3.1.2 Radiometric corrections
22.214.171.124 fAPAR estimation and atmospheric correction (Gobron et al., 2006)
This procedure, published by Gobron et al. (2006), uses the TOA-reflectances in the BLUE, RED and NIR bands (RaB, RaR, RaN) and combines the atmospheric correction and fAPAR-extraction as follows (the functions Fn and band-specific parameter sets Pni are explained later):
* BRDF-correction with the RPV-model: First the directional Rai in the three bands i are converted into more standardized hemispherical reflectances ρai via the equation ρai =F1(Rai, θs, Φs, θv, Φv, P1i) which also uses the known angles and the band-specific parameter sets P1i. * Atmospheric correction: Next, "rectified" surface reflectances are computed for RED (ρsR) and NIR (ρsN) with the following equations: ρsR = F2R(ρaB, ρaR, P2R) and ρsN = F2N(ρaB, ρaN, P2N), where P2R/N are band-specific parameter sets. The underlying idea is that the radiative state of the atmosphere can be assessed from the reflectance differences between the BLUE and the other bands (the BLUE is indeed very susceptible for atmospheric scattering). * Finally fAPAR is estimated from the rectified reflectances and parameter set P3, via fAPAR= F3(ρsR, ρsN, P3).
To define the specific functions Fn and parameter sets Pni, Gobron et al. (2006) coupled three simulation models: 6S links the satellite-registered radiance La with the surface reflectance ρs, the latter is simulated via Gobron's Semi-Discrete canopy reflectance model (SDRM), while the leaf optical properties (one of the SDRM-inputs) are predicted by PROSPECT. All three models require additional information, such as the leaf chlorophyl content (PROSPECT), the LAI (SDRM) and the atmospheric aerosol content (6S). A multitude of "scenario's", i.e. combinations of all these external parameters, is defined in advance. All together they should represent the variability observed in the real world (all vegetation types x atmospheres x viewing conditions ...). The coupled model is then run to compute for each scenario a number of outputs, which also include fAPAR (via the adapted SDRM) and the mentioned intermediate reflectances (BRDF-corrected ρaB/ρaR/ρaN and rectified ρsR/ρsN). The functions Fn and parameter sets Pni are then defined by statistical analysis of the scenario database. De facto, the functions Fn are ratio's of polynomials and the parameter sets Pni contain their coefficients.
Compared to SMAC, the combined approach of Gobron et al. (2006) does not need ancillary information on the distribution of ozone, water vapour and aerosols. But the method is only applicable for sensors such as VGT and MODIS which (in addition to the omni-present RED and NIR) also have a BLUE band. This band lacks for AVHRR, for which another method to derive fAPAR is used (see fAPAR (Baret et al., 2007)).
Go back to SPOT-VEGETATION or TERRA-MODIS pre-processing.
126.96.36.199 Land Surface temperature (LST)
For the determination of the land surface temperature the split window method of Coll & Caselles (1997) is implemented. The brightness temperatures from channel 4 (TIR4) and channel 5 (TIR5) of AVHRR are combined with the surface emissivity (correcting for grey bodies) and the atmospheric water content is taken into account. The surface emissivity is derived from the vegetation cover fraction, in this case derived from the NDVI with the following method.
Method to calculate the vegetation cover fraction:
The emissivity (ε, -) is calculated according to Valor and Caselles (1996) and Rubio et al. (1997).
The difference of emissivity (∆ε) as a function of the vegetation cover fraction (Pv) is calculated as:
Corrections of the atmospheric influence on the brightness temperature:
Finally the land surface temperature (LST) is determined according to the split-window principle (Coll and Caselles, 1997)
Parameters and their default values:
Additional details can be found in Coll and Caselles,1997; Rubio et al., 1997 and Valor and Caselles, 1996.