RAMI4ATM phase
Lambertian surfaces are defined by one parameter, which is the magnitude of surface reflectance or BRF (unitless).
The spectral reflectance values for the lambertian model (LAM) has been obtained by applying a spectral convolution of the MSI spectral responses, the extraterrestrial solar spectrum and an isotropic reflectance spectrum of understorey vegetation.
This parameter depends on the spectral band used for the measurement. In addition to LAM values, black (BLA) and white (WHI) surface scenarios have been defined. These later ones are simulated using a Lambertian surface with a reflectance of 0 and 1 respectively.
The corresponding JSON dotted notations is:
scenario.observations.surface.surface_parameters.reflectance
The description of RPV model (Rahman et al, 1993) was already given in previous phases and is available in the Definition section.
The RPV surface is defined using its 3 parameters $\rho_0$, $k$ and $\Theta$.
These values describe the anisotropy reflectance of understorey vegetation similar to that used in HOM25 of RAMI-V phase and recalculated for MSI bands.
To define RPV parameters in MSI bands, an approach similar to that used in RAMI-V for HOM25 scene has been adopted. While $k$ and $\Theta$ have been defined a-priori, the $\rho_0$ parameter was varied to obtain, by bi-hemispherical integration of the RPV function, the same values of the LAM model, with a uncertainty of less than 1E-4 (bisection method was used to identify $\rho_0$).
Table 2 lists the spectral parametrs for the RPV model.
scenario.observations.surface.surface_parameters.k scenario.observations.surface.surface_parameters.theta scenario.observations.surface.surface_parameters.rho_0
The description of Ross-Li model (Strahler et al, 1999) is given in the Definition section.
The Ross-Thick Li-Sparse Reciprocal model is used to define a second anysotropical surface in RAMI4ATM using the associated $f_{iso}$, $f_{vol}$ and $f_{geo}$ parameters.
The RLI parameters presented in Table 2 were obtained by fitting the Ross-Li function over an array of BRF calculated by using the RPV model defined in [Table 2].
scenario.observations.surface.surface_parameters.f_iso scenario.observations.surface.surface_parameters.f_vol scenario.observations.surface.surface_parameters.f_geo
This phase includes three homogeneous canopies with different Leaf Area Distributions (LAD) and an isotropic background.
All scenes share the same structural parameters in terms of extension (height and width), lead shape and dimension, leaf area index (LAI), leaf optical properties (R, T), except the LAD which is assumed to be Planophile (HOM25_LAM), erectophile (HOM35_LAM) and uniform (HOM45_LAM).
The background surface to be used under the vegetation component is lambertian and have the same optical characteristics of the HOM00_LAM model described above.
The structural properties of the scenes are summarized in [Table 4]. The LAD can be defined using Bunnik (1978) or Goel and Strebel (1984) distribution functions as defined here. The correlation between the distribution functions defined by Bunnik and those of Goel and Strebel parameterized with the $\mu$ and $\nu$ as given in the table, are of the order of 0.9989 for both the planophile and erectophile cases.
Three files are also given providing a deterministic way to define the canopies. Their columns contain the following informations for each leaf of the canopy: 1) the leaf radius (fixed to 0.05m), three columns with XYZ positions of the leaf centers in the scene (given in meters), and the direction cosines ($D_x$,$D_y$,$D_z$) of the angles formed by the leaf normal and the XYZ axes. The distributions of $\theta_Z$ characterizes the three canopies with different LAD as already discussed.
scenario.observations.canopy.leaf_radius scenario.observations.canopy.leaf_area_index scenario.observations.canopy.height scenario.observations.canopy.distribution_type scenario.observations.canopy.distribution_mu scenario.observations.canopy.distribution_nu
Table 5 lists the reflectance and transmittance of the leaves for the six MSI spectral bands. They were calculated using [Equation 1]. The leaves are assumed to be flat object which bi-lambertian scattering properties.
scenario.observations.canopy.canopy_parameters.reflectance scenario.observations.canopy.canopy_parameters.transmittance
Three main elements are combined to create complex atmospheres in RAMI4ATM. These are molecular scattering, molecular absorption, and aerosols.
An atmosphere type is therefore associated with each of the 7 possible combinations as shown in Figure 6:
Each atmosphere component has its own set of parameters with some variations. This means that there are multiple scenarios per atmosphere type.
The atmosphere is assumed plane-parallel.
An atmosphere type is therefore associated with each of the 7 possible combinations:
The corresponding JSON dotted notation is
scenario.observations.atmosphere.atmosphere_type
The Rayleigh scattering atmosphere component only specifies the activation of molecular scattering in the atmosphere, by the different species described in the atmospheric profile.
This is the simplest atmosphere component, and it does not require any additional parameters.
In that scenario, no molecular absorption is accounted for.
The presence of the Absorption component enables molecular absorption in the scenario's atmosphere.
Model and Expert users may use the AFGL US-standard atmospheric profile option in their code with the possibility to rescale water vapour and ozone.
Only the "US standard" profile described in AFGL US-standard atmospheric profile is used in RAMI4ATM. This atmospheric profile details the molecular concentration of 28 species at different altitudes. For RAMI4ATM only the following 7 principal species are considered: H2O, CO2, O3, N2O, CO, CH4, O2.
This profile is available in most atmospheric models and is considered a standard in the field. It is recommended to use the provided atmospheric profile dataset. However, participants who already have their own definition of this profile in their model can use their own.
Various implementations of the AFGL US-standard atmospheric profile profile exist in radiative transfer models, accounting for the absorption of different molelcules.
Model users are encouraged to use the implementation shipped with their radiative transfer model. Expert users can explicitely define the AFGL US-standard atmospheric profile.
The atmospheric profile data file includes 11 columns separated by whitespace. This data file provide the values of different atmospheric variables as a function of altitude. From left to right, these columns are:
In addition to the standard profile, atmospheric profiles with higher and lower concentrations of either water vapour and/or ozone are considered, to create the six variants of the molecular absorption block as described in [Table 7]. While the experiments for the standard values of $H_2O$ and $O_3$ should be performed in all the six S2-MSI spectral bands, those relevant to rescaled water vapour should be performed only in band M12, and those relevant to rescaled values of ozone only in M03, as indicated in [Table 7].
The corresponding JSON dotted notation for H2O and O3 rescaling is
scenario.observations.atmosphere.concentrations.H2O scenario.observations.atmosphere.concentrations.O3
A uniform aerosol layer may be present in the scenario's atmosphere.
There are two different aerosol types in RAMI4ATM:
Desert dust particles are larger than the continental ones.
Each dataset may be used with a set of two different values for the optical thickness specified at 550nm.
There is only one possible vertical distribution for the aerosols: a single uniform layer of 2km high starting from the earth's surface.
The table shows the list of aerosol optical thickness values.
The corresponding JSON dotted notations are:
scenario.observations.atmosphere.aerosols.tau_550
Model users may use the Continental aerosol radiative properties and Desert aerosol radiative properties types shipped with their radiative transfer model. Expert users are provided with two options:
The microphysical properties of the aerosol particles are described by a refractive index data file and a particle radius distribution assuming spherical particles.
The refractive index data for the continental and desert particles are provided by the Continental aerosol refractive properties and Desert aerosol refractive properties data files, respectively.
The format of the refractive index data files is the following:
columns are separated by whitespacewhere the real and imaginary parts of the complex refractive index, a and b, respectively, are such that:
where $i$ is the imaginary number.
For each aerosol type, i.e. desert and continental, the aerosol particles population is divided into two groups: fine and coarse particles. For each group, the particle radius follows a lognormal particle radius distribution. The probability density function for the lognormal particle radius distribution is given by the following equation:
where $r_m$ and $\sigma$ are two parameters. $r_m$ defines the median radius of the radius distribution. The natural logarithm of $\sigma$ represents the standard deviation of the natural logarithm of the radius r. The particle radius distribution of the entire particles population is specified as the linear combination of the two individual particle radius distributions:
The $r_m$ and $\sigma$ parameters, as well as the linear combination weights, are specificed for each aerosol type, i.e. desert and continental, in the table below:
scenario.observations.atmosphere.aerosols.type.radiative_properties_dataset_name
The Mie theory has been used to calculate aerosol particles radiative properties. The microphysical properties of the aerosol particles are described by a refractive index data file and a particle radius distribution.
The radiative properties of the desert and continental particles are provided by the Desert aerosol radiative properties and Continental aerosol radiative properties data files, respectively. The format of these data files is the following:
The spectral variability of the aerosol single scattering properties should be estimated at any wavelength at which the radiative transfer equation is solved assuming a linear interpolation from wavelengths at wich the values of the refractive indices are provided.
Both fine and coarse particles are associated to the same refractive index data. Mie calculations are performed using 1000 particle radius values geometrically spaced between 0.001 and 100.0 micrometers.
