View source: R/temperature_emissivity.R
| estimate_temperature | R Documentation |
estimate_temperature() applies an experimental, spectrally smooth
temperature-emissivity separation (TES) model to calibrated FTIR
thermal-emission radiance. The primary OpenSpecy method evaluates spectra
in bounded, BLAS-backed blocks and returns one compact row per input
spectrum. It does not create a full emissivity cube.
The input must contain calibrated, surface-leaving spectral radiance in
"W m^-2 sr^-1 (cm^-1)^-1". This is not suitable for absorbance,
transmittance, reflectance, normalized intensities, or detector counts.
estimate_temperature(x, ...)
## Default S3 method:
estimate_temperature(x, ...)
## S3 method for class 'OpenSpecy'
estimate_temperature(
x,
downwelling,
temperature_range_k,
fit_range_cm1,
radiance_uncertainty = NULL,
emissivity_stat = c("planck_weighted", "mean", "median", "max"),
block_size = NULL,
...
)
## S3 method for class 'FileSpecs'
estimate_temperature(
x,
downwelling,
temperature_range_k,
fit_range_cm1,
radiance_uncertainty = NULL,
emissivity_stat = c("planck_weighted", "mean", "median", "max"),
block_size = NULL,
...
)
x |
An |
... |
Additional arguments passed to methods. |
downwelling |
Required background/downwelling radiance. Supply a
numeric scalar, a numeric vector aligned to |
temperature_range_k |
A finite, increasing two-value temperature search interval in kelvin. The endpoints are rejection boundaries, not valid estimates. |
fit_range_cm1 |
A finite two-value fitting interval in inverse centimetres. Select a range for which the opaque, isothermal, surface-leaving model is valid. |
radiance_uncertainty |
Optional positive radiance uncertainty, supplied
as a numeric scalar/vector or one-spectrum |
emissivity_stat |
One scalar emissivity reduction per spectrum:
|
block_size |
Optional positive whole number of in-memory spectra per
compute block. |
The surface model is
L = epsilon * B(T) + (1 - epsilon) * L_down, for an opaque isothermal
target. Because temperature-emissivity separation is underdetermined, the
selected temperature is conditional on a smooth-emissivity prior. A unique
interior roughness minimum is required. Boundary, flat, multiple,
unresolved, near-singular, and insufficient-band cases remain aligned but
return NA estimates and a diagnostic status.
planck_weighted integrates the retrieved spectral emissivity over the
fitting band using Planck radiance and trapezoidal wavenumber weights. It is
a band-effective directional value; it is not necessarily the total
hemispherical emissivity listed in material tables. Emissivity also depends
on wavelength, temperature, viewing geometry, surface finish, oxidation,
particle thickness, and sub-pixel mixing.
No values are clipped to [0, 1]. The physical fraction reports how much of
the retrieved curve lies in that interval, making calibration/model failures
visible. Use direct radiance or established signal/noise contrast as the
baseline for particle detection until TES metrics are validated on held-out
measurements.
estimate_temperature() returns a source-aligned data.table with
estimated material temperature, one selected emissivity value, roughness,
physical- and valid-band fractions, fitting-band provenance, and a status.
Only status == "ok" rows contain estimates. calculate_emissivity()
returns an OpenSpecy object with unclipped emissivity spectra.
National Bureau of Standards. Radiometric temperature measurements: II. Applications (Technical Note 910-8). https://www.nist.gov/publications/self-study-manual-optical-radiation-measurements-part-i-concepts-chapter-12
Borel CC (1997). Iterative retrieval of surface emissivity and temperature for a hyperspectral sensor. https://digital.library.unt.edu/ark:/67531/metadc696880/
Wilber AC, Kratz DP, Gupta SK (1999). Surface emissivity maps for use in satellite retrievals of longwave radiation. NASA/TP-1999-209362.
Wu Z, Ren H, Zhang T, Qin Q, Dong J, Ye X (2017). A modified method to prevent false minimums occurring in iterative spectrally smooth temperature emissivity separation. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/IGARSS.2017.8128417")}.
calculate_emissivity(), sig_noise(), def_features()
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