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Instrument

source module isofit.core.instrument

Classes

  • Instrument — A model of the spectrometer instrument, including spectral response and noise covariance matrices. Noise is typically calculated from a parametric model, fit for the specific instrument. It is a function of the radiance level.

source class Instrument(full_config: Config)

A model of the spectrometer instrument, including spectral response and noise covariance matrices. Noise is typically calculated from a parametric model, fit for the specific instrument. It is a function of the radiance level.

Methods

  • xa — Mean of prior distribution, calculated at state x.

  • Sa — Covariance of prior distribution (diagonal).

  • Sb — Uncertainty due to unmodeled variables.

  • Sy — Calculate measuremment error covariance. Kelvin Man Yiu Leung and Jayanth Jagalur Mohan (MIT) developed the noise clipping strategy.

  • dmeas_dinstrument — Jacobian of measurement with respect to the instrument free parameter state vector. We use finite differences for now.

  • dmeas_dinstrumentb — Jacobian of radiance with respect to the instrument parameters that are unknown and not retrieved, i.e., the inevitable persisting uncertainties in instrument spectral and radiometric calibration.

  • eof_offset

  • sample — Apply instrument sampling to a radiance spectrum, returning predicted measurement.

  • simulate_measurement — Simulate a measurement by the given sensor, for a true radiance sampled to instrument wavelengths. This basically just means drawing a sample from the noise distribution.

  • calibration — Calculate the measured wavelengths.

  • DN_additive_uncertainty

  • summarize — Summary of state vector.

source method Instrument.xa()

Mean of prior distribution, calculated at state x.

source method Instrument.Sa()

Covariance of prior distribution (diagonal).

source method Instrument.Sb(meas)

Uncertainty due to unmodeled variables.

source method Instrument.Sy(meas, geom)

Calculate measuremment error covariance. Kelvin Man Yiu Leung and Jayanth Jagalur Mohan (MIT) developed the noise clipping strategy.

Input: meas, the instrument measurement Returns: Sy, the measurement error covariance due to instrument noise

source method Instrument.dmeas_dinstrument(x_instrument, wl_hi, rdn_hi)

Jacobian of measurement with respect to the instrument free parameter state vector. We use finite differences for now.

source method Instrument.dmeas_dinstrumentb(x_instrument, wl_hi, rdn_hi)

Jacobian of radiance with respect to the instrument parameters that are unknown and not retrieved, i.e., the inevitable persisting uncertainties in instrument spectral and radiometric calibration.

Input: meas, a vector of size n_chan Returns: Kb_instrument, a matrix of size [n_measurements x nb_instrument]

source method Instrument.eof_offset(x_instrument)

source method Instrument.sample(x_instrument, wl_hi, rdn_hi)

Apply instrument sampling to a radiance spectrum, returning predicted measurement.

source method Instrument.simulate_measurement(meas, geom)

Simulate a measurement by the given sensor, for a true radiance sampled to instrument wavelengths. This basically just means drawing a sample from the noise distribution.

source method Instrument.calibration(x_instrument)

Calculate the measured wavelengths.

source staticmethod Instrument.DN_additive_uncertainty(meas, rcc, interp, inflation)

source method Instrument.summarize(x_instrument, geom)

Summary of state vector.