.75, # %
solar_zenith = 35, # degrees
solar_azimuth = 120, # degrees
view_zenith = 0, # degrees
view_azimuth = 60, # degrees
hot_spot = 0.01, # unitless
)
# and plot them together
plt.plot(xleaf.wavelengths, leaf, label='leaf')
plt.plot(xleaf.wavelengths, canopy, label='canopy')
plt.legend()📄 The definitions and expected range of values for each parameter are described in the xleaf docstrings.
📊 xleaf provides classes for generating random parameters within the global range of expected values. These classes have a .sample() method for generating an appropriate random value based on a literature review.
import xleaf
import matplotlib.pyplot as plt
# generate 5 random leaf spectra from global defaults
for idx in range(5):
chl = xleaf.ChlorophyllSampler.sample()
car = xleaf.CarotenoidSampler.sample()
antho = xleaf.AnthocyaninSampler.sample()
ewt = xleaf.EWTSampler.sample()
lma = xleaf.LMASampler.sample()
N = xleaf.NSampler.sample()
leaf = xleaf.simulate_leaf(chl, car, antho, ewt, lma, N)
plt.plot(xleaf.wavelengths, leaf, label=f"leaf {idx+1}")
plt.legend()🧪 Or experiment by setting the range of values yourself:
import xleaf
import matplotlib.pyplot as plt
MyLAISampler = xleaf.UniformSampler(min=2, max=6)
MyVZASampler = xleaf.NormalSampler(mean=0, stdv=3, min=-10, max=10)
# generate 5 random canopy spectra just varying LAI/VZA
for idx in range(5):
lai = MyLAISampler.sample()
vza = MyVZASampler.sample()
canopy = xleaf.simulate_canopy(lai=lai, view_zenith=vza)
plt.plot(xleaf.wavelengths, canopy, label=f"lai: {lai:0.2f}, vza: {vza:0.2f}")
plt.legend()⚡ These parameters don't always vary independently. Try to exercise caution when constructing parameter estimates to ensure biological realism.
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