Ocenjevanje vlažnosti iz radarskih slik SAR : doktorska disertacija

slovenian

doktorska dela

2012

Univerza v Mariboru, Fakulteta za elektrotehniko računalništvo in informatiko

This dissertation presents soil-moisture estimation from Synthetic Aperture Radar (SAR) images, where backscattering coefficient is transformed into relative dielectric constant from which it is pos-sible to retrieve volumetricsoil-moisture values. The novelty of this dissertation is a development of a new soil-moisture parameter retrieval model. The proposed model works at X-band microwave radiation, and is composed from bare-soil estimation with additional correction part for small vegeta-tion. The soil-moisture estimation algorithm is compared with in-situ measurements collected simulta-neously to image acquisitions. For this dissertation high-resolution spot-light TerraSAR-X images were collected under different radar parameters, which include: various incidence angles, polarizations and range bandwidths. In this dissertation two different soil moisture estimation models are proposed, where the first one is based on the Shi semi-empirical model, thus it only works on bare-soil surfaces. The Shi semi-empirical model is modified in a sense, that new polynomials are introduced in the Shi model equation. Polynomial parameters are later determined using real synthetic aperture radar images represented as backscattering coefficients and in-situ measurement values. The second proposed model is more general model, because it is able to estimate soil-moisture from areas with small vegetation and is based on the water-cloud model. The soil-moisture is not only dependent on radar parameters, but also on soil surface roughness, that can be described with surface RMS height, correlation length, and correlationćs function. The other unknown parameters are vegetation fractional part and Fresnelćs reflectivity coefficient. The idea of the proposed model is that it is possible to estimate unknown parameters by using good properties of data restoration techniques, for which this dissertation relies on the Tikhonov regularization. This regularization estimates the unknown soil moisture parameters, where regularization parameters should cancel out or compensate the correla-tion length and correlationćs function. Experimental results showed that the proposed models provide good results for estimating soil-moisture on bare-soils and areas with small vegetation. However, the results show far superior estimation accuracy for the proposed two component model based on water-cloud model with Tikhonov regularization scheme.

07.08.2023