This repository contains the Python bindings to the PROSPECT and SAIL leaf and canopy reflectance models. The code is written in FORTRAN. The original fortran code was downloaded from Jussieu. I only added a function to simplify writing the wrappers, and you should go to that page to get newer versions of the code. A recent review of the use of both models is availabe in this paper.
The installation of the bindings is quite straightforward: unpack the distribution and run the following command
python setup.py install
You can usually install it to your user's directory (if you haven't got superuser privileges) by
python setup.py install --user
You will need a working FORTRAN compiler. I have only tested this with GCC on Linux, but it should work on other systems. You can also pass optimisation flags to the compiler:
python setup.py config_fc --fcompiler=gnu95 --arch=-march=native --opt=-O3 install --user
The bindings offer a single function, run_prosail.
retval = run_prosail(n,cab,car,cbrown,cw,cm,lai,lidfa,lidfb,psoil,hspot,tts,tto,psi)
The parameters that are fed into run_prosail are
| Parameter | Description of parameter | Units | Typical min | Typical max |
|---|---|---|---|---|
| N | Leaf structure parameter | N/A | 0.8 | 2.5 |
| cab | Chlorophyll a+b concentration | ug/cm2 | 0 | 200 |
| caw | Equivalent water thickiness | cm | 0 | 200 |
| car | Carotenoid concentration | ug/cm2 | 0 | 200 |
| cbrown | Brown pigment | NA | 0 | 1 |
| cm | Dry matter content | g/cm2 | 0 | 200 |
| lai | Leaf Area Index | N/A | 0.1 | 10 |
| lidfa | Leaf angle distribution | N/A | ||
| lidfb | Leaf angle distribution | N/A | ||
| psoil | Dry/Wet soil factor | N/A | 0 | 1 |
| hspot | Hotspot parameter | N/A | 0 | |
| tts | Solar zenith angle | deg | 0 | 90 |
| tto | Observer zenith angle | deg | 0 | 90 |
| phi | Relative azimuth angle | deg | 0 | 360 |
lidfa and lidfb parameters control the leaf angle distribution. Typical distributions
are given by the following parameter choices:
| LIDF type | lidfa | lidfb |
|---|---|---|
| Planophile | 1 | b |
| Erectophile | -1 | 0 |
| Plagiophile | 0 | -1 |
| Extremophile | 0 | 1 |
| Spherical | -0.35 | -0.15 |
| Uniform | 0 | 0 |
A simple example of using bindings would be
cm=0.009 cab = 80 car = 15 cbrown = 0 cw = 0.001 lidfa = 0 lidfb = 0 psoil = 0 hspot = 0.01 tts = 30 tto = 10 phi = 0 lai = np.arange ( 0, 5, 0.2 ) for l in lai:
plt.plot ( run_prosail(1.5,cab,car,cbrown,cw,cm,l,lidfa,lidfb,psoil,hspot,tts,tto,psi))
This results in a simulation of surface reflectance (from 400 to 2500 nm) as a function of LAI and under the other parameters' prescribed values. This yields the following
