Table of Contents
PROSPECT-D simulates spectral leaf reflectance and transmittance from 400 to 2500 nm as a function of the leaf’s biochemical composition. The underlying physics treats the leaf as a stack of N absorbing and scattering layers separated by Fresnel interfaces.
| Parameter | Symbol | Unit | Description |
|---|---|---|---|
| Structure | N | – | Number of cell layers (1.0–3.6) |
| Chlorophyll a+b | Cab | µg cm⁻² | Primary photosynthetic pigment |
| Carotenoids | Car | µg cm⁻² | Accessory pigments; active in blue/green |
| Anthocyanins | Anth | µg cm⁻² | Red pigment; elevated in stressed leaves |
| Brown pigments | Cbrown | a.u. | Senescence marker |
| Water | Cw | cm | Equivalent water layer thickness |
| Dry matter | Cm | g cm⁻² | Structural carbon (cell walls, wax) |
Spectral signatures to watch for: - Green peak (~550 nm): driven by chlorophyll - Red edge (~700 nm): separates PAR-absorbing and NIR-reflecting regions - NIR plateau (700–1300 nm): cell structure scattering; insensitive to pigments - Water absorption bands (~970, ~1200, ~1450, ~1940 nm): driven by Cw
import de.grogra.rtmodelslibrary.*;
// Preset
double[][] lrt = ProspectD.compute(ProspectDPreset.CORN_FRESH);
// Custom values
double[][] lrt = ProspectD.compute(
1.5, // N
30.0, // Cab (µg/cm²)
10.0, // Car
1.0, // Anth
0.0, // Cbrown
0.015, // Cw (cm)
0.009 // Cm (g/cm²)
);
// Restrict to PAR range only
double[][] par = ProspectD.compute(ProspectDPreset.DEFAULT, 400, 700);
// Access result
double refl550 = lrt[150][1]; // reflectance at 550 nm (index = wavelength - 400)
double tran800 = lrt[400][2]; // transmittance at 800 nm
// Extract individual arrays (e.g. for IrregularSpectralCurve)
double[] wl = ProspectD.getWavelengths(lrt);
double[] refl = ProspectD.getReflectance(lrt);
double[] tran = ProspectD.getTransmittance(lrt);
// Build spectral curve directly from double arrays
IrregularSpectralCurve reflCurve = new IrregularSpectralCurve(wl, refl);
Feret, J.-B., Gitelson, A.A., Noble, S.D., Jacquemoud, S. (2017). PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle. Remote Sensing of Environment, 193:204–215. DOI: 10.1016/j.rse.2017.03.004