Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

Microwave Emission Models (EM) are used in retrieval algorithms to estimate geophysical state parameters such as soil Water Content ( $WC$ ) and vegetation optical depth ( $\tau$ ), from brightness temperatures $T_{\mathrm{B}}^{p,\theta }$ measured at nadir angles $\theta$ at Horizontal and Vertical polarizations $p=\left\{\mathrm{H},\mathrm{V}\right\}$ . An EM adequate for implementation in a retrieval algorithm must capture the responses of $T_{\mathrm{B}}^{p,\theta }$ to the retrieval parameters, and the EM parameters must be experimentally accessible and representative of the measurement footprint. The objective of this study is to explore the benefits of the multiple-scattering Two-Stream (2S) EM over the “Tau-Omega” (TO) EM considered as the “reference” to retrieve $WC$ and $\tau$ from L-band $T_{\mathrm{B}}^{p,\theta }$ . For sparse and low-scattering vegetation $T_{\mathrm{B},EM}^{p,\theta }$ simulated with $EM=\left\{\mathrm{TO},2\mathrm{S}\right\}$ converge. This is not the case for dense and strongly scattering vegetation. Two-Parameter (2P) retrievals $\mathbf{2}{P}_{RC}=(W{C}_{RC},{\tau }_{RC})$ are computed from elevation scans $T_{\mathrm{B}}^{p,{\theta }_j}=T_{\mathrm{B},\mathrm{TO}}^{p,{\theta }_j}$ synthesized with TO EM and from $T_{\mathrm{B}}^{p,{\theta }_j}$ measured from a tower within a deciduous forest. Retrieval Configurations ( $RC$ ) employ either $EM=\mathrm{TO}$ or $EM=2\mathrm{S}$ and assume fixed scattering albedos. $W{C}_{RC}$ achieved with the 2S RC is marginally lower ( $~1{\mathrm{m}}^3{\mathrm{m}}^{-3}$ ) than if achieved with the “reference” TO RC, while ${\tau }_{RC}$ is reduced considerably when using 2S EM instead of TO EM. Our study outlines a number of advantages of the 2S EM over the TO EM currently implemented in the operational SMOS and SMAP retrieval algorithms.

A low-mass and low-volume dual-polarization L-band radiometer is introduced that has applications for ground-based remote sensing or unmanned aerial vehicle (UAV)-based mapping. With prominent use aboard the ESA Soil Moisture and Ocean Salinity (SMOS) and NASA Soil Moisture Active Passive (SMAP) satellites, L-band radiometry can be used to retrieve environmental parameters, including soil moisture, sea surface salinity, snow liquid water content, snow density, vegetation optical depth, etc. The design and testing of the air-gapped patch array antenna is introduced and is shown to provide a 3-dB full power beamwidth of 37°. We present the radio-frequency (RF) front end design, which uses direct detection architecture and a square-law power detector. Calibration is performed using two internal references, including a matched resistive source (RS) at ambient temperature and an active cold source (ACS). The radio-frequency (RF) front end does not require temperature stabilization, due to characterization of the ACS noise temperature by sky measurements. The ACS characterization procedure is presented. The noise equivalent delta (Δ) temperature (NEΔT) of the radiometer is ~0.14 K at 1 s integration time. The total antenna temperature uncertainty ranges from 0.6 to 1.5 K.

Airport terminal is one of the indoor scenarios envisioned for mm-wave 5G deployment. In this paper, we characterize the propagation channel in the Helsinki airport at 15, 28, and 60 GHz bands by means of directional wideband channel sounding. The radio environment was characterized by studying the specular propagation paths, specular and diffuse power contributions, polarization, and the delay and angular spreads. All the studied metrics in different bands were compared and the frequency dependency was analyzed.

Ground permittivity and snow density retrievals for the “snow-free period”, “cold winter period”, and “early spring period” are performed using the experimental L-band radiometry data from the winter 2016/2017 campaign at the Davos-Laret Remote Sensing Field Laboratory. The performance of the single-angle and multi-angle two-parameter retrieval algorithms employed during each of the aforementioned three periods is assessed using in-situ measured ground permittivity and snow density. Additionally, a synthetic sensitivity analysis is conducted that studies melting effects on the retrievals in the form of two types of “geophysical noise” (snow liquid water and footprint-dependent ground permittivity). Experimental and synthetic analyses show that both types of investigated “geophysical noise” noticeably disturb the retrievals and result in an increased correlation between them. The strength of this correlation is successfully used as a quality-indicator flag for the purpose of filtering out highly correlated ground permittivity and snow density retrievals. It is demonstrated that this filtering significantly improves the accuracy of both ground permittivity and snow density retrievals compared to corresponding reference in-situ data. Experimental and synthetic retrievals are performed in retrieval modes RM = “H”, “V”, and “HV”, where brightness temperatures from polarizations p = H, p = V, or both p = H and V are used, respectively, in the retrieval procedure. Our analysis shows that retrievals for RM = “V” are predominantly least prone to the investigated “geophysical noise”. The presented experimental results indicate that retrievals match in-situ observations best for the “snow-free period” and the “cold winter period” when “geophysical noise” is at minimum.

The present study demonstrates the successful use of the high sensitivity of L-band brightness temperatures to snow liquid water in the retrieval of snow liquid water from multi-angular L-band brightness temperatures. The emission model employed was developed from parts of the “microwave emission model of layered snowpacks” (MEMLS), coupled with components adopted from the “L-band microwave emission of the biosphere” (L-MEB) model. Two types of snow liquid water retrievals were performed based on L-band brightness temperatures measured over (i) areas with a metal reflector placed on the ground (“reflector area”— $T_{\mathrm{B},\mathrm{R}}$ ), and (ii) natural snow-covered ground (“natural area”— $T_{\mathrm{B},\mathrm{N}}$ ). The reliable representation of temporal variations of snow liquid water is demonstrated for both types of the aforementioned quasi-simultaneous retrievals. This is verified by the fact that both types of snow liquid water retrievals indicate a dry snowpack throughout the “cold winter period” with frozen ground and air temperatures well below freezing, and synchronously respond to snowpack moisture variations during the “early spring period”. The robust and reliable performance of snow liquid water retrieved from $T_{\mathrm{B},\mathrm{R}}$ , together with their level of detail, suggest the use of these retrievals as “references” to assess the meaningfulness of the snow liquid water retrievals based on $T_{\mathrm{B},\mathrm{N}}$ . It is noteworthy that the latter retrievals are achieved in a two-step retrieval procedure using exclusively L-band brightness temperatures, without the need for in-situ measurements, such as ground permittivity ${\epsilon }_{\mathrm{G}}$ and snow mass-density ${\rho }_{\mathrm{S}}$ . The latter two are estimated in the first retrieval-step employing the well-established two-parameter $\left({\rho }_{\mathrm{S}},{\epsilon }_{\mathrm{G}}\right)$ retrieval scheme designed for dry snow conditions and explored in the companion paper that is included in this special issue in terms of its sensitivity with respect to disturbative melting effects. The two-step retrieval approach proposed and investigated here, opens up the possibility of using airborne or spaceborne L-band radiometry to estimate $\left({\rho }_{\mathrm{S}},{\epsilon }_{\mathrm{G}}\right)$ and additionally snow liquid water as a new passive L-band data product.