We present a concept for UHF/L-band (0.5–2 GHz) remote sensing of Antarctic ice sheet internal temperature using a highly sparse synthetic aperture radiometer constellation. This concept leverages the relative stability of ice sheet thermal emission over long temporal periods to gradually assemble a collection of array baselines which are jointly transformed to develop large image facets. We formulate a calculation of minimum array complexity based on the desired sensitivity, spatial resolution, and time available for observations. We determine from this calculation that such a system can achieve 1–10-km spatial resolution (significantly finer than the program of record) over monthly to yearly timescales with as few as 10–20 elements; even fewer elements are required for observing only the ice sheet center. The inverse problem of reconstructing image facets from mixed-pointing and mixed-configuration observations is posed using a Fourier domain data constraint with a total variational regularization in the image domain. This approach enables image formation from heterogeneous observations while mitigating artifacts. We present a notional constellation design for three satellites which could accomplish the necessary baseline sampling by rotating the phase and semimajor axis of spacecraft relative positions in planar circular orbits (PCOs). We demonstrate image formation by observing system simulations leveraging predictions of Antarctica’s multiwavelength brightness temperature computed from ice sheet thermomechanical and radiative transfer models.

Accurate models for atmospheric propagation are necessary to derive operational data products from GNSS remote sensing measurements. A laboratory system has been developed to make direct measurements of the refractivity and absorptivity of atmospheric gases at GPS L1 and L2 frequencies. The laboratory system consists of a cylindrical microwave resonator housed within an environmental chamber and is designed for measurements at temperatures and pressures relevant to the Earth's atmosphere. We present measurements of the Debye absorption of oxygen from 1.2-2.3 GHz at pressures of 200-1000 Torr and at temperatures from 290-350 K. These measurements are applicable to the modeling of L band atmospheric opacity in the context of GNSS radio occultation experiments.

We present the STASIS concept, an innovative approach to developing high spatial resolution maps of Antarctic ice sheet thermal emission at P/L band. Rather than using a large real aperture system, the relative stability of ice sheet temperature over time implies that a sparse array system would be able to gradually build up spatial frequency sampling and generate images with 1K sensitivity at 1-10 km spatial resolution over monthly-seasonal time scales. This contrasts with the requirement for full snapshot spatial frequency coverage required by systems for monitoring soil moisture and ocean salinity Sensitivity heuristic calculations are presented, and simulated interferometric observations are generated incorporating a realistic ice sheet thermal emission model.

The soil moisture active passive (SMAP) instrument has been used to infer sea surface wind speed from its brightness temperature measurements. To do so, the SMAP wind speed retrieval process requires the removal of sea surface temperature (sst) and sea surface salinity (sss) impact on brightness temperature. Estimating the so-called excess surface emissivity ($\Delta {e}$), that is the sst normalized difference between the measured brightness temperature of the sea surface and the corresponding brightness temperature of a flat surface, is one way of accomplishing such a task. In this article, we investigate whether SMAP$\Delta {e}$contains residual dependencies to sst and sss. To do so, v5.0 SMAP brightness temperature measurements, derived by the Jet Propulsion Laboratory, are used. For any fixed numerical weather prediction model wind speed above 15 m/s, down to a 20% decrease in SMAP$\Delta {e}$is observed as the sst increases from 274 to 304 K. For any fixed wind speed between 8 and 15 m/s, the sst residual dependence is weaker with SMAP$\Delta {e}$exhibiting a 1%–2% decrease as the sst increases. Below 8 m/s, this pattern becomes prevalent again, when the significant wave height (Hs) is greater than 3.5 m. SMAP$\Delta {e}$decreases as much as 50% with increasing sss, most notably below 8 and above 15 m/s when Hs is considered. This analysis has also shown that below 8 m/s and for swell dominant seas, a decrease in either sss or sst results in a decrease in SMAP$\Delta {e}$sensitivity to wind-induced sea surface roughness.

We discuss an approach to measuring high-resolution maps of Antarctic ice sheet temperatures using repeat-pass sparsely sampled microwave interferometry. This approach follows from the inference that the relative invariance of ice sheet temperatures on annual timescales obviates the need for high snapshot sensitivity imposed as a requirement for observing more variable regions of the Earth system with interferometers such as SMOS. Such measurements could hypothetically be conducted with spatial resolutions less than 10 km using a small constellation of satellites. We discuss specifically how modifications to sheet-base geothermal heat flux could manifest as observable thermal signatures and a strategy to form images from a mosaic of multiple heterogeneous sparsely sampled observations, and we conclude with comments on necessary areas for future investigations.

The Soil Moisture Active Passive instrument (SMAP) sea surface wind speed potential dependence to sea surface salinity (sss) and sea surface temperature (sst) is explored. SMAP JPL v5.0 and SMAP REMSS v0.10 are used for this analysis. The SMAP wind speed error (i.e. SMAP-NCEP) is bin averaged per sss and per sst ranges, respectively, and plotted against NCEP wind. This analysis shows that both SMAP wind speed products exhibit clear dependence to both sss and sst: for NCEP winds less than 15 m/s, sst bin averaged error curves for both products show spreads below 1 m/s; for NCEP winds greater than 15 m/s, the maximum spread between the error curves can be greater than 2 m/s. Spreads between sss bin averaged curves can be as high as 5 m/s. This maximum spread is reduced below 1 m/s if data with salinity less than 32 psu is excluded.

This paper explores the application of modern image processing techniques in retrieving high-resolution passive microwave images of the polar ice regions on Earth from sparsely sampled interferometric array measurements. Such observations, sensitive to ice sheet temperature, would be valuable benchmark measurements for ice process models. In this paper, we propose to use a total variation-based method that addresses the challenges associated with large sidelobes and blurry maps resulting from long baseline interferometry. We present a robust algorithm that employs total variation (TV) minimization and the split Bregman optimization. This technique effectively deconvolves images, preserves edges, and minimizes noise amplification without introducing artifacts. To evaluate the algorithm’s performance, we performed tests on a simulated image and a real satellite image of Antarctica. Additionally, we assessed the algorithm’s performance using different interferometric array configurations, including both dense and sparse arrays with varying numbers of elements.

We present the Forward Ocean Atmosphere Microwave (FOAM) radiative transfer model, a Python package designed to simulate passive microwave observations of ocean state, and discuss its basic architecture. FOAM is particularly useful for exploring concept architectures for future missions measuring ocean salinity from space. We discuss how FOAM can be used to design missions meeting a range of accuracy and revisit requirements, and we consider an example of ocean salinity retrieval with a wideband radiometer system.

We consider the impacts of wind direction and model function choice on retrieved wind speed statistics using SMAP Version 5 L2 product retrieval algorithms. Attempts to retrieve ocean wind direction using SMAP measurements lead to arti-facts in wind speed statistics below 12 m/s which are absent if the wind direction is taken from ancillary data. Model functions derived from Aquarius matchups achieve biases within 0.5 psu and standard deviations within 2 m/s at speeds greater than 5 m/s, with the better performance from NCEP-based matchups. The SMAP-derived model function obtains bet-ter agreement with the global wind speed distribution of the NCEP ancillary product, but with greater scatter and poorer performance near coastal regions. Further effort is needed to improve SMAP wind retrievals for use in operational applications.

Remote sensing of sea surface salinity (SSS) near land is difficult due to land contamination. In this study, we assess SSS retrieved from SMAP (JPL V5 and RSS V4) in coastal region using in situ data collected by saildrones during the North American West Coast Survey. Collocated satellite and saildrone salinity measurements reveal consistent large-scale features: the fresh water (low SSS) related with the Columbia River discharge, and the relatively salty water (high SSS) near Baja California associated with regional upwelling. The standard deviation of the difference (stdD) for collocations with SMAP Level 3 (8 days average) between 40 to 100km from land is 0.51 (0.56) psu for JPL V5 (RSS V4 70km). This is encouraging for the potential application of SMAP SSS in monitoring coastal zone freshwater particularly where exists large freshwater variance. In regions closer to land, stdD for JPL V5 increases to 0.8 (1.4) psu in the zone 20-40km (<20km). RSS V4 delivers 42% less data in 20-40km, and almost no data within 20km. In attempt to reduce the uncertainty of SMAP SSS near land and understand the discrepancy between JPL and RSS products, we investigate saildrone collocations with SMAP Level 2 (direct output from retrieval), in terms of distance to land, and saildrone's simultaneous measurements of sea surface temperature and surface wind speed (both are important ancillary parameters for SSS retrieval). Our analysis identified quite different areas of future improvement for JPL and RSS algorithms.