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ABoVE: Active Layer Thickness Derived from Airborne L- and P-band SAR, Alaska, 2017

Documentation Revision Date: 2021-01-14

Dataset Version: 2

Summary

This dataset provides estimates of seasonal subsidence, active layer thickness (ALT), the vertical soil moisture profile, and uncertainties at a 30 m resolution for 28 sites across the ABoVE domain, including 16 sites in Alaska and 12 sites in Northwest Canada. The ALT and soil moisture profile retrievals simultaneously use L- and P-band synthetic aperture radar (SAR) data acquired by the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instruments during the 2017 Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign.

This product was created by the Permafrost Dynamics Observatory (PDO) project to estimate the seasonal subsidence owing to active layer thaw from the L-band interferometric SAR (InSAR) pair acquired in June and September 2017. It also estimates the vertical profile of soil volumetric water content (VWC) from the P-band polarimetric SAR (PolSAR) backscatter acquired in August by Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS). The joint retrieval uses seasonal subsidence derived from L-band and P-band backscatter simultaneously to estimate the ALT and vertical soil moisture profile, along with uncertainties.

There are 28 files in netCDF version 4 (*.nc4) format, one for each site, included in this dataset.

Figure 1. Sites in Version 2 of the Permafrost Dynamics Observatory Project product. Green = data delivered in Version 2. Red = not delivered in Version 2 but will be delivered in future version. Yellow = not processed due to only L-band data being available.

Citation

Schaefer, K., R.J. Michaelides, R.H. Chen, T.D. Sullivan, A.D. Parsekian, Y. Zhao, K. Bakian-Dogaheh, A. Tabatabaeenejad, M. Moghaddam, J. Chen, A.C. Chen, L. Liu, and H.A. Zebker. 2020. ABoVE: Active Layer Thickness Derived from Airborne L- and P-band SAR, Alaska, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1796

Table of Contents

  1. Dataset Overview
  2. Data Characteristics
  3. Application and Derivation
  4. Quality Assessment
  5. Data Acquisition, Materials, and Methods
  6. Data Access
  7. References
  8. Dataset Revisions

Dataset Overview

This dataset provides estimates of seasonal subsidence, active layer thickness (ALT), the vertical soil moisture profile, and uncertainties at a 30 m resolution for 28 sites across the ABoVE domain, including 16 sites in Alaska and 12 sites in Northwest Canada. The ALT and soil moisture profile retrievals simultaneously use L- and P-band synthetic aperture radar (SAR) data acquired by the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instruments during the 2017 Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign.

This product was created by the Permafrost Dynamics Observatory (PDO) project to estimate the seasonal subsidence owing to active layer thaw from the L-band interferometric SAR (InSAR) pair acquired in June and September 2017. It also estimates the vertical profile of soil volumetric water content (VWC) from the P-band polarimetric SAR (PolSAR) backscatter acquired in August by Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS). The joint retrieval uses seasonal subsidence derived from L-band and P-band backscatter simultaneously to estimate the ALT and vertical soil moisture profile, along with uncertainties.

Project: Arctic-Boreal Vulnerability Experiment

The Arctic-Boreal Vulnerability Experiment (ABoVE) is a NASA Terrestrial Ecology Program field campaign being conducted in Alaska and western Canada, for 8 to 10 years, starting in 2015. Research for ABoVE links field-based, process-level studies with geospatial data products derived from airborne and satellite sensors, providing a foundation for improving the analysis, and modeling capabilities needed to understand and predict ecosystem responses to, and societal implications of, climate change in the Arctic and Boreal regions.

Related Publications

Chen, A.C., A.D. Parsekian, K. Schaefer, E.E. Jafarov, S. Panda, L. Liu, T. Zhang, and H.A. Zebker. 2016. Ground-Penetrating Radar Measurements of Active Layer Thickness on the Alaska North Slope. Geophysics, 81(2):H1–H11. https://doi.org/10.1190/GEO2015-0124.1

Chen, R.H., A. Tabatabaeenejad, and M. Moghaddam. 2018. P-Band Radar Retrieval of Permafrost Active Layer Properties: Time-Series Approach and Validation with In-Situ Observations. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/IGARSS.2018.8518179

Jafarov, E.E., A.D. Parsekian, K. Schaefer, L. Liu, A.C. Chen, S.K. Panda, and T. Zhang. 2018. Estimating active layer thickness and volumetric water content from ground penetrating radar measurements in Barrow, Alaska. Geoscience Data Journal. https://doi.org/10.3334/ornldaac/1355

Schaefer, K., L. Liu, A.D. Parsekian, E.E. Jafarov, A.C. Chen, T. Zhang, A. Gusmeroli, S.K. Panda, H.A. Zebker, T. Schaefer. 2015. Remotely Sensed Active Layer Thickness (ReSALT) at Barrow Alaska using Interferometric Synthetic Aperture Radar. Journal of Remote Sensing, 7:3735-3759. https://doi.org/10.3390/rs70403735

User Note: Schaefer et al. (2019) is Version 1 of the PDO data product. Instead of performing retrievals by using P- and L-band data in tandem as in generating Version 1 data, the method used for this Version 2 dataset integrates the seasonal subsidence model and the radar backscattering model in a joint inversion process. Version 2 replaces and supersedes the Version 1 data and includes additional sites across the ABoVE domain.

Related Datasets

Schaefer, K., R.J. Michaelides, R.H. Chen, T. Sullivan, A.D. Parsekian, K. Bakian-dogaheh, A. Tabatabaeenejad, M. Moghaddam, J. Chen, A.C. Chen, L. Liu, and H.A. Zebker. 2019. ABoVE: Active Layer Thickness Derived from Airborne L- and P-band SAR, Alaska, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1676

Chen, A., A. Parsekian, K. Schaefer, E. Jafarov, S.K. Panda, L. Liu, T. Zhang, and H.A. Zebker. 2015. Pre-ABoVE: Ground-penetrating Radar Measurements of ALT on the Alaska North Slope. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1265

Jafarov, E.,A.D. Parsekian, K. Schaefer, L. Liu, A. Chen, S.K. Panda, and T. Zhang. 2016. Pre-ABoVE: Active Layer Thickness and Soil Water Content, Barrow, Alaska, 2013. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1355

Liu, L., K. Schaefer, A. Chen, A. Gusmeroli, E. Jafarov, S. Panda, A. Parsekian, T. Schaefer, H. A. Zebker, T. Zhang. 2015. Pre-ABoVE: Remotely Sensed Active Layer Thickness, Barrow, Alaska, 2006-2011. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1266

Liu, L., K. Schaefer, A. Chen, A. Gusmeroli, E. Jafarov, S. Panda, A. Parsekian, T. Schaefer, H. A. Zebker, T. Zhang. 2015. Pre-ABoVE: Remotely Sensed Active Layer Thickness, Prudhoe Bay, Alaska, 1992-2000. ORNL DAAC, Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1267

Acknowledgments

This work was supported by NASA grants NNX13AM25G, NNX14A154G, NNX16AH36A, and NNX17AC59A.

Data Characteristics

Spatial Coverage: 28 sites in the ABoVE Domain, including 16 in Alaska and 12 in Canada

ABoVE Reference Locations

Domain: Core and Extended ABoVE Regions

State/Territory: Alaska, Yukon, Northwest Territories

Grid cells: See Table 1

Spatial Resolution: 30 m

Temporal Coverage: 2017-06-19 to 2017-09-16

Temporal Resolution: The estimates are one-time estimates. The surface deformation estimates are considered to represent the subsidence that occurred during the thawing season in 2017. The thaw depth and soil moisture estimates are considered to represent the active layer soil profile at maximum thaw, in which case the thaw depth is equivalent to ALT for the year 2017.

Study Area: Latitude and longitude are given in decimal degrees.

Site Site Code UAVSAR Flight Lines ABoVE Grid Cells North Latitude South Latitude East Longitude West Longitude
Alaska
Ambler ambler ambler Bh005v002, Bh006v002 67.2533648 66.3303943 -157.81629 -159.41414
Atqasuk atqasu atqasu Bh007v001, Bh008v001 71.0457556 69.6727191 -156.7922 -158.29708
Barrow barrow barrow Bh008v001 71.4895144 70.2516655 -154.44317 -156.85705
Coldfoot coldfo coldfo Bh007v003, Bh006v004, Bh007v004 67.4315676 66.1964522 -149.66555 -151.11791
Council counci counci Bh003v002, Bh004v002, Bh004v001 65.5506789 64.5076694 -163.25417 -165.14618
Delta Junction deltaj deltjA, deltjB, deltjC, djNEON Bh006v005, Bh006v006, Bh006v007, Bh007v006, Bh007v007 64.4978357 62.8483799 -142.08318 -147.6053
Huslia huslia huslia Bh005v003 65.5400763 65.1815982 -154.8244 -156.79808
Inigok inigok inigok Bh007v002, Bh008v002 70.6816689 69.7135719 -151.22727 -154.19735
Katmai National Park B katmaB katmaB Bh001v006, Bh002v006 58.4873484 58.1228228 -154.57597 -157.05948
Kougarok kougar kougar Bh004v002, Bh005v002 65.7073814 65.4504802 -159.96013 -163.0292
Koyuk koyukk koyukk Bh004v002, Bh004v003 65.0616375 64.7098302 -159.18804 -161.11936
Poorman poorma poorma Bh004v004, Bh005v004 64.4237975 64.2583861 -153.1226 -156.2271
Snake River sriver sriver Bh003v004, Bh003v005 61.6822051 60.7736531 -156.04572 -157.48292
Toolik toolik toolik Bh008v003 68.8377303 68.5053676 -148.97329 -150.02527
Yukon Flats yflats yflatE, yflatW Bh007v004, Bh008v004, Bh007v005, Bh008v005 67.2070658 65.7327427 -144.73525 -147.421
Yukon-Kuskokwin Delta ykdelt ykdelA, ykdelB Bh002v003 61.3177122 61.0475397 -161.86449 -163.89005
Canada
Aklavik Highway aklavi aklavi Bh010v005 68.3676033 68.0118889 -133.1116 -135.55288
Behchoko behcho behcho Bh013v011, Bh013v010 62.6668191 62.0756687 -116.41366 -116.88251
Daring Lake daring daring Bh014v009, Bh015v009, Bh014v010 64.9962538 63.6507266 -111.09271 -113.32612
Faber Lake faberl faberl Bh013v009, Bh013v010 64.3921318 63.8563166 -116.94599 -118.2102
Kakisa Lake A kakisA kakisA Bh012v011, Bh012v012 61.2975291 60.7984522 -116.41524 -118.01967
Kakisa Lake B kakisB kakisB Bh012v011 61.396317 60.6692971 -117.38315 -117.9889
Old Crow Airport oldcrB oldcrB Bh008v005, Bh009v005 67.6276361 66.8504555 -139.57781 -143.16628
Old Crow Flats oldcrA oldcrA Bh009v005 68.2226757 67.6647719 -139.04966 -140.04993
Fort Providence provid provid Bh013v011, Bh012v011 62.2011249 61.2887645 -116.20676 -117.90332
Snare River snarer snarer Bh012v010, Bh013v010 63.9582782 62.7176466 -116.55526 -118.29913
Inuvik to Tuk Highway tukhwy tukhwy Bh010v005, Bh011v005 69.505042 68.1769914 -132.91564 -134.41337
Yellowkhife yellow yellow Bh010v005, Bh011v005 62.5842288 61.9409843 -112.34412 -113.81533

Data File Information

There are 28 files in netCDF version 4 (*.nc4) format, one for each site. The file naming convention is PDO_ReSALT_site _2017_02.nc4, where site is provided as a Site Code in Table 1.

Data File Details

Missing values are represented by -9999. The projection used is “Canada_Albers_Equal_Area_Conic” (EPSG:102001). The proj.4 string of the projection is “+proj=aea +lat_1=50 +lat_2=70 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m no_defs”.

Table 2. Variable names and descriptions.

Variable Units Description
lat degrees_north Latitude coordinate
lon degrees_east Longitude coordinate
x m x coordinate of projection
y m y coordinate of projection
alt m Active layer thickness (ALT)
sub m Seasonal subsidence from L-band InSAR
Sw0 vol/vol Soil water saturation fraction at the surface (z=0)
wtd m Water table depth
mv_6cm vol/vol Soil volumetric water content averaged over 0 to 6 cm
mv_12cm vol/vol Soil volumetric water content averaged over 0 to 12 cm
mv_20cm vol/vol Soil volumetric water content averaged over 0 to 20 cm
mv_alt vol/vol Soil volumetric water content averaged over the active layer thickness
alt_unc m Uncertainty of ALT estimates
sub_unc m Uncertainty of seasonal subsidence
Sw0_unc vol/vol Uncertainty of surface water saturation
wtd_unc m Uncertainty of water table depth

Companion Files

The Python script file generate_pdo_soil_vwc.py is provided as a companion file for users to generate the soil VWC averaged to the depth of interest. The script requires the numpy and gdal libraries. Execute the script as follows:

python generate_pdo_soil_vwc.py -s site_code -a depth_avg_in_cm

where site_code is one listed in Table 1 and depth_avg_in_cm is the averaging depth in centimeter.

Application and Derivation

Soil moisture and active layer thickness are critical variables in understanding how permafrost and active layer dynamics respond to climate warming in high-latitude regions.

Quality Assessment

InSAR correlation data was used for both masking of poor data and uncertainty quantification. Poorly correlated pixels were masked with coherence < 0.35 because they do not provide reliable phase estimates. Uncertainties were estimated in phase from InSAR correlation using the Cramer-Rao bound (Tough et al., 1995) converted to centimeters of uncertainties in subsidence. Owing to a lack of repeat temporal airborne observations within a single thaw season, the Cramer-Rao bound estimates are considered lower-bound estimates with actual uncertainties likely larger than those we report. Nonetheless, empirical estimates of uncertainty of phase calibration yield point values of approximately 0.5—1 cm. The plausible range of uncertainty values lies between the Cramer-Rao bound and 1—2 cm.

The uncertainty in P-band PolSAR backscatter was assumed to be 0.5 dB based on absolute radiometric calibration (Chapin et al., 2015). A data cube of all possible solutions was constructed based on the forward model. All solutions were identified with a cost function value less than the P-band backscatter uncertainty. The mean of these solutions is reported as the best estimate for the desired geophysical variables and the standard deviation as uncertainty.

Data Acquisition, Materials, and Methods

InSAR Processing

InSAR uses phase differences between SAR images acquired at different times to produce interferograms of surface deformation in the radar line-of-sight direction. The interferometric phase provided by the NASA/JPL UAVSAR (https://uavsar.jpl.nasa.gov) was unwrapped using the SNAPHU algorithm (Chen and Zebker, 2002). The unwrapped interferometric phase from the line-of-sight (LOS) viewing geometry was converted to vertical motions (Chen et al., 2017). Variations in atmospheric and tropospheric water content can introduce biases into interferometric phase values (Jolivet et al., 2011). For swaths with noticeable atmospheric noise, a high pass filter was applied to remove signals at length scales on the order of atmospheric correlation (Lohman and Simons, 2005).

Daymet was used reanalyzed temperature data to generate a time series of accumulated degree days of thaw (ADDT) for each site (Thornton et al., 2016). The measured subsidence was adjusted using the ADDT time series to estimate the total seasonal subsidence of the active layer over the course of the thaw season. The Schaefer et al. (2015) technique was modified to scale the measured subsidence with the difference in ADDT between the full thaw season and the L-band SAR image acquisition dates. Some sites did not include the ADDT correction, which was added in the midst of Version 2 processing.

Known subsidence values at reference sites were used to convert the relative phase to absolute seasonal subsidence. Where available, reliable in-situ field measurements of ALT were used and converted to seasonal subsidence using the soil expansion model (Schaefer et al., 2015, Michaelides et al., 2019). For sites without reliable in situ data, the 5th percentile of deformation was chosen as zero-based on scene-wide histograms of relative deformation values. All estimates of seasonal deformation, correlation, and deformation uncertainties were projected onto the 30 m ABoVE reference grid.

Table 3. Summary of the InSAR processing steps applied to each site.

Site Code UAVSAR Flight Line Calibrated to Reference Pixel Referenced to 5th Percentile of Deformation Atmospheric Noise Correction: PyAPS (Jolivet et al., 2011) ADDT (Schaefer et al., 2015)
Alaska
ambler ambler X      
atqasu atqasu X      
barrow barrow X     X
coldfo coldfo   X X  
counci counci X     X
deltaj (referenced to deltjC) deltjA   X X  
deltjB   X X  
deltjC X      
djNEON   X X  
huslia huslia   X X  
inigok inigok X      
katmaB katmaB   X X X
kougar kougar   X X  
koyukk koyukk   X X  
poorma poorma   X X X
sriver sriver   X X X
toolik toolik   X X X
yflats (referenced to yflatW) yflatE   X X X
yflatW   X X X
ykdelt ykdelA X     X
ykdelB X     X
Canada
aklavi aklavi   X X X
behcho behcho   X X X
daring daring   X X X
faberl faberl   X X X
kakisA kakisA   X X X
kakisB kakisB   X X X
oldcrB oldcrB   X X X
oldcrA oldcrA   X X X
provid provid   X X X
snarer snarer   X X X
tukhwy tukhwy   X X X
yellow yellow   X X X

Forward Models

The ground surface settles as the active layer thaws in summer mainly because groundwater takes up about 9% less volume than ground ice. Therefore, the seasonal subsidence directly relates to the volume of melted water in the active layer. The subsidence model integrates the soil VWC profile from the surface to thaw depth, multiplied by a factor accounting for the change in volume of water phase changes.

The vertical profile of VWC profile in the active layer is soil porosity times water saturation fraction. Soil porosity depends on soil composition, mineral texture, and organic matter content. The saturation fraction is the fraction of pore space filled with water. The organic matter profile is assumed to be a generalized logistic function, which can be parametrized by the surface organic matter content (OM_0) and organic layer thickness (OLT). Nominal values for OM_0 and OLT over the ABoVE domain are chosen to be 0.8 g/g and 0.15 m, respectively (Chen et al., 2019).

The forward model of P-band PolSAR backscatter includes a radar backscattering model that calculates the radar backscattering coefficients from a multilayer dielectric structure with a rough surface atop, and a soil parametrization model that translates soil physical parameters (soil organic matter, soil texture, soil moisture) to soil dielectric constant (Chen et al., 2019). The subsidence and backscatter models share the same set of parameters that characterize the active layer soils, which enables the simultaneous retrieval of ALT and soil VWC profiles using L-band InSAR and P-band PolSAR.

Joint Retrieval of ALT and Soil VWC Profile

The retrieval process is an inversion of both subsidence and backscatter models, which can be formulated as an optimization problem minimizing their cost function (L2 norm of the observation-model differences). There are four unknowns: ALT (alt), surface water saturation (Sw0), water table depth (wtd), and surface roughness (h) (Eq. 1). In order to facilitate the inversion process, which is to find the solution points that meet the cost function criteria (L2 norm smaller than the uncertainties in the measured subsidence and P-band backscatter), the data cubes of subsidence and P-band HH and VV backscatter were pre-computed with dimensions of the unknown variables. The mean and standard deviation of all the solution points that met the cost function criteria are reported as the retrieved value and associated uncertainty for the unknowns. After the retrieval, soil porosity and water saturation profiles can be calculated from the retrieved profile parameters (Sw0 and wtd) and the assumed organic matter profile, which then be used to calculate the soil VWC profile (Eq. 2).

Equation1     (1)

Equation 2     (2)

The python script (companion file generate_pdo_soil_vwc.py) can be used to generate the soil VWC averaged to the depth of the user's interest. The soil VWC values averaged over the first 6, 12, 20 cm from the surface and over the entire active layer are provided.

Data Access

These data are available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

ABoVE: Active Layer Thickness Derived from Airborne L- and P-band SAR, Alaska, 2017

Contact for Data Center Access Information:

References

Chapin, E., A. Chau, J. Chen, B. Heavey, S. Hensley, Y. Lou, R. Machuzak, and M. Moghaddam. 2012. AirMOSS: an airborne P-band SAR to measure root-zone soil moisture. Proc. IEEE Radar Conference (RadarCon). 0693–0698. https://doi.org/10.1109/RADAR.2012.6212227

Chen, C.W., and H.A. Zebker. 2002. Phase unwrapping for large SAR interferograms: statistical segmentation and generalized network models. IEEE Trans. Geoscience Remote Sensing, 40:1709–1719. https://doi.org/10.1109/TGRS.2002.802453

Chen, A.C., A.D. Parsekian, K. Schaefer, E.E. Jafarov, S. Panda, L. Liu, T. Zhang, and H.A. Zebker. 2016. Ground-Penetrating Radar Measurements of Active Layer Thickness on the Alaska North Slope. Geophysics, 81(2):H1–H11. https://doi.org/10.1190/GEO2015-0124.1

Chen, J., R. Knight, and H.A. Zebker. 2017. The Temporal and spatial variability of the confined aquifer head and storage properties in the San Luis Valley, Colorado inferred from multiple InSAR missions. Water Resources Research, 53:9708–9720. https://doi.org/10.1002/2017WR020881

Chen, R.H., A. Tabatabaeenejad, and M. Moghaddam. 2018. P-Band Radar Retrieval of Permafrost Active Layer Properties: Time-Series Approach and Validation with In-Situ Observations. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/IGARSS.2018.8518179

Chen, R.H., K. Bakian-Dogaheh, A. Tabatabaeenejad, and M. Moghaddam. 2019. Modeling and retrieving soil moisture and organic matter profiles in the active layer of permafrost soils from P-band radar observations. International Geoscience and Remote Sensing Symposium (IGARSS). 10095–10098. https://doi.org/10.1109/IGARSS.2019.8899802

Jafarov, E.E., A.D. Parsekian, K. Schaefer, L. Liu, A.C. Chen, S.K. Panda, and T. Zhang. 2018. Estimating active layer thickness and volumetric water content from ground penetrating radar measurements in Barrow, Alaska. Geoscience Data Journal. https://doi.org/10.3334/ornldaac/1355

Jolivet, R., R. Grandin, C. Lasserre, M.-P. Doin, and G. Peltzer. 2011. Systematic InSAR tropospheric phase delay corrections from global meteorological reanalysis data. Geophysical Research Letters, 38:L17311. https://doi.org/10.1029/2011GL048757

Liu, L., K. Schaefer, T. Zhang, and J. Wahr. 2012. Estimating 1992–2000 average active layer thickness on the Alaskan North Slope from remotely sensed surface subsidence. Journal of Geophysical Research, 117:F01005. https://doi.org/10.1029/2011JF002041

Lohman, R.B., and M. Simons. 2005. Some thoughts on the use of InSAR data to constrain models of surface deformation: Noise structure and data downsampling. Geochemistry, Geophysics, Geosystems, 6:Q01007. https://doi.org/10.1029/2004GC000841

Michaelides, R.J., K. Schaefer, H.A. Zebker, A. Parsekian, L. Liu, J. Chen, S. Natali, S. Ludwig, and S.R. Schaefer. 2019. Inference of the impact of wildfire on permafrost and active layer thickness in a discontinuous permafrost region using the remotely sensed active layer thickness (ReSALT) algorithm. Environmental Research Letters, 14:035007. https://doi.org/10.1088/1748-9326/aaf932

Miller, C.E., P.C. Griffith, S.J. Goetz, E.E. Hoy, N. Pinto, I.B. McCubbin, A.K. Thorpe, M. Hofton, D. Hodkinson, C. Hansen, J. Woods, E. Larson, E.S. Kasischke, and H.A. Margolis. 2019. An overview of ABoVE airborne campaign data acquisitions and science opportunities. Environmental Research Letters, 14:080201. https://doi.org/10.1088/1748-9326/ab0d44

Schaefer, K., L. Liu, A.D. Parsekian, E.E. Jafarov, A.C. Chen, T. Zhang, A. Gusmeroli, S.K. Panda, H.A. Zebker, T. Schaefer. 2015. Remotely Sensed Active Layer Thickness (ReSALT) at Barrow Alaska using Interferometric Synthetic Aperture Radar. Journal of Remote Sensing, 7:3735-3759. https://doi.org/10.3390/rs70403735

Thornton, P.E., M.M. Thornton, B.W. Mayer, Y. Wei, R. Devarakonda, R.S. Vose, and R.B. Cook. 2016. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1328

Tough, J.A., D. Blacknell, and S. Quegan. 1995. A statistical description of polarimetric and interferometric synthetic aperture radar data. Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences, 449:567–589. https://doi.org/10.1098/rspa.1995.0059

Dataset Revisions

Product Version Release Date Revision Notes
2 2021-01-14 Instead of performing retrievals by using P- and L-band data in tandem as in generating Version 1 data, the method used for this Version 2 dataset integrates the seasonal subsidence model and the radar backscattering model in a joint inversion process. Version 2 replaces and supersedes the Version 1 data and includes additional sites across the ABoVE domain.
1 2019-05-08 ORNL DAAC released Schaefer et al. (2019), Version 1 of this dataset.