Martti Hallikainen

TL;DR: In this paper, the authors evaluated the microwave dielectric behavior of soil-water mixtures as a function of water content and soil textural composition for the 1.4-to 18-GHz region.

TL;DR: In this article, the authors evaluate the microwave dielectric behavior of soil-water mixtures as a function of water content, temperature, and soil textural composition, and present two mixing models to account for the observed behavior: 1) a semi-empirical refractive mixing model that accurately describes the data and requires only volumetric moisture and soil texture as inputs, and 2) a theoretical four-component mixing model explicitly accounts for the presence of bound water.

TL;DR: The derivation, testing, and employment to parameter retrieval of the Helsinki University of Technology snow microwave emission model is presented and a new inversion technique for the SWE retrieval from spaceborne data based on the developed model is developed.

TL;DR: In this article, both the Debye-like semi-empirical model and the theoretical Polder-Van Santen mixing model were found to describe adequately the dielectric behavior of wet snow.

TL;DR: In this article, an empirical neural network algorithm is applied to estimate the transfer functions between the major characteristics of surface waters and the satellite optical and microwave data in the Gulf of Finland, where significant correlations were observed between digital data and chlorophyll-a (Chl-a ), suspended sediment concentration (SSC), turbidity (Turb), and Secchi disk depth (SDD).