We demonstrate that spatial maps of significant wave height (SWH) with kilometric resolutions can be derived from the data acquired by the Ka-band radar interferometer (KaRIn) instrument onboard the surface water ocean topography (SWOT) mission by exploiting the measured interferometric decorrelation. We discuss the sensitivity to errors in the volumetric decorrelation estimates and show that a successful inversion of SWH, particularly in the outer part of KaRIn’s swath and for low values of SWH, requires factoring out all sources of decorrelation of instrumental origin to an exquisite precision. We then validate KaRIn’s SWH measurement against independent data, namely, GPS buoys, airborne LiDAR, Sentinel3, SWOT’s nadir altimeter, and the ECMWF global wave model. We show that biases between KaRIn and the other sensors are centimetric and that KaRIn is able to capture features in the 2-D SWH field of only a few kilometers. While KaRIn’s SWH measurement error is difficult to fully characterize due to the absence of 2-D ground-truth data valid at such fine spatial scales and spanning a wide range of sea states, we argue that the retrieved fields are dominated by signal rather than noise, except possibly in the last few kilometers of the swath at low SWH. We briefly discuss the implications in terms of advancing our understanding of the phenomena that shape the wave fields at small scales. The algorithm and calibration described in this article will be the basis for version D of the operational SWOT products.

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.

We find that the multilook phase noise associated with geometric decorrelation in a synthetic aperture radar interferogram does not follow the spectral shape of the signal in the range dimension because the phase noise arises from the uncorrelated ends of the frequency bands of the single-channel radar data contributing to the interferogram. The phase noise due to geometric decorrelation therefore diminishes much more quickly than the square root of the number of looks in range when the number of looks is large, and it is thus not well characterized by the common practice of computing the multilook phase noise from the Cramer-Rao bound as a function only of the total coherence and the total number of looks. Large discrepancies may result in cases for which the phase noise due to geometric decorrelation dominates, such as when simulating data with no receiver thermal noise. We present here a more accurate model of the multilook phase noise due to geometric decorrelation in addition to thermal noise. Our model depends on the spectra of the individual channels and on the spectrum of the multilook averaging window in range. Use of this model may have bearing on decisions of whether common-band filtering is necessary for particular applications.

The Soil Moisture Active-Passive (SMAP) L-band microwave radiometer is a conical scanning instrument designed to measure soil moisture with 4% volumetric accuracy at 40-km spatial resolution. SMAP is NASA's first Earth Systematic Mission developed in response to its first Earth science decadal survey. Here, the design is reviewed and the results of its first year on orbit are presented. Unique features of the radiometer include a large 6-m rotating reflector, fully polarimetric radiometer receiver with internal calibration, and radio-frequency interference detection and filtering hardware. The radiometer electronics are thermally controlled to achieve good radiometric stability. Analyses of on-orbit results indicate that the electrical and thermal characteristics of the electronics and internal calibration sources are very stable and promote excellent gain stability. Radiometer NEDT1 MHz and 1/f noise rising at longer time scales fully captured by the internal calibration scheme. Results from sky observations and global swath imagery of all four Stokes antenna temperatures indicate that the instrument is operating as expected.

The Soil Moisture Active Passive (SMAP) mission launched on Jan 31, 2015. The mission employs L-band radar and radiometer measurements to estimate soil moisture with 4\% volumetric accuracy at a resolution of 10 km, and freeze-thaw state at a resolution of 1-3 km [1]. Immediately following launch, there was a three month instrument checkout period, followed by six months of level 1 (L1) calibration and validation. A beta release of L1 radar data was available on July 1, 2015 and a validated release was made available starting on November 1, 2015. Work continued on L1 radar calibration and validation for several more months to improve the quality of the final product due to be released with the L2 validated release. In this presentation, we will discuss the radar processing algorithms and the calibration and validation activities for the L1 radar data.

The Soil Moisture Active-Passive (SMAP) mission will launch in late 2014 and will carry a combined L-band radiometer/radar instrument for the retrieval of global soil moisture and surface freeze-thaw state. Radio frequency interference (RFI) is a known challenge for Earth remote sensing in the L-band portion of the spectrum. This paper addresses efforts to characterize and mitigate RFI for the SMAP radar. A model for the RFI environment due to surface-based emitters is developed, and is shown to agree well with the observations of currently operating L-band radar systems. An analysis of the environment due to space-based emitters is also presented. Techniques to mitigate RFI in the radar band are described, and are shown to perform sufficiently well to meet the stringent SMAP measurement requirements. A companion paper addresses the different issues encountered with RFI in the radiometer band.

The NASA Soil Moisture Active and Passive (SMAP) mission will measure soil moisture with a combination of L-band radar and radiometer measurements. We present an assessment of the expected impact of radio frequency interference (RFI) on SMAP performance, incorporating projections based on recent data collected by the Aquarius and SMOS missions. We discuss the impacts of RFI on the radar and radiometer separately given the differences in (1) RFI environment between the shared radar band and the protected radiometer band, (2) mitigation techniques available for the different measurements, and (3) existing data sources available that can inform predictions for SMAP.

We propose a technique for mitigating tropospheric phase errors in repeat-pass interferometric synthetic aperture radar (InSAR). The mitigation technique is based upon the acquisition of multisquint InSAR data. On each satellite pass over a target area, the radar instrument will acquire images from multiple squint (azimuth) angles, from which multiple interferograms can be formed. The diversity of viewing angles associated with the multisquint acquisition can be used to solve for two components of the 3D surface displacement vector as well as for the differential tropospheric phase. We describe a model for the performance of the multisquint technique, and we present an assessment of the performance expected

Observational architectures for allowing the eventual forecasting of earthquake are discussed. Current science requirements suggest that L-band InSAR systems with short repeat periods would be best suited to such measurements. Constellations of such sensors in orbits around 2000-5000 km altitude might provide optimal Earth coverage for interferometry, while higher orbits around 10,000 - 40,000 km might approach the goal of around-the-clock for disaster-response applications.

Fine temporal sampling is essential for disaster management, e.g. of flooding, fires, landslides, hurricanes, and earthquakes. A powerful technique for mapping such natural hazards is synthetic aperture radar (SAR) interferometry, providing displacement measurements at the subwavelength scale and decorrelation estimates. Pre-seismic deformation, one of the most elusive aspects of earthquakes, will require much finer temporal sampling than present InSAR capabilities provide. Observations taken every few hours could provide time series data of rapidly evolving phenomena, such as pre-eruptive volcano dynamics, leading to major advances in predictive capability, improving the potential for modeling as well as for civil protection. Such radical performance improvements could be attained through large constellations of conventional low Earth orbit (LEO) satellites or small constellations of geosynchronous SARs. The unique capability of a geosynchronous SAR in terms of instantaneously accessible area is contrasted with the requirements for huge electronically steered array (ESA) antennas. The optimal approach is very much dependant on technological developments, in particular geosynchronous SAR depends on the development of affordable very large ESA antennas, but also other technological developments will be required.

Two-dimensional (2-D) phase unwrapping is a key step in the analysis of interferometric synthetic aperture radar (InSAR) data. While challenging even in the best of circumstances, this problem poses unique difficulties when the dimensions of the interferometric input data exceed the limits of one's computational capabilities. In order to deal with such cases, we propose a technique for applying the statistical-cost, network-flow phase-unwrapping algorithm (SNAPHU) of Chen and Zebker (2001) to large datasets. Specifically, we introduce a methodology whereby a large interferogram is partitioned into a set of several smaller tiles that are unwrapped individually and then divided further into independent, irregularly shaped reliable regions. These regions are subsequently assembled into a full unwrapped solution, with the phase offsets between regions computed in a secondary optimization problem whose objective is to maximize the a posteriori probability of the final solution. As this secondary problem assumes the same statistical models as employed in the initial tile-unwrapping stage, the technique results in a solution that approximates the solution that would have been obtained had the full-size interferogram been unwrapped as a single piece. The secondary problem is framed in terms of network-flow ideas, allowing the use of an existing nonlinear solver. Applying the algorithm to a large topographic interferogram acquired over central Alaska, we find that the technique is less prone to unwrapping artifacts than more simple tiling approaches.