Enceladus, Saturn's sixth-largest and one of its innermost moons, is an active icy world. The Cassini mission (2004-2017) discovered water-rich plumes venting from fractures in its South Polar Terrain (SPT), making Enceladus a prime candidate in the search for habitable environments in the Solar System. Understanding its geological environment and thermal evolution requires knowledge of both surface and subsurface properties, which remain poorly constrained by optical and infrared observations alone. Microwave radar observations, on the other hand, are sensitive to subsurface scattering structures and temperatures at depth. Here we present the joint analysis of Cassini RADAR active and passive observations acquired at a wavelength of 2.2-cm during the only targeted-flyby of Enceladus (called E16) dedicated to the RADAR, on November 6, 2011. Using a multi-layer unified backscatter-thermal emission radiative transfer model, we jointly analyze the Synthetic Aperture Radar backscatter (sigma(0)) and brightness temperatures (T-B) measured along the E16 closest-approach swath, in the SPT. The results point to large ice grain radii (>500 mu m) throughout the swath, favoring volume scattering as the dominant scattering mechanism. As a result, most of the radar signal comes from the first few meters below the surface, even though, due to the high transparency of Enceladus' icy subsurface, waves can penetrate much deeper, up to 10-20 m in some areas. Variations in water-ice purity (0-25%) and porosity (60-90%) are inferred between geological terrains, indicating differences in surface maturity and age. Anomalously high T-B values observed over parts of the swath can only be explained by the presence of an ocean at shallow subsurface (2-5 km deep) combined with a thin (1-10 m) regolith and a conductive ice-shell, leading to enhanced heat loss (as high as similar to 900 mWm(-2)). The presence of an ocean at shallow depth is also possible elsewhere on the swath but combined with a thick (similar to 100-500 m) and porous (60-90%) regolith layer acting as an insulating layer, leading to low heat losses (< 300 mWm(-2)) in these areas. Our study also highlights the strong value of co-located radar active and passive observations for constraining the geological and thermal state of the sub-surfaces in the Solar system and provides key information for future radar observations of Enceladus.

The Surface Water and Ocean Topography (SWOT) mission is primarily designed to measure Sea Surface Height in two dimensions at an unprecedented resolution thanks to its innovative Ka-band radar interferometer KaRIn. In addition to the topography measurements derived from the phase difference between the images acquired at each of the two antennas separated by 10 meters, KaRIn can also provide information about the sea state, by exploiting the measured power in each of the SAR images and the interferometric correlation between both acquisition channels.This last quantity, sometimes referred to as interferometric coherence, is directly affected by the presence of surface waves. This provides a fantastic opportunity to measure, for the first time at a global scale, Significant Wave Height at kilometric resolutions (well below the reach of nadir altimeters) and in two dimensions. This, however, requires estimating all other sources of decorrelation of instrumental origin with an exquisite precision to avoid misinterpreting instrumental effects as geophysical signals.In this talk, I will briefly describe how the interferometric acquisitions by KaRIn are calibrated and processed to obtain SWH maps in 2D at various km-scale resolutions (typically 2x2 km or 5x5 km), and discuss how the accuracy at which we need to estimate all the other sources of decorrelation varies with cross-track distance and actual SWH to highlight the most challenging regimes for the inversion. I will then present comparisons between the KaRIn two-dimensional SWH measurements and several independent sets of validation data, including data from SWOT’s nadir altimeter, from the SAR nadir altimeter on-board Sentinel-3, from MASS’s lidar, and from in-situ data. I will finish by discussing various physical features that can be observed in the retrieved SWH fields to illustrate that the high resolution and the two-dimensional character of SWOT measurements really open the door to the quantitative study of the processes that contribute to sea-state variations at small scales.

The SWOT mission was launched in December 2022. Its first of a kind KaRIn instruments provides two dimensionnal images of ocean surface topography over a 120 km wide swath. Ocean surface topography data quality and mission performance have been assessed and monitored over the first year of SWOT mission, as part of mission performance activities performed by the mission project. Here we present a synthesis of SWOT mission performances over ocean: from data availability and validity to end-to-end performance metrics (eg SSH differences at crossovers, wavenumber spectrum and comparison with the current nadir altimetry constellation). We also discuss the performance of cross-calibration algorithms at level 2 (based on crossovers) and level 3 (based on other altimeters). Results presented here are based on the analysis of the 2 km product during both the calval (1 day repeat orbit) and science (21 day repeat orbit) phases and focused on ocean surface topography retrievals. All mission CalVal metrics highlight the excellent performance of KaRIn measurements. We also present some known limitations of current SWOT products that are of interest to science users.

The Surface Water and Ocean Topography (SWOT) mission is a collaboration between NASA and CNES that measures water extent, surface heights, and river slopes for inland water bodies and sea surface height (SSH), wind speed, and significant wave height (SWH) over open ocean. SWOT was launched on Dec. 15, 2022 and is currently operational. In this paper we discuss the algorithm for retrieving ocean surface wind speed from backscatter measurements obtained from the SWOT Ka-band Radar Interferometer (KaRIn). We validate that algorithm by comparing the retrieved wind speed to collocated measurements from the ASCAT ocean wind scatterometer onboard ESA’s MetOP-B and -C satellites.

The Surface Water and Ocean Topography (SWOT) mission was recommended by the 2007 National Research Council Decadal Survey to expand on previous altimetry missions like TOPEX/Poseidon. Utilizing wide-swath altimetry technology, SWOT aims to achieve complete coverage of the world’s oceans and freshwater bodies through high-resolution elevation measurements. SWOT received approval for implementation in 2016, it was ultimately launched in December 2022, and it is currently delivering preliminary data to the public. The primary instrument in SWOT is the Ka-band Radar Interferometer (KaRIn) which utilizes JPL-developed radar interferometry technology to measure ocean and surface water levels with unprecedented accuracy. This paper focuses on the challenges in designing, testing, and finally commissioning in flight a complex instrument like KaRIn. We also present preliminary flight performance and compare it with ground measurements and simulations. Our analysis indicates that KaRIn meets or exceeds all its requirements, but it has also revealed several interesting and unexpected observations, offering just a glimpse of future scientific discoveries that KaRIn will enable.

The NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument has performed tomographic SAR experiments over a number of study areas, including Rabi Forest in Gabon in 2016 and Sierra National Forest in California, USA in 2021. Tomographic SAR, or TomoSAR, is a technique enabling 3-D radar imaging with diverse applications including mapping of vegetation structure. Convolutional neural networks (CNNs) have shown widespread potential for many image processing and computer vision tasks such as image segmentation, classification, and object recognition. By using 3-D CNNs rather than 2-D CNNs, the filters can be applied to all three dimensions of a forest volume imaged by TomoSAR. We have trained 3-D CNN-based deep learning models to estimate canopy height and canopy cover from fully polarimetric UAVSAR TomoSAR images using lidar data as training and validation. When applied to canopy height estimation in the Rabi Forest study area, a trained network had root mean square error (RMSE) of 3.6 m (11%) compared to the validation dataset. For canopy cover estimation in the Sierra National Forest study area, the RMSE was 12%. Further work can be done to optimize the network architecture, improve the output spatial resolution, and to check if these methods can be applied to other study areas or to other vegetation structure parameters such as above-ground biomass. The results show the strong potential of 3-D CNNs for mapping wall-to-wall vegetation structure from tomographic SAR imagery using lidar training data.

A spaceborne P-band signals of opportunity synthetic aperture radar concept is proposed for the remote sensing of terrestrial snow. We have completed a performance analysis assuming a formation flight of 3 to 5 SmallSats on one orbit plane. The spacing between the SmallSats is chosen so that their ground tracks will be separated by 50 to 100 m to allow the use of interferometric synthetic aperture radar processing technique to obtain a spatial resolution of a few hundred meters. A point system design has been completed to determine the antenna concept and to indicate the dependence of spatial resolution and signal to noise ratio on the number of receivers. The performance for range delay determination was analyzed to assess the impact of various error sources, including instrument receiver noise and ionospheric delay. The dominant error source is the ionospheric delay, which will be corrected using the split-spectrum algorithm. Our overall error budget analysis indicates that an accuracy of about 3 cm for the snow water equivalent in dry snow and 5 cm for the snow depth of wet snow can be achieved.

We develop, utilize, and validate techniques to produce a global data set of accurate coastal ocean surface vector winds. The dataset extends as near to the coast as 5 km and includes 10 years of SeaWinds on QuikSCAT ocean scatterometer data obtained from 1999 to 2009. We demonstrate improved retrievals over other large land-locked bodies of water as well, such as the Caspian Sea and the Great lakes. To determine the coastal winds we quantify the extent of land contamination in each scatterometer backscatter measurement and to the extent possible remove that contamination. After the measurements are thus corrected we retrieve winds with the corrected measurements using a previously published algorithm which has been extensively used for JPL scatterometer wind products. The coastal processing vastly increases the number of wind vector cells near coasts. We have ten times the number of wind vectors within 10 km of coast as without coastal processing, and over twice as many at 20 km from coast. These new wind vectors are high-quality, and have zero effect on non-coastal wind vectors. The effect of residual land contamination is quantified by comparing to buoys at varying distance from the coast and comparing coastal wind vector cells to oceanward neighbors. We show that the non-coastal QuikSCAT processing has very few good wind vectors nearer to the coast than about 22.5 km. In comparison to buoys, and oceanward neighbors, we find a small increase in speed errors of these new coastal wind vectors versus the performance of non-coastal QuikSCAT at 22.5 km, indicating the high-quality of these new coastal wind vectors. A quality control scheme is employed that flags regions where the coastal wind retrieval is poor due to the assumptions inherent in the technique being locally invalid. The coastal winds retrieved in this manner have been publicly distributed to the oceanography community and utilized in other published works.

An unusual number of tropical systems in the Atlantic, captured in this TCIS visualization, were all evolving simultaneously on 15 September during the record-breaking 2020 hurricane season.

The spaceborne aperture radar (SAR) technique based on a combination of P-band signals of opportunity (SoOp) reflectometry with a sparse array of receivers at low earth orbits (LEOs) and transmit signals from the United States Navy's Mobile User Objective System operating on a geosynchronous altitude has been analyzed. The design focuses on the forward-looking geometry near the specular direction, which allows a high surface reflectivity, in order to obtain adequate signal-to-noise ratio (SNR) with a moderate receiving antenna gain. The sparse array is utilized to sharpen the across-track resolution and reduce the iso-range ambiguity. The formulation for match filtering and illustrations of point target response are presented. This work shows that an array of five to seven receivers is able to achieve an across-track resolution of about 200 m in the outer portion of swath and about 1 km in the center part of swath. The along-track resolution can reach 10 m or better due to the feasibility of a long dwell time for Doppler filtering. We find that the sparse array allows the reduction of the iso-range ambiguity to a level of lower than 5% for a major portion of swath, ~70% or greater depending on the number of receivers and spacing. We have completed an SNR formulation, which can consistently account for both coherent and incoherent scattering regardless the spatial resolution. An analysis of SNR based on the Kirchhoff approximation for rough surface scattering has been performed. We find that it is possible to obtain a swath width of 100 km with an SNR of 5 dB or better for a constellation of seven satellites with a receiving antenna directivity of 15 dBi at a LEO altitude of 675 km for a wide range of surface roughness. Our study suggests the promise of the SoOpSAR concept for high-resolution remote sensing of land surfaces.

Understanding and forecasting hurricanes remains a challenge for the operational and research communities. To accurately predict the Tropical Cyclone (TC) evolution requires properly reflecting the storm’s inner core dynamics by using: (i) high-resolution models; (ii) realistic physical parameterizations. The microphysical processes and their representation in cloud-permitting models are of crucial importance. In particular, the assumed Particle Size Distribution (PSD) functions affect nearly all formulated microphysical processes and are among the most fundamental assumptions in the bulk microphysics schemes. This paper analyzes the impact of the PSD assumptions on simulated hurricanes and their synthetic radiometric signatures. It determines the most realistic, among the available set of assumptions, based on comparison to multi-parameter satellite observations. Here we simulated 2005′s category-5 Hurricane Rita using the cloud-permitting community Weather Research and Forecasting model (WRF) with two different microphysical schemes and with seven different modifications of the parametrized hydrometeor properties within one of the two schemes. We then used instrument simulators to produce satellite-like observations. The study consisted in evaluating the structure of the different simulated storms by comparing, for each storm, the calculated microwave signatures with actual satellite observations made by (a) the passive microwave radiometer that was carried by the Tropical Rainfall Measuring Mission (TRMM) satellite—the TRMM microwave imager TMI, (b) TRMM’s precipitation radar (PR) and (c) the ocean-wind-vector scatterometer carried by the QuikSCAT satellite. The analysis reveals that the different choices of microphysical parameters do produce significantly different microwave signatures, allowing an objective determination of a “best” parameter combination whose resulting signatures are collectively most consistent with the wind and precipitation observations obtained from the satellites. In particular, we find that assuming PSDs with larger number of smaller hydrometeors produces storms that compare best to observations.

Tropical cyclones (TCs) are essential for many reasons, including their destruction of human lives and property and their effect on heat and nutrient fluxes between the ocean’s surface and its depths. A better understanding of ocean fluxes is needed to predict the impact of global climate change on the oceans and to quantify how ocean heat content modulates the dynamics of global climate change. Similarly, improved modeling of nutrient fluxes is crucial for maintaining fisheries and preserving crucial marine ecosystems to benefit both humanity and marine life. Numerous remote sensors measure crucial geophysical quantities before, during, and after TCs, including sea surface temperature (SST), ocean color, chlorophyll concentration, ocean surface winds, sea surface height, and significant wave height. In this special issue, an international group of researchers have written articles describing (1) novel techniques and remote sensors for measuring the aforementioned quantities in tropical cyclones, (2) methods for validating and improving the accuracy of those measurements and harmonizing them among different sensors, (3) scientific analyses that investigate the relationships between remote-sensed ocean surface measurements and in situ measurements of vertical profiles of ocean temperature, salinity, and current, and (4) strategies for utilizing remote-sensed measurements to improve operational forecasts in order to provide better tropical cyclone warnings to human populations.

Ocean surface winds and currents are tightly coupled, essential climate variables, synoptic measurements of which require a remote sensing approach. Global measurements of ocean vector winds have been provided by scatterometers for decades, but a synoptic approach to measuring total vector surface currents has remained elusive. Doppler scatterometry is a coherent burst-scatterometry technique that builds on the long heritage of spinning pencil beam scatterometers to enable the wide-swath, simultaneous measurement of ocean surface vector winds and currents. To prove the measurement concept, NASA funded the DopplerScatt airborne Doppler scatterometer through the Instrument Incubator Program (IIP) and Airborne Instrument Technology Transition (AITT) program. DopplerScatt has successfully shown that pencil beam Doppler scatterometry can be used to form wide swath measurements of ocean winds and currents, and has increased the technology readiness level of key instrument components, including: Ka-band pulsed radar hardware, optimized scatterometer burst-mode operation, calibration techniques, geophysical model functions, and processing algorithms. With the promise and progress shown by DopplerScatt, and the importance of air-sea interactions in mind, the National Academy’s Decadal Survey has targeted simultaneous measurements of winds and currents from a Doppler scatterometer for an Earth Explorer class spaceborne mission. Besides DopplerScatt’s place as a technology stepping stone towards a satellite mission, DopplerScatt provides scientifically important measurements of ocean currents and winds (400 m resolution) and their derivatives (1 km resolution) over a 25 km swath. These measurements are enabling studies of the submesoscales and air-sea interactions that were previously impossible, and are central to the upcoming NASA Earth Ventures Suborbital-3 Submesoscale Ocean Dynamics Experiment (S-MODE). This paper summarizes the development of DopplerScatt hardware, systems, calibration, and operations, and how advances in each relate to progress towards a spaceborne Doppler scatterometer mission.

Titan is Saturn's largest moon and has a dynamic surface with methane rivers carved by an active hydrological cycle and sand seas shaped by aeolian processes. One way to study the rates of these processes is by examining Titan's impact craters, because they provide quantitative constraints on the level of degradation. With the end of the Cassini Mission in September 2017, we have reassessed the crater population using the entire Cassini Synthetic Aperture Radar (SAR) dataset, including 30 additional craters since the last assessment in 2012, for a total of 90 certain to possible impact craters on Titan. We adjust for incomplete coverage (~69%) of the moon by SAR imaging using a Monte-Carlo approach, and find no major change in Titan's inferred surface age from prior studies. We then used the SARTopo and stereo topography data sets to measure crater depths and diameters. For the first time, rim heights for twelve craters were also reported. On average, Titan's craters are shown to be shallower, with lower rims, than those observed on similarly sized icy moons (e.g. Ganymede). This suggests that the observed modification is due to a combination of sand and sediment infilling onto the crater floor and fluvial erosion of the rims with fluvial erosion playing a larger role than previously thought in crater degradation on Titan.

Tropical cyclones (TCs) are among the most destructive natural phenomena with huge societal and economic impact. They form and evolve as the result of complex multiscale processes and nonlinear interactions. Even today the understanding and modeling of these processes is still lacking. A major goal of NASA is to bring the wealth of satellite and airborne observations to bear on addressing the unresolved scientific questions and improving our forecast models. Despite their significant amount, these observations are still underutilized in hurricane research and operations due to the complexity associated with finding and bringing together semicoincident and semicontemporaneous multiparameter data that are needed to describe the multiscale TC processes. Such data are traditionally archived in different formats, with different spatiotemporal resolution, across multiple databases, and hosted by various agencies. To address this shortcoming, NASA supported the development of the Jet Propulsion Laboratory (JPL) Tropical Cyclone Information System (TCIS)—a data analytic framework that integrates model forecasts with multiparameter satellite and airborne observations, providing interactive visualization and online analysis tools. TCIS supports interrogation of a large number of atmospheric and ocean variables, allowing for quick investigation of the structure of the tropical storms and their environments. This paper provides an overview of the TCIS’s components and features. It also summarizes recent pilot studies, providing examples of how the TCIS has inspired new research, helping to increase our understanding of TCs. The goal is to encourage more users to take full advantage of the novel capabilities. TCIS allows atmospheric scientists to focus on new ideas and concepts rather than painstakingly gathering data scattered over several agencies.

Titan was a mostly unknown world prior to the Cassini spacecraft’s arrival in July 2004. We review the major scientific advances made by Cassini’s Titan Radar Mapper (RADAR) during 13 years of Cassini’s exploration of Saturn and its moons. RADAR measurements revealed Titan’s surface geology, observed lakes and seas of mostly liquid methane in the polar regions, measured the depth of several lakes and seas, detected temporal changes on its surface, and provided key evidence that Titan contains an interior ocean. As a result of the Cassini mission, Titan has gone from an uncharted world to one that exhibits a variety of Earth-like geologic processes and surface-atmosphere interactions. Titan has also joined the ranks of “ocean worlds” along with Enceladus and Europa, which are prime targets for astrobiological research.

In this letter, we discuss some observations of the Soil Moisture Active Passive (SMAP) mission’s high-resolution synthetic aperture radar (SAR) for extreme winds and tropical cyclones. We find that the L-band cross-polarized backscatter is far more sensitive to wind speed at extreme winds than the co-polarized backscatter and it is essential to observations of extreme winds with L-band SAR. We introduce a cyclone wind speed retrieval algorithm and apply it to the limited SMAP SAR dataset of cyclones. We show that the SMAP SAR instrument is capable of measuring extreme winds up to the category 5 (70 m/s) wind speed regime providing unique capabilities as compared to traditional scatterometers with C and Ku-band radars.