This data set contains snowpack relative permittivities and densities derived from Ground Penetrating Radar (GPR) surveys and airborne lidar observations of snow depths. The results of the original GPR surveys are published in the SnowEx17 Ground Penetrating Radar, Version 2 data set, while the lidar snow depths were sourced from ASO L4 Lidar Snow Depth 3m UTM Grid, Version 1. Data are available between 08 Feb 2017 to 25 Feb 2017 from Grand Mesa, a snow-covered, forested area about 40 miles east of Grand Junction. Parameters include two-way travel (TWT) time, lidar-measured snow depth, calculated snow water equivalent (SWE), calculated snow density, and calculated relative permittivity.

This product represents the first full polarimetric Global Navigation Satellite System Reflectometry (GNSS-R) dataset, derived from the Soil Moisture Active Passive (SMAP) mission radar. The main parameters are the normalized Stokes parameters, total power normalized bistatic radar cross section, and reflectivity. The SMAP radar receiver, in bistatic radar configuration, measures the horizontal and vertical components of the Global Positioning System (GPS) signal as it is reflected and scatters from the Earth’s surface. Stokes parameters are computed with these horizontal and vertical polarization measurements. The dataset comprises a full reconstruction of the Earth' surface polarimetric values, with approximately 3,200 observations per day and about 1,200,000 per year.

This data set contains the results of 1 GHz ground-penetrating radar surveys conducted in Alaska, USA as part of the NASA SnowEx 2023 field campaign. The study site is a coastal tundra environment in the North Slope region of the Alaska coastal plain (Upper Kuparuk Toolik). Data were collected between 08 Mar 2023 to 15 Mar 2023, spatially coinciding with snow pit locations and along transects between snow pits. Data include two-way travel (TWT) time, calculated snow depth, and calculated snow water equivalent (SWE). Raw GPR data are available as SnowEx23 CRREL Ground Penetrating Radar Raw, Version 1.

This data set contains the results of 1 GHz ground-penetrating radar surveys conducted in Alaska, USA as part of the NASA SnowEx 2023 field campaign. The study site is a coastal tundra environment in the North Slope region of the Alaska coastal plain (Upper Kuparuk Toolik). Data were collected between 08 Mar 2023 to 15 Mar 2023, spatially coinciding with snow pit locations and along transects between snow pits. Data include georeferenced multichannel ground-penetrating radargrams stored within .nc files. PRocessed GPR data are available as SnowEx23 CRREL Ground Penetrating Radar, Version 1.

The CYGNSS Level 3 UC Berkeley Watermask Record Version 3.2 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.



This dataset is derived from version 3.2 of the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X32). This is an update from the previous watermask monthly product (https://doi.org/10.5067/CYGNS-L3W31) which derived from the CYGNSS L1 SDR v3.1 (https://doi.org/10.5067/CYGNS-L1X31). The new product provides daily binary inland surface water classification data at a 0.01-degree (~1x1 kilometer) resolution with an approximate 6-day latency. The algorithm utilized data from up to 30 days prior to generate the daily map. This product, known as the UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC), generates water classification for a given location based on CYGNSS observations combined with a random walker algorithm. The watermask variable includes binary values indicating land (0), surface water (1), and no data/ocean (-99). The data product is archived in daily files in netCDF-4 format and covers the period from September 2018 to present.

This dataset contains ice concentration rankings of S-band images from the S-BAND marine navigation radar collected during the 2022 Salinity and Stratification at the Sea Ice Edge (SASSIE) campaign. SASSIE is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. Images from the radar were digitized and saved every 10-60 seconds on days that the R/V Wolstad was in or around ice. Ice concentration rankings ranging from 0 to 3 determined by VGG19 Machine Learning Model. L2 summary data are available in NetCDF format.

This data set contains the raw results of 1 GHz ground-penetrating radar surveys conducted as part of the NASA SnowEx23 field campaign in Alaska, USA. Surveys were conducted at three different field sites between 07 March 2023 and 16 March 2023: 1) Farmers Loop/Creamers Field, 2) the Bonanza Creek Experimental Forest, and 3) the Caribou/Poker Creek Research Watershed.

This data set contains snow water equivalent (SWE) and snow density raster data derived from a lidar-based snow depth data set (SnowEx20 Grand Mesa IOP QSI Lidar Snow Depth Data, Version 1), a lidar-based digital terrain model (ASO L4 Lidar Point Cloud Digital Terrain Model 3m UTM Grid, Version 1), and two GPR data sets (SnowEx20 Grand Mesa IOP BSU 1 GHz Multi-polarization GPR, Version 1 and Mesa IOP UNM 800 and 1600 MHz MALA GPR, Version 1). Data was collected during the NASA SnowEx 2020 field campaign in Grand Mesa, Colorado.

The CYGNSS Level 3 UC Berkeley Watermask Record Version 3.1 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.



This dataset is derived from version 3.1 of the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X32), and provides monthly binary inland surface water classification data at a 0.01-degree (~1x1 kilometer) resolution with a 1-month latency. This product, known as the UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC), generates water classification for a given location based on CYGNSS observations combined with a random walker algorithm. The watermask variable includes binary values indicating land (0), surface water (1), and no data/ocean (-99). The data product is archived in monthly files in netCDF-4 format and covers the period from August 2018 to present.

This dataset contains the first release, Version 1.0, of the CYGNSS Calibrated Raw Intermediate Frequency (IF) based L1 Product. This product includes several established signal coherence detectors, including the power-ratio P_ratio, complex zero-Doppler delay waveform and full entropy E_full, and a novel fast entropy detector E_fast. Both entropy detectors are provided with two temporal resolutions: 2 ms and 50 ms. Several scattered signal strength products are included: Signal-to-Noise Ratio SNR, reflected power P_g, reflectivity Γ, and Normalized Bistatic Radar Cross-Section NBRCS. Each of these products is derived using a coherent integration time of T_c = 1 ms and incoherent integration times of N_inc = 1000, 500, 250, 100, 50, and 2 ms. Signal strength time series at the shorter (2 and 50 ms) times provides excellent detection of land-water transitions in heterogeneous scenes. Delay Doppler Maps (DDMs) are also generated with high delay (∆τ = 1/16 chip) and Doppler (∆f= 50 Hz) resolution. This suite of coherence detection methods can be used to detect the presence of small inland water bodies.