Seasonal snowpack is a crucial water resource, making accurate Snow Water Equivalent (SWE) estimation essential for water management and environmental assessment. This study introduces a novel approach to Passive Microwave (PMW) SWE estimation, leveraging the strong, unexpected correlation between SWE and the Spatial Standard Deviation (SSD) of PMW Calibrated Enhanced-Resolution Brightness Temperatures (CETB). By integrating spatial statistics, linear correlation, machine learning (Linear Regression, Random Forest, GBoost, and XGBoost), and SHapley Additive exPlanations (SHAP) analysis, this research evaluates CETB SSD as a key feature to improve SWE estimations or other environmental retrievals by investigating environmental drivers of CETB SSD. Analysis at three sites—Monument Creek, AK; Mud Flat, ID; and Jones Pass, CO—reveals site-specific SSD variability, showing correlations of 0.64, 0.82, and 0.72 with SNOTEL SWE, and 0.67, 0.89, and 0.67 with PMW-derived SWE, respectively. Among the sites, Monument Creek exhibits the highest ML model accuracy, with Random Forest and XGBoost achieving test R2 values of 0.89 and RMSEs ranging from 0.37 to 0.39 [K] when predicting CETB SSD. SHAP analysis highlights SWE as the driver of CETB SSD at Monument Creek and Mud Flat, while soil moisture plays a larger role at Jones Pass. In snow-dominated regions with less surface heterogeneity, such as Monument Creek, SSDs can improve SWE estimation by capturing snow spatial variability. In complex environments like Jones Pass, SSDs aid SWE retrievals by accounting for factors such as soil moisture that impact snowpack dynamics. PMW SSDs can enhance remote sensing capabilities for snow and environmental research across diverse environments, benefiting hydrological modeling and water resource management.

Conventional near-surface vector wind (VW) estimates from microwave scatterometers degrade in quality in the presence of rain because the standard wind-only (WO) geophysical model function (GMF) does not model rain effects. The WO GMF maps the noise-free normalized radar cross-section (sigma(degrees)) to the near-surface VW but omits other geophysical phenomena, such as rain rate (R). Previous studies have developed simultaneous wind/rain (SWR) GMFs that map sigma(degrees) to the near-surface VW and R to account for rain effects [1]. This article develops an SWR GMF for the Ku-band OceanSat-2 Scatterometer (OSCAT) using a sigma(degrees), VW, and R triple collocated database. OSCAT sigma(degrees) measurements are collocated with Tropical Rain Measuring Mission (TRMM) near-surface R, and European Centre for Medium-Range Weather Forecasts (ECMWFs) numerical weather prediction near-surface VW product creating the OSCAT, TRMM, and ECMWF Database (OTED). Four novel approaches for creating an SWR GMF are discussed and the most accurate GMF is found to be the path integrated attenuation method. The estimate accuracy is analyzed using the OTED, which reveals that the SWR wind speed estimate accuracy is between 0.25 and 0.7 m/s more accurate (depending on the rain rate) on average compared to WO speed estimates. However, wind direction estimates are not improved by SWR with WO outperforming the SWR estimates by between 1(degrees) and 10(degrees) (depending on the sigma(degrees )spatial resolution).

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In multilateration, distances between an unknown location and several known points are used to determine the location of the unknown point. Papers on algorithms for multilateration, such as time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA), have proliferated in the literature. These use the same equations, but are often written in a way that hides the underlying geometry and make it difficult to see the similarity in approaches between different papers. This paper provides a review of TOA and AOA-based localization approaches, with a focus on linear positioning algorithms. This paper presents simple and consistent versions of TOA, TDOA, and AOA position equations with intuitive explanations for how those equations relate to the geometry of the localization problem. This perspective can make it easier to implement these algorithms in a way that produces stable results and provides insight into the underlying geometric problem. We also review the literature relating to TDOA and AOA localization algorithms and multistatic radar.

Detailed information on locations (Table S1) and sediment descriptions (Table S2) of the 1999 sediment gravity cores, overviews of major cation pore-water chemistry analysis procedures and quality assurance/quality control protocols (Tables S3 and S4), and additional information on sources and modifications to the generalized regional bedrock geology compilation (Table S5); figures depicting calcium versus sodium variations in pore-water samples (Fig. S1) and depicting sediment descriptions and corresponding changes in pore-water sodium concentrations (Fig. S2); and data tables of pore-water composition analyses (chloride, bromide, and major cations) for the Lake Michigan (Data S1 and S2) and Saginaw Bay (Lake Huron) (Data S3) samples presented in the paper.

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.

The MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) includes conventional- and enhanced-resolution radiometer brightness temperature (TB) images on standard, compatible grids from calibrated satellite radiometer measurements collected over a multi-decade period. Recently, the CETB team processed the first 4 years of enhanced resolution Soil Moisture Active Passive (SMAP) L-band (1.41 GHz) radiometer TB images. The CETB processing employs the radiometer form of the Scatterometer Image Reconstruction (rSIR) algorithm to create enhanced resolution images, which are posted on fine resolution grids. In this paper, we evaluate the effective resolution of the SMAP TB image products using coastline and island crossings. We similarly evaluate the effective resolution of the SMAP L1C_TB_E enhanced resolution product that is based on Backus-Gilbert processing. We present a comparison of the spatial resolution of the rSIR and L1C_TB_E enhanced resolution products with conventionally-processed (gridded) SMAP data. We find that the effective resolution of daily CETB rSIR SMAP TB images is slightly finer than that of L1C_TB_E and about 30% finer than conventionally processed data.

AbstractDuring Cook's 1772–75 Antarctic circumnavigation on the HMS Resolution, he recorded the positions of hundreds of icebergs. This paper compares Cook's observations and those of Halley in 1700, Bouvet in 1739 and Riou in 1789, with the Brigham Young University/National Ice Center (BYU/NIC) and the Alfred Wegener Institute datasets. Cook's description of the iceberg plume east of the Amery Ice Shelf and the iceberg distributions in the Weddell, Ross and Amundsen Seas agree with modern data. In January 1774, Cook reached his farthest south on the shelf of the Amundsen Sea Embayment, the site of the current International Thwaites Glacier Collaboration field study. Cook's largest iceberg had a 2.5 km diameter, where power-law models show that icebergs of this size or smaller comprise 92% of their total number. In the eastern Weddell, Cook's observation of a sea-ice tongue with a much greater extent than in satellite imagery remains unexplained. Although Riou's icebergs lie 1000 km east of the BYU/NIC trajectories, application of the England and others (2020) fracture and drift model to the trajectories removes the discrepancy and means that all the ship observations are consistent with modern observations and theory.

We use enhanced-resolution L-band radar backscatter and brightness temperature image time series generated from observations collected by NASA’s Soil Moisture Active Passive (SMAP) mission to map the extent and physical characteristics of an expansive perennial firn aquifer recently identified in the Wilkins Ice Shelf, Antarctic Peninsula. Empirical algorithms to map extent are derived from a continuous logistic model. Distinct physical characteristics of the firn overlying the perennial firn aquifer are distinguished using images of the slope in radar backscatter versus incidence angle. An inverted two-layer L-band brightness temperature model parameterized by the optical thickness is used to map seasonal firn saturation. Our results demonstrate the potential for using low-frequency L-band satellite microwave missions to map large englacial meltwater reservoirs in the firn of Antarctic ice shelves that may trigger hydrofracture and ice shelf instability year-round.

Scatterometers are designed to measure the normalized radar backscatter (σ o ) of the ocean surface. Wind-only Geophysical Model Functions (GMF) are used to relate the σ o to the near surface Wind Vector (WV), wind direction and speed. Rain alters the relationship between WV and σ o captured in the wind-only GMF. Making a GMF that encapsulates the rain effects on σ o lowers the error between retrieved WV and actual measured WV.The Ocean Scatterometers (OSCAT-1 and -2) are a Ku-band dual beam scatterometers that operated from 2009-2021. To create a rain GMF OSCAT is collocated with Tropical Rain Measurement Mission (TRMM) rain measurements and European Centre for Medium-Range Weather Forecast (ECMWF) model winds. The rain GMF is created using a simple phenomenological rain model that relates σ o in the presence of rain to surface wind-induced roughness. The Simultaneous Wind and Rain (SWR) algorithm uses the conventional wind-only GMF injunction with the rain GMF to estimate WV from σ o .

Existing multilateration algorithms typically do not account for information that is previously known about the target’s position. This paper presents a method of using an a priori estimate of the target’s location and planar approximations of the spheres to determine the target’s position. This algorithm meets or exceeds the performance of traditional multilateration algorithms when the a priori position is accurate enough. It significantly out-performs traditional multilateration in wide-area multilateration scenarios, particularly in the typical case where the reference points are nearly coplanar.

Long-term studies of climate change can benefit from a retrospective analysis of past satellite missions. To facilitate such studies, in this paper we describe two new radar backscatter (σ o ) products derived from the Seasat-A Scatterometer System (SASS) and the NASA Scatterometer (NSCAT). Operating at Ku-band (14.6 and 13.995 GHz, respectively), the two sensors provided global σ o measurements in 1978 and 1996-1997 and are unique benchmarks for studying long-term climate change, particularly over the polar ice sheets. The products are compatibly gridded to be consistent with other existing climate data record products of σ o and microwave brightness temperature (T B ) that span multiple decades.

While the influence of surface melt on Antarctic ice shelf stability can be large, the duration and affected area of melt events are often small. Therefore, melt events are difficult to capture with remote sensing, as satellite sensors always face the trade-off between spatial and temporal resolution. To overcome this limitation, we developed UMelt: a surface melt record for all Antarctic ice shelves with a high spatial (500 m) and high temporal (12 h) resolution for the period 2016–2021. Our approach is based on a deep learning model, specifically a U-Net, which was developed in Google Earth Engine. The U-Net combines microwave remote sensing observations from three sources: Sentinel-1, Special Sensor Microwave Imager/Sounder (SSMIS), and Advanced Scatterometer (ASCAT). The U-Net was trained on the Shackleton Ice Shelf for melt seasons 2017–2021, using the fine-scale melt patterns of Sentinel-1 as reference data and SSMIS, ASCAT, a digital elevation model, and multi-year Sentinel-1 melt fraction as predictors. The trained U-Net performed well on the Shackelton Ice Shelf for test melt season 2016–2017 (accuracy: 91.3%; F1-score: 86.9%), and the Larsen C Ice Shelf, which was not considered during training (accuracy: 91.0%; F1-score: 89.3%). Using the trained U-Net model, we have successfully developed the UMelt record. UMelt allows Antarctic-wide surface melt to be detected at a small scale while preserving a high temporal resolution, which could lead to new insights into the response of ice shelves to a changing atmospheric forcing.

Knowing the location of polar bears (Ursus maritimus) in their winter dens is crucial for minimizing disturbance during this critical period in their life cycle. Previous research has used Forward Looking Infrared (FLIR) technology to detect bear dens but has only achieved a detection accuracy of 45% for single flights. The thermal nature of FLIR means that some bears are never detected nor are detectable using FLIR. In this paper we explore the use of Synthetic Aperture Radar (SAR) as an alternative polar bear detection technology in a simple pilot study in Churchill, Manitoba, Canada, during October 2021. In this experimental study, we focused on the detection of polar bears on the surface in the SAR images. The result of this study can inform future efforts to proceed to den-detection experiments. In this study, we achieved a polar bear identification accuracy of 66%, albeit with a small sample size. Many of the challenges we encountered involved low signal-to-noise ratios and imprecise flight paths. Concurrent research from other parties shows that neural networks and other machine learning techniques can overcome these challenges to some degree, suggesting that SAR may be a promising candidate to become an effective tool for polar bear detection, particularly when coupled with other sensors such as FLIR.

Current and past scatterometers provide useful measurements of vector winds, vegetation, and ice that span multiple decades, with measurements collected at Ku-, C-, and most recently, L-band. To support long-term climate studies cross-calibration of the sensors is important. The multiple frequency bands complicate intersensor calibration. Using a simple scattering model, we evaluate the use of wind glazed regions in Antarctica as a transfer standard to cross-calibrate backscatter from the various sensors in the scatterometer dataset. We characterize the consistency of the data from the wind glaze region and propose a combined surface and volume scattering model for determining the calibration offset. We evaluate the sensitivity of the model in its current state with respect to those parameters.

Radiometric and radar data from NASA’s Soil Moisture Active Passive (SMAP) satellite are presented in a study of the Ross ice shelf, Antarctica. L-band brightness temperature ( $T_{B}$ ) patterns compare favorably to the outflow patterns from East and West Antarctica. Cooler $T_{B}$ is associated with the broad outflow from West Antarctic ice streams and the outflow from narrow outlet glaciers that drain East Antarctica. Aside from outlet glacier discharges, ice from East Antarctica is thinner and radiometrically warmer than that from West Antarctica. $T_{B}$ is stable across the ice shelf over the 6-year period of observations (1–2 K standard deviation). Over shorter times, surface melt events cause pre- and post-melt TB to vary by as much as 5 K in vertical polarization. Radar measurements highlight areas where backscatter is strong from melt-related ice lenses and ice layers, consistent with a corresponding decrease in TB. The TB polarization ratio on the ice shelf is approximately 1.13 and decreases from the West Antarctic grounding line toward the East Antarctic outlet glaciers. The backscatter polarization ratio increases by several dB from West to East Antarctica, indicating a decreasing influence of volume scatter toward East Antarctica. The decreasing TB polarization ratio indicates a diminishing role of firn layering on the depth-integrated emission. There is a negative correlation between warming brightness temperature and thinning. An explanation for this observation is that the relatively warm ice shelf has a relatively shallow (500 m or less) penetration depth leading to a warm physical temperature bias.

The Soil Moisture Active Passive (SMAP) mission includes a unique combination of instruments intended to provide daily global soil moisture data with high accuracy and resolution. Due to radar instrument failure, the default resolution of the data product decreased from the intended 9 km to 36 km shortly after the mission started to return data. To improve this, we employed the Scatterometer Image Reconstruction algorithm in its radiometer form (rSIR) to enhance the resolution of the radiometer brightness temperature measurements from which the soil moisture was derived. This paper compares the soil moisture estimates created from the rSIR-enhanced brightness temperatures with SMAP project radiometer L2_SM_SP and SMAP-Sentinel L2_SM_P products reported on 9 km and 3 km grids, respectively. We find that the difference of the rSIR-enhanced passive soil moisture product is generally within 0.020 cm3 cm−3 RMS of the 9 km SMAP radiometer L2_SM_SP and 0.045 cm3 cm−3 RMS of the 3 km SMAP-Sentinel L2_SM_P soil moisture products. The accuracy of the rSIR soil moisture can be improved by including better antenna pattern correction methods applied to the input TB measurements.

Perennial firn aquifers are subsurface meltwater reservoirs consisting of a meters-thick water-saturated firn layer that can form on spatial scales as large as tens of kilometers. They have been observed within the percolation facies of glaciated regions experiencing intense seasonal surface melting and high snow accumulation. Widespread perennial firn aquifers have been identified within the Greenland Ice Sheet (GrIS) via field expeditions, airborne ice-penetrating radar surveys, and satellite microwave sensors. In contrast, ice slabs are nearly continuous ice layers that can also form on spatial scales as large as tens of kilometers as a result of surface and subsurface water-saturated snow and firn layers sequentially refreezing following multiple melting seasons. They have been observed within the percolation facies of glaciated regions experiencing intense seasonal surface melting but in areas where snow accumulation is at least 25 % lower as compared to perennial firn aquifer areas. Widespread ice slabs have recently been identified within the GrIS via field expeditions and airborne ice-penetrating radar surveys, specifically in areas where perennial firn aquifers typically do not form. However, ice slabs have yet to be identified from space. Together, these two ice sheet features represent distinct, but related, sub-facies within the broader percolation facies of the GrIS that can be defined primarily by differences in snow accumulation, which influences the englacial hydrology and thermal characteristics of firn layers at depth. Here, for the first time, we use enhanced-resolution vertically polarized L-band brightness temperature (TVB) imagery (2015–2019) generated using observations collected over the GrIS by NASA's Soil Moisture Active Passive (SMAP) satellite to map perennial firn aquifer and ice slab areas together as a continuous englacial hydrological system. We use an empirical algorithm previously developed to map the extent of Greenland's perennial firn aquifers via fitting exponentially decreasing temporal L-band signatures to a set of sigmoidal curves. This algorithm is recalibrated to also map the extent of ice slab areas using airborne ice-penetrating radar surveys collected by NASA's Operation IceBridge (OIB) campaigns (2010–2017). Our SMAP-derived maps show that between 2015 and 2019, perennial firn aquifer areas extended over 64 000 km2, and ice slab areas extended over 76 000 km2. Combined together, these sub-facies are the equivalent of 24 % of the percolation facies of the GrIS. As Greenland's climate continues to warm, seasonal surface melting will increase in extent, intensity, and duration. Quantifying the possible rapid expansion of these sub-facies using satellite L-band microwave radiometry has significant implications for understanding ice-sheet-wide variability in englacial hydrology that may drive meltwater-induced hydrofracturing and accelerated ice flow as well as high-elevation meltwater runoff that can impact the mass balance and stability of the GrIS.

The MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth Science Data Record and the SMAP Twice-Daily rSIR-Enhanced EASE-Grid 2.0 Brightness Temperature (SETB) Data Set provide an extensive multi-instrument, multi-decadal, time series of global enhanced-resolution microwave radiometer image products. The Earth-based CETB and SETB archives are generated using swath-based multi-frequency micro-wave brightness temperature (TB) observations collected by the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) series, the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), and the Soil Moisture Active Passive (SMAP) microwave radiometer. In this paper, we describe new global SMAP enhanced-resolution scatterometer and synthetic aperture radar (SAR) image products that augment the CETB and SETB archives. Earth-based, morning and evening images are generated using swath-based L-band (1.26 GHz) radar backscatter (σ°) observations and previous developed image reconstruction techniques. The available image products include cylindrical and azimuthal projection scatterometer and SAR σ° images with 1- and 3-day imaging intervals, and azimuthal projection scatterometer σ° images with an 8-day imaging interval that have been further corrected for incidence and azimuth angle variation over the Greenland and Antarctic ice sheets. These σ° image time series are compatibly-gridded with CETB and SETB T B image time series to support a wide variety of geophysical applications.