Machine Learning-Based Soil Moisture Retrieval Using CYGNSS and Interpolated SMAP Data

Recently, various models have been developed, including the fractional Brownian motion (fBm), to analyse the stochastic properties of geodetic time series, together with the estimated geophysical signals. The noise spectrum of these time series is generally modelled as a mixed spectrum, with a sum of white and coloured noise. Here, we are interested in modelling the residual time series after deterministically subtracting geophysical signals from the observations with the Lévy processes.

Geodetic time series, series of observations measured from various satellites, must be modelled carefully to extract accurate information about geophysical processes. These models take into account the properties of the noise in these time series, which are generally a mixed of several kinds of noise. This work proposes a model based on the family of Levy processes (Gaussian, fractional and stable) as an alternative with real and simulated data.