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The range of salinity and temperature measurements used to develop models for the dielectric constant of sea water have in the past been limited to values appropriate for the open ocean. But there are important water bodies, such as the Great Salt Lake in Utah, with salinities much larger than those encountered in the open ocean. Extrapolating existing models to the values of salinity beyond the limits of the data used to create the model can result in unrealistic predictions in remote sensing applications. This can be prevented by using a model for conductivity based on the definition of salinity and ensuring that polynomials used to model the other unknown parameters result in bounded behavior at high salinity. New laboratory measurements with high salinity (up to 150 pss) are reported and used to test a model with these adjustments.

There is growing interest in using passive microwave observations and vegetation optical depth (VOD) to study the above-ground biomass (AGB) and carbon stocks evolution. L-band observations, in particular, have been shown to be very sensitive to AGB. Here, thanks to the multiangle capabilities of the soil moisture and ocean salinity mission, a new approach to estimate AGB directly from multiangular L-band brightness temperatures (TBs) is proposed, thus surpassing the use of intermediate variables such as VOD. The European Space Agency (ESA) Climate Change Initiative (CCI) Biomass maps for the years 2010, 2017, and 2018 are used as the AGB reference. AGB estimates from artificial neural networks (ANN) using a purely data-driven approach explained up to 88% of AGB variability globally; even so, a decrease in retrieval performance was observed when models are applied to data from years different than the year used for their training. A new training methodology based on multiyear training sets is presented, leading to results showing more stability for temporal analyses. The best set of predictors and an optimal learning dataset configuration are proposed based on an assessment of the accuracy of the estimates. The ANN methodology using TBs is a promising alternative with respect to the common method of using a parametric function to estimate AGB from VOD. ANNs AGB estimates showed a higher correlation with CCI AGB maps ($R$2 $\sim$0.87 instead of $\sim$0.84) and presented a stronger agreement with their spatial structure and less differences in residual maps.

The freezing front depth (zff) of annual freeze–thaw cycles is critical for monitoring the dynamics of the cryosphere under climate change because zff is a sensitive indicator of the heat balance over the atmosphere-cryosphere interface. Meanwhile, although it is very promising for acquiring global soil moisture distribution, the L-band microwave remote sensing products over seasonally frozen grounds and permafrost is much less than in wet soil. This study develops an algorithm, i.e., the brightness temperature inferred freezing front (BT-FF) model, for retrieving the interannual zff with the diurnal amplitude variation of L-band brightness temperature (ΔTB) during the freezing period. The new algorithm assumes first, the daily-scale solar radiation heating/cooling effect causes the daily surface thawing depth (ztf) variation, which leads further to ΔTB; second, ΔTB can be captured by an L-band radiometer; third, ztf and zff are negatively linear correlated and their relation can be quantified using the Stefan equation. In this study, the modeled soil temperature profiles from the land surface model (STEMMUS-FT, i.e., simultaneous transfer of energy, mass, and momentum in unsaturated soil with freeze and thaw) and TB observations from a tower-based L-band radiometer (ELBARA-III) at Maqu are used to validate the BT-FF model. It shows that, first, ΔTB can be precisely estimated from ztf during the daytime; second, the decreasing of ztf is linearly related to the increase of zff with the Stefan equation; third, the accuracy of retrieved zff is about 5–25 cm; fourth, the proposed model is applicable during the freezing period. The study is expected to extend the application of L-band TB data in cryosphere/meteorology and construct global freezing depth dataset in the future.

NASA's Soil Moisture Active Passive (SMAP) mission originally included both passive and active L-band measurement capabilities. It was the first satellite instrument to provide global L-band radar observations of normalized radar cross section ($\sigma ^{0}$) at multiple resolutions. The SMAP radar collected high-resolution ($\sim$1–3 km) synthetic aperture radar (SAR) measurements over most of the earth's land mass. It simultaneously collected low-resolution 6 × 30 km “slice” and full-footprint 29 × 35 km measurements. The SMAP radar operated for 83 days, from day of the year 103 to 186 in 2015, before the transmitter failed. The SMAP radar was designed to make vegetation roughness measurements in support of the SMAP primary mission to measure soil moisture, but the radar data are useful for a variety of applications, particularly in the polar regions. Unfortunately, limitations in the data download volume precluded the downlink of high-resolution data over Antarctica, sea ice in the polar regions, and various islands. Nonetheless, low-resolution slice and footprint data were collected and downlinked over these areas. To better exploit these low-resolution data, this article employs image reconstruction techniques to create twice-daily enhanced resolution SMAP radar images from the slice and footprint measurements. To validate the resolution enhancement, the enhanced resolution data are compared to SAR results over Greenland and South America. The new dataset is provided to the science community to support cryosphere and climate studies.

Sea surface salinity (SSS) in coastal zones with high temporal and spatial resolutions is also needed for scientific research and marine economy. However, one of the limitations of airborne L-band real-aperture microwave radiometers and synthesis aperture interferometric radiometers is high mass and volume or the high complexity of the system. To overcome the limitations, an L-band phased array radiometer is proposed as an alternative solution to monitor SSS in coastal zones from a small unmanned aerial vehicle (UAV). The L-band phased array radiometer is introduced, which only comprises a phased array and a single receiver. In addition, the calibration methods of the L-band phased array, radiometer that the external calibration method and the internal calibration method, are also introduced. A series of experiments was performed to assess the performance of the L-band phased array radiometer. The results indicate that the L-band phased array radiometer show a good performance in low pointing angles and low scanning angles. As a conclusion, the L-band phased array radiometer can be an alternative solution to monitor SSS in coastal zones with high temporal and spatial resolutions from a small UAV.

This article presents the FMPL-2 on board the FSSCat mission, the 2017 ESA Small Sentinel Satellite Challenge and overall Copernicus Masters Competition winner. FMPL-2 is a passive microwave instrument based on a software-defined radio that implements a conventional global navigation satellite system-Reflectometer and an L-band radiometer, occupying 1 U of a 6 U CubeSat. The article describes the FSSC at mission context, the payload design and implementation phases, the tests results in a controlled environment, and finally the calibration algorithms applied to the downloaded data in order to extract the appropriate geophysical parameters: sea-ice coverage, sea-ice thickness (SIT), and low-resolution soil moisture. This article covers the overall payload design, from a high-level block diagram down to single-component specifications from both hardware and software points of view. The main block of the instrument is based on the combination of an FPGA, which virtualizes a dual-core ARM processor, where most of the calculus are performed, and a software-defined radio module, in charge of I/Q data demodulation. The article also explains the design and implementation of a signal conditioning board required for the correct operation and calibration of both instruments.