Articles | Volume 18, issue 2
https://doi.org/10.5194/tc-18-575-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-575-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Feb 2024
Research article |
| 12 Feb 2024
| 12 Feb 2024
Snow water equivalent retrieval over Idaho – Part 2: Using L-band UAVSAR repeat-pass interferometry
Zachary Hoppinen
CORRESPONDING AUTHOR
Department of Geosciences, Boise State University, 1295 University Drive, Boise, ID, USA
Cold Regions Research and Engineering Laboratory, Engineer Research and Development Center, United States Army, Hanover, NH 03755, USA
Shadi Oveisgharan
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr, Pasadena, CA, USA
Hans-Peter Marshall
Department of Geosciences, Boise State University, 1295 University Drive, Boise, ID, USA
Ross Mower
National Center for Atmospheric Research, Boulder, Colorado, USA
Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
Kelly Elder
US Forest Service, Rocky Mountain Research Station, Fort Collins, CO, USA
Carrie Vuyovich
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
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Estimates of 250 m of snow water equivalent in the western USA and Canada are improved by assimilating observations representative of a snow-focused satellite mission with a land surface model. Here, by including a gap-filling strategy, snow estimates could be improved in forested regions where remote sensing is challenging. This approach improved estimates of winter maximum snow water volume to within 4 %, on average, with persistent improvements to both spring snow and runoff in many regions.
Shadi Oveisgharan, Robert Zinke, Zachary Hoppinen, and Hans Peter Marshall
The Cryosphere, 18, 559–574, https://doi.org/10.5194/tc-18-559-2024, https://doi.org/10.5194/tc-18-559-2024, 2024
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Short summary
The seasonal snowpack provides water resources to billions of people worldwide. Large-scale mapping of snow water equivalent (SWE) with high resolution is critical for many scientific and economics fields. In this work we used the radar remote sensing interferometric synthetic aperture radar (InSAR) to estimate the SWE change between 2 d. The error in the estimated SWE change is less than 2 cm for in situ stations. Additionally, the retrieved SWE using InSAR is correlated with lidar snow depth.
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An airborne gamma-ray remote-sensing technique provides reliable snow water equivalent (SWE) in a forested area where remote-sensing techniques (e.g., passive microwave) typically have large uncertainties. Here, we explore the utility of assimilating the gamma snow data into a land surface model to improve the modeled SWE estimates in the northeastern US. Results provide new insights into utilizing the gamma SWE data for enhanced land surface model simulations in forested environments.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
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As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
Jack Tarricone, Ryan W. Webb, Hans-Peter Marshall, Anne W. Nolin, and Franz J. Meyer
The Cryosphere, 17, 1997–2019, https://doi.org/10.5194/tc-17-1997-2023, https://doi.org/10.5194/tc-17-1997-2023, 2023
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Mountain snowmelt provides water for billions of people across the globe. Despite its importance, we cannot currently measure the amount of water in mountain snowpacks from satellites. In this research, we test the ability of an experimental snow remote sensing technique from an airplane in preparation for the same sensor being launched on a future NASA satellite. We found that the method worked better than expected for estimating important snowpack properties.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, Rhae Sung Kim, and Jennifer M. Jacobs
Hydrol. Earth Syst. Sci., 26, 5721–5735, https://doi.org/10.5194/hess-26-5721-2022, https://doi.org/10.5194/hess-26-5721-2022, 2022
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While land surface models are a common approach for estimating macroscale snow water equivalent (SWE), the SWE accuracy is often limited by uncertainties in model physics and forcing inputs. In this study, we found large underestimations of modeled SWE compared to observations. Precipitation forcings and melting physics limitations dominantly contribute to the SWE underestimations. Results provide insights into prioritizing strategies to improve the SWE simulations for hydrologic applications.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
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Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
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The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
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Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
Ahmad Hojatimalekshah, Zachary Uhlmann, Nancy F. Glenn, Christopher A. Hiemstra, Christopher J. Tennant, Jake D. Graham, Lucas Spaete, Arthur Gelvin, Hans-Peter Marshall, James P. McNamara, and Josh Enterkine
The Cryosphere, 15, 2187–2209, https://doi.org/10.5194/tc-15-2187-2021, https://doi.org/10.5194/tc-15-2187-2021, 2021
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We describe the relationships between snow depth, vegetation canopy, and local-scale processes during the snow accumulation period using terrestrial laser scanning (TLS). In addition to topography and wind, our findings suggest the importance of fine-scale tree structure, species type, and distributions on snow depth. Snow depth increases from the canopy edge toward the open areas, but wind and topographic controls may affect this trend. TLS data are complementary to wide-area lidar surveys.
Rhae Sung Kim, Sujay Kumar, Carrie Vuyovich, Paul Houser, Jessica Lundquist, Lawrence Mudryk, Michael Durand, Ana Barros, Edward J. Kim, Barton A. Forman, Ethan D. Gutmann, Melissa L. Wrzesien, Camille Garnaud, Melody Sandells, Hans-Peter Marshall, Nicoleta Cristea, Justin M. Pflug, Jeremy Johnston, Yueqian Cao, David Mocko, and Shugong Wang
The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, https://doi.org/10.5194/tc-15-771-2021, 2021
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High SWE uncertainty is observed in mountainous and forested regions, highlighting the need for high-resolution snow observations in these regions. Substantial uncertainty in snow water storage in Tundra regions and the dominance of water storage in these regions points to the need for high-accuracy snow estimation. Finally, snow measurements during the melt season are most needed at high latitudes, whereas observations at near peak snow accumulations are most beneficial over the midlatitudes.
Miguel A. Aguayo, Alejandro N. Flores, James P. McNamara, Hans-Peter Marshall, and Jodi Mead
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-451, https://doi.org/10.5194/hess-2020-451, 2020
Manuscript not accepted for further review
Cited articles
Carriere, L.: Discover SCU Hardware, NASA, https://www.nccs.nasa.gov/systems/discover/scu-info (last access: March 2023), 2023. a
Cohen, J., Ye, H., and Jones, J.: Trends and variability in rain‐on‐snow events, Geophys. Res. Lett., 42, 7115–7122, 2015. a
Deeb, E., Forster, R., and Kane, D.: Monitoring snowpack evolution using interferometric synthetic aperture radar on the North Slope of Alaska, USA, Int. J. Remote Sens,, 32, 3985–4003, 2011. a
Doin, M. P., Lasserre, C., Peltzer, G., Cavalié, O., and Doubre, C.: Corrections of stratified tropospheric delays in SAR interferometry: Validation with global atmospheric models, J. Appl. Geophys., 69, 35–50, 2009. a
Dominguez, F., Rivera, E., Lettenmaier, D. P., and Castro, C. L.: Changes in winter precipitation extremes for the western United States under a warmer climate as simulated by regional climate models, Geophys. Res. Lett., 39, L05803, https://doi.org/10.1029/2011GL050762, 2012. a
Fore, A. G., Chapman, B. D., Hawkins, B. P., Hensley, S., Jones, C. E., Michel, T. R., and Muellerschoen, R. J.: UAVSAR Polarimetric Calibration, IEEE T. Geosci. Remote, 53, 3481–3491, 2015. a
Gesch, D,, Oimoen, M. J., and Arundel, S.: The National Elevation Dataset, USGS Publication Warehouse, Geological Survey (U.S.), https://doi.org/10.3133/fs14899, 2018. a, b
Goldstein, R. M., Zebker, H. A., and Werner, C. L.: Satellite radar interferometry: Two‐dimensional phase unwrapping, Radio Sci., 23, 713–720, https://doi.org/10.1029/rs023i004p00713, 1988. a
Greene, E., Birkeland, K., Elder, K., McCammon, I., Staples, M., Sharaf, D., Trautman, S., and Wagner, W.: Snow, Weather, and Avalanches: Observation Guidelines for Avalanche Programs in the United States, Denver, Colorado, 4th edn., American Avalanche Association, ISBN 78-0-9760118-1-1, 2022. a
Guneriussen, T., Hogda, K. A., Johnsen, H., and Lauknes, I.: Insar for Estimation of Changes in Snow Water Equivalent of Dry Snow, IEEE T. Geosci. Remote, 39, 2101, https://doi.org/10.1109/36.957273, 2001. a, b, c, d
Hensley, S., Wheeler, K., Sadowy, G., Jones, C., Shaffer, S., Zebker, H., Miller, T., Heavey, B., Chuang, E., Chao, R., Vines, K., Nishimoto, K., Prater, J., Carrico, B., Chamberlain, N., Shimada, J., Simard, M., Chapman, B., Muellerschoen, R., Le, C., Michel, T., Hamilton, G., Robison, D., Neumann, G., Meyer, R., Smith, P., Granger, J., Rosen, P., Flower, D., and Smith, R.: THE UAVSAR Instrument: Description and first results, 2008 IEEE Radar Conf., 26–30 May 2008. Rome, Italy, 1–6, 2008. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 2020. a
Homer, C., Dewitz, J., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N., Wickham, J., and Megown, K.: Completion of the 2011 National Land Cover Database for the Conterminous United States – Representing a Decade of Land Cover Change Information, Photogrammetric Eng. Remote Sens., 81, 346–354, 2015. a
Hoppinen, Z.: ZachHoppinen/uavsar-validation: The Cryosphere Publication (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.10407311, 2023. a
Hoppinen, Z., Tarricone, J., Abedisi, N., and Marshall, H. P.: SnowEx/uavsar_pytools: v0.2.0, Zenodo [code], https://doi.org/10.5281/zenodo.6789624, 2022. a, b
Jin, S., Homer, C., Yang, L., Danielson, P., Dewitz, J., Li, C., Zhu, Z., Xian, G., and Howard, D.: Overall Methodology Design for the United States National Land Cover Database 2016 Products, Remote Sens., 11, 2971, https://doi.org/10.3390/rs11242971, 2019. a, b
Kunkel, K. E., Robinson, D. A., Champion, S., Yin, X., Estilow, T., and Frankson, R. M.: Trends and Extremes in Northern Hemisphere Snow Characteristics, Curr. Clim. Change Rep., 2, 65–73, 2016. a
Latifovic, R., Homer, C., Ressl, R., Pouliot, D. A., Hossian, S., Colditz, R., Olthof, I., Chandra, G., and Victoria, A. (Eds.): North American Land Change Monitoring System, U.S. Geological Survey, 05, 303–324, https://doi.org/10.1201/b11964-24, 2012. a
Li, H., Wang, Z., He, G., and Man, W.: Estimating Snow Depth and Snow Water Equivalence Using Repeat-Pass Interferometric SAR in the Northern Piedmont Region of the Tianshan Mountains, J. Sensors, 2017, 1–17, 2017. a
Liston, G. E., Stroeve, J., Buzzard, S., Zhou, L., Mallett, R., Barrett, A., Tschudi, M., Tsamados, M., Itkin, P., and Stewart, J.: A Lagrangian Snow‐Evolution System for Sea Ice Applications (SnowModel‐LG): Part II – Analyses, J. Geophys. Res.-Oceans, 125, 10, https://doi.org/10.1029/2019JC015913, 2020. a, b
Livneh, B. and Badger, A. M.: Drought less predictable under declining future snowpack, Nat. Clim. Change, 10, 452–458, 2020. a
Mason, M., Marshall, H., McCormick, M., Craaybeek, D., Elder, K., and Vuyovich, C. M.: SnowEx20 Time Series Snow Pit Measurements, Version 1, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/POT9E0FFUUD1, 2023. a
Matzler, C.: Microwave permittivity of dry snow, IEEE T. Geosci. Remote, 34, 573–581, 1996. a
Mower, R., Gutmann, E. D., Lundquist, J., Liston, G. E., and Rasmussen, S.: Parallel SnowModel (v1.0): a parallel implementation of a Distributed Snow-Evolution Modeling System (SnowModel), EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1612, 2023. a
Naderpour, R., Schwank, M., Houtz, D., Werner, C., and Mätzler, C.: Wideband Backscattering From Alpine Snow Cover: A Full-Season Study, IEEE T. Geosci. Remote, 60, 1–15, https://doi.org/10.1109/tgrs.2021.3112772, 2022. a
Nagler, T., Rott, H., Scheiblauer, S., Libert, L., Mölg, N., Horn, R., Fischer, J., Keller, M., Moreira, A., and Kubanek, J.: Airborne Experiment on Insar Snow Mass Retrieval in Alpine Environment, IGARSS 2022 – 2022 IEEE Int. Geosci. Remote Sensing Symposium, 00, 4549–4552, 2022. a
NASA: UAVSAR Data Search, NASA Jet Propulsion Laboratory [data set], https://uavsar.jpl.nasa.gov/cgi-bin/data.pl (last access: August 2023), 2024. a
Oveisgharan, S., Zinke, R., Hoppinen, Z., and Marshall, H. P.: Snow water equivalent retrieval over Idaho – Part 1: Using Sentinel-1 repeat-pass interferometry, The Cryosphere, 18, 559–574, https://doi.org/10.5194/tc-18-559-2024, 2024. a, b
Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M. C., Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C. E., Armston, J., Dubayah, R., Blair, J. B., and Hofton, M.: Mapping global forest canopy height through integration of GEDI and Landsat data, Remote Sensi. Environ., 253, 112165, https://doi.org/10.1016/j.rse.2020.112165, 2021. a
Rasmussen, R. M., Chen, F., Liu, C., Ikeda, K., Prein, A., Kim, J., Schneider, T., Dai, A., Gochis, D., Dugger, A., Zhang, Y., Jaye, A., Dudhia, J., He, C., Harrold, M., Xue, L., Chen, S., Newman, A., Dougherty, E., Abolafia-Rozenzweig, R., Lybarger, N., Viger, R., Dunne, K. A., Rasmussen, K., and Miguez-Macho, G.: Four-kilometer long-term regional hydroclimate reanalysis over the conterminous United States (CONUS), 1979–2020, Research Data Arc. at the Nat. Center for Atm. Research, Computational and Information Systems Lab., USGS, https://doi.org/10.5066/P9PHPK4F, 2023. a
Rosen, P. A., Hensley, S., Joughin, I. R., Li, F. K., Madsen, S. N., RodrÍgues, E., Goldstein, R. M., Bamler, R., and Hartl, P.: Synthetic aperture radar interferometry, Proc. IEEE, 88, 333–382, https://doi.org/10.1088/0266-5611/14/4/001, 2000. a
Ruiz, J., Lemmetyinen, J., Kontu, A., Tarvainen, R., Pulliainen, J., Vehmas, R., and Praks, J.: Analysis of Snow Coherence Conservation for SWE Retrieval at L-, S-, C-and X-Bands, 2021 IEEE Int. Geosci Remote Sens. Symposium IGARSS, 00, 860–863, 2021. a
Ruiz, J. J., Lemmetyinen, J., Kontu, A., Tarvainen, R., Vehmas, R., Pulliainen, J., and Praks, J.: Investigation of Environmental Effects on Coherence Loss in SAR Interferometry for Snow Water Equivalent Retrieval, IEEE T. Geosci. Remote, 60, 1–15, https://doi.org/10.1109/tgrs.2022.3223760, 2022. a
Schaefer, G. L. and Paetzold, R. F.: SNOTEL (SNOwpack TELemetry) And SCAN (Soil Climate Analysis Network), Automated Weather Stations for Applications in Agriculture and Water Resources Management: Current Use and Future Perspectives, edited by: Sivakumar, M. V. K., World Meteorological Organization, http://www.wamis.org/agm/pubs/agm3/WMO-TD1074.pdf (last access: August 2023), 2000. a
Strapazzon, G., Schweizer, J., Chiambretti, I., Maeder, M. B., Brugger, H., and Zafren, K.: Effects of Climate Change on Avalanche Accidents and Survival, Front. Physiol., 12, 639433, https://doi.org/10.3389/fphys.2021.639433, 2021. a
Sturm, M., Goldstein, M. A., and Parr, C.: Water and life from snow: A trillion dollar science question, Water Resour. Res., 53, 3534–3544, 2017. a
Tarricone, J., Webb, R. W., Marshall, H.-P., Nolin, A. W., and Meyer, F. J.: Estimating snow accumulation and ablation with L-band interferometric synthetic aperture radar (InSAR), The Cryosphere, 17, 1997–2019, https://doi.org/10.5194/tc-17-1997-2023, 2023. a
USDA Natural Resources Conservation Service: SNOwpack TELemetry Network (SNOTEL), NRCS [data set], https://www.nrcs.usda.gov/wps/portal/wcc/home/aboutUs/snowProgramOverview (last access: August 2023), 2022. a
Short summary
We used changes in radar echo travel time from multiple airborne flights to estimate changes in snow depths across Idaho for two winters. We compared our radar-derived retrievals to snow pits, weather stations, and a 100 m resolution numerical snow model. We had a strong Pearson correlation and root mean squared error of 10 cm relative to in situ measurements. Our retrievals also correlated well with our model, especially in regions of dry snow and low tree coverage.
We used changes in radar echo travel time from multiple airborne flights to estimate changes in...
