isofit.core.instrument

Attributes

wl_tol

Classes

Instrument

Module Contents

wl_tol = 0.01

class Instrument(full_config: Config)

n_chan

fast_resample

bounds

scale

init

prior_mean

prior_sigma

Sa_cached

Sa_normalized

statevec_names

n_state

integrations

dn_uncertainty_embedding = None

unknowns

bval

bvec

calibration_fixed = True

xa()

Mean of prior distribution, calculated at state x.

Sa()

Covariance of prior distribution (diagonal).

Sb(meas)

Uncertainty due to unmodeled variables.

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

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.

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]

sample(x_instrument, wl_hi, rdn_hi)

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

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.

calibration(x_instrument)

Calculate the measured wavelengths.

static DN_additive_uncertainty(meas, rcc, interp, inflation)

summarize(x_instrument, geom)

Summary of state vector.