Arctic sea-ice thickness and salinity retrievals are simulated to explore the performance of nadir-observing microwave radiometry operating with up to 16 frequency channels in the 0.5–2-GHz frequency range. A radiative transfer model is used to create lookup tables of the circularly polarized thermal emissions of first-year (FY) and multiyear (MY) sea ice, and the performance of two distinct retrieval methods is examined. The first method retrieves only sea-ice thicknesses, while the second retrieves both ice thickness and ice salinity. Retrieval errors are simulated for both FY and MY sea ice as a function of ice thickness, salinity, and temperature to investigate the impact of radiometric uncertainty, the frequency channels used, and any errors in ancillary information. To gain further insight into Arctic scale retrieval performance, a simulated brightness temperature dataset is produced for Arctic sea ice for the period October 2020–March 2021 using sea-ice thicknesses from the SMOS-CryoSat-2 algorithm. Compared to existing sea-ice thickness retrievals obtained from 1.4-GHz microwave radiometers, the results demonstrate that 0.5–2-GHz radiometry can achieve higher sensitivity to a sea-ice thickness within the range 0.5–1.5 m for FY sea ice and enable the retrieval of multiple sea-ice parameters (thickness and salinity) simultaneously.

The Snow Microwave Radiative Transfer (SMRT) model was initially developed to explore the sensitivity of microwave scattering to snow microstructure for active and passive remote sensing applications. Here, we discuss the modular design of SMRT that has enabled its rapid extension by the community. SMRT can now represent a layered medium consisting of snow, land ice, lake ice and/or sea ice overlying a substrate of soil, water or parameterized by reflectivity. A time-dependent radiative transfer solution method has also been added to allow for low resolution mode altimetry applications. We illustrate the use of SMRT to simulate brightness temperature for snow on lake ice, backscatter for snow on soil and altimeter waveforms for snow on sea ice.

A tri-frequency microwave synthetic aperture radar (SAR), designed for the estimation of snow water equivalent (SWE), was recently developed as part of the SWESARR (Snow Water Equivalent Synthetic Aperture Radar and Radiometer) instrument. The SAR operates at 9.65 GHz, 13.6 GHz, and 17.25 GHz, and at two polarizations (VV, VH), with a nominal bandwidth of 140 MHz. The SAR was first flight tested in December 2018 and later, along with SWESARR's radiometer, participated in the SnowEx science flight campaign in the fall 2019. During these flights, the SAR collected comprehensive data sets of a variety of terrains, including calibration sites where several trihedral corner reflectors had been deployed. Analysis of these data sets indicated the radar performed according to the design specifications.

A microwave emissivity retrieval is applied to five years of global precipitation measurement (GPM) microwave imager (GMI) observations over land and sea ice. The emissivities are colocated with GPM's dual-frequency precipitation radar (DPR) surface backscatter measurements in clear-sky conditions. The emissivity-backscatter database is used to characterize surfaces within the GPM orbit for precipitation retrieval algorithms and other applications. The full 10-166-GHz emissivity vector is retrieved using optimal estimation. Since GMI includes water vapor sounding channels, retrieval of the atmospheric and surface states are performed simultaneously. Using the MERRA2 reanalysis as the a priori atmospheric state and with proper characterization of its error, we are able to effectively screen for cloud- and precipitation-affected emissivities. Comparisons with colocated CloudSat data show that this GMI-based screen is able to detect precipitation that DPR alone does not; however, about 10% of precipitation occurrence from CloudSat is still undetected by GMI. The unsupervised Kohonen classification technique was then applied to multiyear monthly 0.25° gridded mean retrieved emissivities and backscatter distinctly for snow-free, snow-covered, and sea ice surfaces in order to classify surfaces based on both active and passive microwave characteristics. The classes correspond to vegetation coverage and type, inundation zones, soil composition, and terrain roughness. Snow and sea ice surfaces show clear seasonal cycles representing the increase in snow and ice spatial extent and reduction in the spring. Applications toward GPM precipitation retrieval algorithms and sensitivity to accumulated rain and snowfall are also explored.

A new airborne synthetic aperture radar (SAR) system was recently developed for the estimation of snow water equivalent (SWE). The radar is part of the SWESARR (Snow Water Equivalent Synthetic Aperture Radar and Radiometer) instrument, an active passive microwave system specifically designed for the accurate estimation of SWE. The dual polarization (VV, VH) radar operates at three frequency bands (9.65 GHz, 13.6 GHz, and 17.25 GHz), with bandwidths of up to 200 MHz. The radar flew its first flight campaign in November 2019, along with SWESARR's -already operational - radiometer. The radar collected comprehensive data sets over various terrains that show a successful system performance. The instrument is slated to participate in future SnowEx campaigns.

Seasonal snow cover plays a key role in freshwater resources, water security, natural hazards, and weather and climate. However, accurate estimation of snow-water equivalent (SWE) with remote sensing observations remains a significant challenge. NASA Terrestrial Hydrology Program launched its multi-year SnowEx mission whose primary goal is to develop and test techniques for estimating how much water is stored in some complex Earth's terrestrial snow-covered regions (e.g. forested areas). The first year of the 5-year campaign took place in Colorado during the winter 2016–2017, during which in situ measurements and ground-based remote sensing observations were collected by the scientific community. Throughout February 2017, about 100 people were deployed and over 30 remote sensing instruments were used. This required an exceptional coordination effort, which resulted in collocated in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements) with ground-based remote sensing observations (microwave radiometers, radar, scatterometers, lidars, etc.). The public release of all these datasets has started (nsidc.org/data/snowex).

Rain-on-snow (ROS) events have been the focus of numerous studies in the past five years. Their characteristics (frequency, extent, and duration) represent a new and relevant climate indicator. However, monitoring ROS occurrences remotely using satellite observations is deemed challenging. The ROS events can be sporadic, of very different intensities, and the outcome of the rain water uncertain (either it freezes in the snow cover or runs off). Using passive and active microwave remote sensing observations, our study proposes new approaches to monitor the occurrence of ROS events. Specifically, we utilize observations from Advanced Microwave Scanning Radiometer 2 (AMSR2), and Global Precipitation Measurements (GPM) Microwave Imager (GMI), and GPM Dual-frequency Precipitation Radar (DPR). We compare our ROS detection against weather stations and recently published algorithms using a different set of microwave frequencies.

In this study, we used an artificial neural network method to estimate the fractional snow cover area (fSCA), which is fast and accurate, and that can be easily adapted to different remote sensing instruments. We tested our approach using SnowEx data from NASA's Cloud Absorption Radiometer (CAR) over Grand Mesa; one of the largest flat-topped mountains in the world, which features sufficient forested stands with a range of density and height (and a variety of other forest conditions); a spread of snow depth/snow water equivalent conditions over sufficiently flat snow-covered terrain. The retrieved fractional snow-covered area from CAR compares reasonably with a Sentinel-2 image over the same location and demonstrates CAR's unique capability to improve the retrieval of snow properties using machine learning. The retrieved snow fraction parameter from our method is expected to minimize the error associated with the traditional binary snow detection scheme, and improve the retrieval quality of key parameters such as surface albedo.

The first NASA SnowEx campaign was held in February 2017 over Grand Mesa, Colorado, covering both open and forested areas. The Belspo SNOPOST project aims at using the collected SnowEx data to enhance snow estimates using the Level 1 remote sensing data together with land surface modeling and to document the limitations of snow remote sensing where needed. A preliminary spatiotemporal analysis of in situ and satellite remote sensing data, and modeling estimates of snow will be presented.

Snow-covered surface passive microwave surface-based radiometer (SBR) observations were taken over 50 locations during the NASA SnowEx 2017 campaign. Several black body measurements done during the campaign show that the mean absolute errors (MAE: absolute difference between the radiometer measurements and measured black body physical temperature) of all measurements are about 1 K and the bias are lower than 1 K. Continuous observations during surface melt episodes show a strong increase in TB at higher frequencies (37 and 89 GHz) due to liquid water formation in the top snow layer. The increase also significant at lower frequencies (11 and 19) but slower due to the higher emission depth.

NASA SnowEx's goal is estimating how much water is stored in Earth's terrestrial snow-covered regions. To that end, two fundamental questions drive the mission objectives: (a) What is the distribution of snow-water equivalent (SWE), and the snow energy balance, among different canopy and topographic situations?; and (b) What is the sensitivity and accuracy of different SWE sensing techniques among these different areas? In situ, ground-based and airborne remote sensing observations were collected during winter 2016–2017 in Colorado to provide the scientific community with data needed to work on these key questions. An intensive period of observations occurred in February 2017 during which over 30 remote sensing instruments were used. Their observations were coordinated with in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements). Both remote sensing and in situ data will be archived and publicly distributed by the National Snow and Ice Data Center at nsidc.org/data/snowex.

The overall reduction in sea ice thickness may have profound impact on the Earth's climate system, but currently predictive models do not have consistent sea ice thickness datasets available for improving their results through proper initialization and assimilation. We will present a novel approach to retrieve Arctic sea ice thickness from satellite observations. Our approach offers hemispheric scale sea ice thickness and volume at a temporal resolution shorter than a month (from days to weeks) by utilizing altimetry-derived thickness and passive microwave observations. Our method is validated against the airborne Operation IceBridge measuremnts. Moreover, as an application, we will show the gain obtained when assimilating our basin wide Arcitc sea ice thickness in the NASA Goddard Earth Observing System Model, version 5 (GEOS-5) ocean sea-ice data assimilation system.

L-band radiometers are used to retrieve sea surface salinity (SSS) and study the cryosphere. At high latitudes, it is frequent that both sea ice and water are present in the sensor's field of view. Accurately characterizing the scene is crucial for oceanographic and cryospheric applications. We present a model to derive the ice fraction affecting observations by the Aquarius and SMAP radiometers and discuss the impact of different types of ice on observed brightness temperatures. Our model shows improved match between brightness temperatures and ice fraction when compared to the model used previously in the Aquarius products. We will report on the feasibility and expected accuracy for an ice correction algorithm in the SSS retrieval algorithm.

The spatial and temporal distribution of ROS across the Canadian Arctic Archipelago (CAA) remains poorly understood owing to their sporadic nature in time and space. In this study, we highlight the distribution and evolution of ROS occurrences inventoried since 1984 at 14 Environment and Climate Change Canada (ECCC) weather stations in the CAA. We introduce an adaptation of the detection algorithm proposed by Dolant et al., (2016), to investigate spatio-temporal patterns in occurrence. Across the 14 weather stations, more than 600 ROS events were identified since 1984, 80% of which occurred during the Spring season.

SnowEx is a multi-year airborne snow campaign with the primary goal of addressing the question: How much water is stored in Earth's terrestrial snow-covered regions? Year 1 (2016-17) focused on the distribution of snow-water equivalent (SWE) and the snow energy balance in a forested environment. The year 1 primary site was Grand Mesa and the secondary site was the Senator Beck Basin, both in western, Colorado, USA. Nine sensors on five aircraft made observations using a broad range of sensing techniques-active and passive microwave, and active and passive optical/infrared - to determine the sensitivity and accuracy of these potential satellite remote sensing techniques, along with models, to measure snow under a range of forest conditions. SnowEx also included an extensive range of ground truth measurements - in-situ manual samples, snow pits, ground based remote sensing measurements, and sophisticated new techniques. A detailed description of the data collected will be given and some preliminary results will be presented.

Radiometers operating at L-band (1.4 GHz) are used to retrieve sea surface salinity over ice-free oceans and have been used recently to study the cryosphere. One hindrance of their use in the high latitudes is the preponderance of mixed scenes, where seawater and sea ice are both present in the sensor's field of view (FOV). Accurately characterizing the scene is crucial for oceanographic and cryospheric applications. To that end, a sea ice fraction model, composed of passive microwave sea ice concentration retrievals and an instrument simulator that integrates radiative power coming from all around the antenna, is used. We investigate the model currently used operationally to derive the ice fraction affecting the Aquarius observations and show that it can be significantly improved. On the one hand, the current model tends to overestimate sea ice fraction in the marginal ice zone where observations are used for salinity retrievals. On the other hand, the current model underestimates ice fraction within the ice pack where observations are used to derive sea ice properties. For the northern hemisphere, we also find evidence of the sea ice type impact on L-band radiometric observations. We present a model to derive sea ice fractions that are in better agreement with Aquarius radiometric observations using the Advanced Microwave Scanning Radiometer 2 Bootstrap algorithm for sea ice concentration and using high-resolution integration over the sensor's FOV.

Satellite microwave radiometers are widely used to estimate sea ice cover properties (concentration, extent, and area) through the use of sea ice concentration (IC) algorithms. Rare are the algorithms providing associated IC uncertainty estimates. Algorithm uncertainty estimates are needed to assess accurately global and regional trends in IC (and thus extent and area), and to improve sea ice predictions on seasonal to interannual timescales using data assimilation approaches. This paper presents a method to provide relative IC uncertainty estimates using the enhanced NASA Team (NT2) IC algorithm. The proposed approach takes advantage of the NT2 calculations and solely relies on the brightness temperatures (TBs) used as input. NT2 IC and its associated relative uncertainty are obtained for both the Northern and Southern Hemispheres using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) TB. NT2 IC relative uncertainties estimated on a footprint-by-footprint swath-by-swath basis were averaged daily over each 12.5-km grid cell of the polar stereographic grid. For both hemispheres and throughout the year, the NT2 relative uncertainty is <;5%. In the Southern Hemisphere, it is low in the interior ice pack, and it increases in the marginal ice zone up to 5%. In the Northern Hemisphere, areas with high uncertainties are also found in the high IC area of the Central Arctic. Retrieval uncertainties are greater in areas corresponding to NT2 ice types associated with deep snow and new ice. Seasonal variations in uncertainty show larger values in summer as a result of melt conditions and greater atmospheric contributions. Our analysis also includes an evaluation of the NT2 algorithm sensitivity to AMSR-E sensor noise. There is a 60% probability that the IC does not change (to within the computed retrieval precision of 1%) due to sensor noise, and the cumulated probability shows that there is a 90% chance that the IC varies by less than ±3%. We also examined the daily IC variability, which is dominated by sea ice drift and ice formation/melt. Daily IC variability is the highest, year round, in the MIZ (often up to 20%, locally 30%). The temporal and spatial distributions of the retrieval uncertainties and the daily IC variability is expected to be useful for algorithm intercomparisons, climate trend assessments, and possibly IC assimilation in models.

The Antarctic Plateau presents ideal characteristics to study the relationship between microwave observations and snow/ice properties. It is also a promising target for radiometer calibration and sensor intercalibration, which are critical for applications requiring subkelvin accuracy, such as sea surface salinity retrievals. This paper presents the spaceborne Aquarius L-band radiometric observations collected since August 2011 over the Antarctic Plateau, and it focuses on their temporal evolutions at Dome C (75.1° S, 123.35° E). Aquarius operates three radiometers with a sensitivity of 0.15 K (over the oceans), allowing us to analyze small variations in brightness temperature (TB) and changes with incidence angles. Over the Antarctic Plateau, Aquarius TBs have a relatively low annual standard deviation (0.2-0.9 K) where melting never occurs. However, the analysis of the TB time series at Dome C revealed significant variations (up to 2.5 K) in summer. First, these variations are compared with a remote sensing grain index (GI) based on high-frequency (89 and 150 GHz) shallow-penetration TB channels. Variations in the ratio of TBs observed at horizontal and vertical polarizations are synchronous with GI changes. Second, Aquarius TB variations are compared with the presence of hoar crystals on the surface identified using surface-based near-infrared photographs. The largest and longest changes in TBs correspond to periods with hoar crystals on the surface. Therefore, in spite of the deep penetration of the L-band radiation, evolutions of the snow properties near the surface, which usually change rapidly and irregularly, do influence L-band observations. Collection of accurate snow surface measurements and thorough analyses of the L-band observations are thus needed to use the Antarctic Plateau as a calibration/inter-calibration target.

We propose a new method for joint segmentation of monotonously growing or shrinking shapes in a time sequence of noisy images. The task of segmenting the image time series is expressed as an optimization problem using the spatio-temporal graph of pixels, in which we are able to impose the constraint of shape growth or of shrinkage by introducing monodirectional infinite links connecting pixels at the same spatial locations in successive image frames. The globally optimal solution is computed with a graph cut. The performance of the proposed method is validated on three applications: segmentation of melting sea ice floes and of growing burned areas from time series of 2D satellite images, and segmentation of a growing brain tumor from sequences of 3D medical scans. In the latter application, we impose an additional intersequences inclusion constraint by adding directed infinite links between pixels of dependent image structures.

We propose a new method based on graph cuts for joint segmentation of monotonously growing or shrinking shapes in time series of noisy images. By introducing directed infinite links connecting pixels at the same spatial locations in successive image frames, we impose shape growth/shrinkage constraint in graph cuts. Minimization of energy computed on the resulting graph of the image sequence yields globally optimal segmentation. We validate the proposed approach on two applications: segmentation of melting sea ice floes from a time series of multimodal satellite images and segmentation of a growing brain tumor from sequences of 3D multimodal medical scans. In the latter application, we impose an additional inter-sequences inclusion constraint by adding directed infinite links between pixels of dependent image structures.

The melting of sea ice is correlated to increases in sea surface temperature and associated climatic changes. Therefore, it is important to investigate how rapidly sea ice floes melt. For this purpose, a new TempoSeg method for multitemporal segmentation of multiyear ice floes is proposed. The microwave radiometer is used to track the position of an ice floe. Then, a time series of MODIS images are created with the ice floe in the image center. A TempoSeg method is performed to segment these images into two regions: Floe and Background. First, morphological feature extraction is applied. Then, the central image pixel is marked as Floe, and shape-constrained best merge region growing is performed. The resulting two-region map is post-filtered by applying morphological operators. We have successfully tested our method on a set of MODIS images and estimated the area of a sea ice floe as a function of time.

A comparison of snow depths on sea ice was made using airborne altimeters and an Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) simulator. The data were collected during the March 2006 National Aeronautics and Space Administration (NASA) Arctic field campaign utilizing the NASA P-3B aircraft. The campaign consisted of an initial series of coordinated surface and aircraft measurements over Elson Lagoon, Alaska and adjacent seas followed by a series of large-scale (100 km × 50 km) coordinated aircraft and AMSR-E snow depth measurements over portions of the Chukchi and Beaufort seas. This paper focuses on the latter part of the campaign. The P-3B aircraft carried the University of Colorado Polarimetric Scanning Radiometer (PSR-A), the NASA Wallops Airborne Topographic Mapper (ATM) lidar altimeter, and the University of Kansas Delay-Doppler (D2P) radar altimeter. The PSR-A was used as an AMSR-E simulator, whereas the ATM and D2P altimeters were used in combination to provide an independent estimate of snow depth. Results of a comparison between the altimeter-derived snow depths and the equivalent AMSR-E snow depths using PSR-A brightness temperatures calibrated relative to AMSR-E are presented. Data collected over a frozen coastal polynya were used to intercalibrate the ATM and D2P altimeters before estimating an altimeter snow depth. Results show that the mean difference between the PSR and altimeter snow depths is -2.4 cm (PSR minus altimeter) with a standard deviation of 7.7 cm. The RMS difference is 8.0 cm. The overall correlation between the two snow depth data sets is 0.59.