سابِق|تال

Progress and prospects of remote sensing of lake ice thickness

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China

    Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, China

  • Email：lxd6304@126.com
  • Introduction：李兴东，研究方向为湖泊水量变化及湖泊冰情的遥感监测与模拟。E-mail： lxd6304@126.com
LI Xingdong13,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China

    Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, China

  • Email：dlong@tsinghua.edu.com
  • Introduction：龙笛，研究方向为遥感水文与水资源。E-mail： dlong@tsinghua.edu.com
LONG Di13*,  
  • Affiliation：

    State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China

    Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, China

HUANG Qi13,  
  • Affiliation：

    State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China

    Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, China

ZHAO Fanyu13,  
  • Affiliation：

    Water Conservancy and Civil Engineering College, Inner Mongolia Key Laboratory of Water Rresource Protection and Utilization, Inner Mongolia Agricultural University, Hohhot 010018, China

    Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, China

LIU Tingxi23

ملخص

Lake ice is an important variable in cryospheric hydrology that is quite sensitive to climate change, serving as a regulator to regional climate as well as lake ecosystems and a natural resource to sustain human activities on the ice. Lake ice cover and Lake Ice Thickness (LIT) are two key variables in lake ice studies. However, most lake ice studies focus on lake ice cover as opposed to LIT, due mostly to the lack of in situ LIT observations and effective satellite platforms. Consequently, LIT states in ungauged basins remain largely unknown, and critical knowledge gaps need to be filled with breakthroughs in remote sensing observations and retrieval methods. This study reviews the progress in the remote sensing of LIT for the past two decades, focusing on methods based on active and passive microwave information as well as methods based on thermal infrared information. Currently, the best retrieval accuracy of LIT is 0.1—0.2 m for satellite platforms, unable to meet the accuracy requirement (1—2 cm) from the Global Climate Observing System (GCOS). Passive microwave sensors such as AMSR-E and AMSR2 with high temporal-resolution and coarse spatial resolution (~30 km) are efficient for monitoring LIT in large lakes. SAR images can be used to discriminate bedfast lake ice and floating ice, applicable to shallow lakes and lakes with bathymetry information. Waveforms of satellite altimeters containing signals backscattered from the lake ice surface and the lake ice bottom can be used to estimate the traveling time of radar pulse within the ice, which is suitable for ungauged basins because the method is physically based and does not need calibration. Backscattering coefficients from satellite altimeters can also reflect LIT evolution but needs further investigation to improve its robustness. Thermal infrared information such as MODIS land surface temperatures can be used to drive lake ice models to estimate the LIT, but its accuracy is subject to the complicated process of lake surface snow. We summarize the challenges for current LIT studies and indicate future directions for LIT studies. There are four critical issues: (1) The physical process of lake ice and lake surface snow needs to be better resolved to improve the performance of remote sensing and modeling of LIT; (2) In situ measurements of LIT need to be aggregated and standardized in a larger scale to provide sufficient calibration and validation data; (3) Multisource remote sensing information of LIT can be better combined with lake ice modeling through data fusion and data assimilation to derive global LIT datasets with a longer period, higher spatiotemporal resolution and better accuracy; (4) With the development of new generations of satellite platforms, such as the latest altimetry missions (e.g., ICESat-2, Jason-CS, and SWOT) and SAR platforms, advanced methods can be developed to better achieve the LIT monitoring target set by GCOS.

مفهوم

lake ice thickness;brightness temperature;Backscattering coefficient;satellite altimetry;thermal infrared remote sensing

References

  1. 1.
    Antonova S, Duguay C R, Kääb A, Heim B, Langer M, Westermann S and Boike J. 2016. Monitoring bedfast ice and ice phenology in lakes of the Lena River Delta using TerraSAR-X backscatter and coherence time series. Remote Sensing, 8(11): 903
  2. 2.
    Atwood D K, Gunn G E, Roussi C, Wu J F, Duguay C and Sarabandi K. 2015. Microwave backscatter from Arctic lake ice and polarimetric implications. Ieee Transactions on Geoscience and Remote Sensing, 53(11): 5972-5982
  3. 3.
    Aulicino G, Fusco G, Kern S and Budillon G. 2014. Estimation of sea-ice thickness in Ross and Weddell Seas from SSM/I brightness temperatures. IEEE Transactions on Geoscience and Remote Sensing, 52(7): 4122-4140
  4. 4.
    Bai L L, Long D and Yan L. 2019. Estimation of surface soil moisture with downscaled land surface temperatures using a data fusion approach for heterogeneous agricultural land. Water Resources Research, 55(2): 1105-1128
  5. 5.
    Beckers J F, Casey J A and Haas C. 2017. Retrievals of lake ice thickness from Great Slave Lake and Great Bear Lake using CryoSat-2. IEEE Transactions on Geoscience and Remote Sensing, 55(7): 3708-3720
  6. 6.
    Brown L C and Duguay C R. 2011. The fate of lake ice in the North American Arctic. The Cryosphere, 5(4): 869-892
  7. 7.
    Cai Y, Ke C Q and Duan Z. 2017. Monitoring ice variations in Qinghai Lake from 1979 to 2016 using passive microwave remote sensing data. Science of the Total Environment, 607-608: 120-131
  8. 8.
    Cai Y, Ke C Q, Li X G, Zhang G Q, Duan Z and Lee H. 2019. Variations of lake ice phenology on the Tibetan Plateau from 2001 to 2017 based on MODIS data. Journal of Geophysical Research: Atmospheres, 124(2): 825-843
  9. 9.
    Cao J, Yao X J, Jin H A, Zhang T F, Gao Y P, Zhang D H and Zhao Q N. 2021. Spatiotemporal variation of ice thickness of Lake Qinghai derived from field measurements and model simulation. Journal of Lake Sciences, 33(2): 607-621
  10. 10.
    Chen X, Long D, Hong Y, Zeng C and Yan D H. 2017. Improved modeling of snow and glacier melting by a progressive two-stage calibration strategy with GRACE and multisource data: how snow and glacier meltwater contributes to the runoff of the Upper Brahmaputra River basin?. Water Resources Research, 53(3): 2431-2466
  11. 11.
    Cheng B, Cheng Y B, Vihma T, Kontu A, Zheng F, Lemmetyinen J, Qiu Y B and Pulliainen J. 2021. Inter-annual variation in lake ice composition in the European Arctic: observations based on high-resolution thermistor strings. Earth System Science Data, 13(8): 3967-3978
  12. 12.
    Duguay C R, Flato G M, Jeffries M O, Ménard P, Morris K and Rouse W R. 2003. Ice-cover variability on shallow lakes at high latitudes: model simulations and observations. Hydrological Processes, 17(17): 3465-3483
  13. 13.
    Duguay C R, Pultz T J, Lafleur P M and Drai D. 2002. RADARSAT backscatter characteristics of ice growing on shallow sub-Arctic lakes, Churchill, Manitoba, Canada. Hydrological Processes, 16(8): 1631-1644
  14. 14.
    Engram M, Anthony K W, Meyer F J and Grosse G. 2013. Synthetic aperture radar (SAR) backscatter response from methane ebullition bubbles trapped by thermokarst lake ice. Canadian Journal of Remote Sensing, 38(6): 667-682
  15. 15.
    Engram M, Anthony K M W, Sachs T, Kohnert K, Serafimovich A, Grosse G and Meyer F J. 2020. Remote sensing northern lake methane ebullition. Nature Climate Change, 10(6): 511-517
  16. 16.
    Grant L, Vanderkelen I, Gudmundsson L, Tan Z L, Perroud M, Stepanenko V M, Debolskiy A V, Droppers B, Janssen A B G, Woolway R I, Choulga M, Balsamo G, Kirillin G, Schewe J, Zhao F, del Valle I V, Golub M, Pierson D, Marcé R, Seneviratne S I and Thiery W. 2021. Attribution of global lake systems change to anthropogenic forcing. Nature Geoscience, 14(11): 849-854
  17. 17.
    Gunn G E, Brogioni M, Duguay C, Macelloni G, Kasurak A and King J. 2015. Observation and modeling of X- and Ku-band backscatter of snow-covered freshwater lake ice. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(7): 3629-3642
  18. 18.
    Han P F, Long D, Han Z Y, Du M D, Dai L Y and Hao X H. 2019. Improved understanding of snowmelt runoff from the headwaters of China’s Yangtze River using remotely sensed snow products and hydrological modeling. Remote Sensing of Environment, 224: 44-59
  19. 19.
    Han P F, Long D, Li X D, Huang Q, Dai L Y and Sun Z L. 2021. A dual state-parameter updating scheme using the particle filter and high-spatial-resolution remotely sensed snow depths to improve snow simulation. Journal of Hydrology, 594: 125979
  20. 20.
    Hewison T J and English S J. 1999. Airborne retrievals of snow and ice surface emissivity at millimeter wavelengths. IEEE Transactions on Geoscience and Remote Sensing, 37(4): 1871-1879
  21. 21.
    Hirose T, Kapfer M, Bennett J, Cott P, Manson G and Solomon S. 2008. Bottomfast ice mapping and the measurement of ice thickness on tundra lakes using C-band synthetic aperture radar remote sensing. Journal of the American Water Resources Association, 44(2): 285-292
  22. 22.
    Hori Y, Cheng V Y S, Gough W A, Jien J Y and Tsuji L J S. 2018. Implications of projected climate change on winter road systems in Ontario’s Far North, Canada. Climatic Change, 148(1/2): 109-122
  23. 23.
    Huang Q, Long D, Du M D, Zeng C, Li X D, Hou A Z and Hong Y. 2018. An improved approach to monitoring Brahmaputra River water levels using retracked altimetry data. Remote Sensing of Environment, 211: 112-128
  24. 24.
    Huang W F, Cheng B, Zhang J R, Zhang Z, Vihma T, Li Z J and Niu F J. 2019. Modeling experiments on seasonal lake ice mass and energy balance in the Qinghai-Tibet Plateau: a case study. Hydrology and Earth System Sciences, 23(4): 2173-2186
  25. 25.
    Kang K K, Duguay C R, Howell S E L, Derksen C P and Kelly R E J. 2010. Sensitivity of AMSR-E brightness temperatures to the seasonal evolution of lake ice thickness. IEEE Geoscience and Remote Sensing Letters, 7(4): 751-755
  26. 26.
    Kang K K, Duguay C R, Lemmetyinen J and Gel Y. 2014. Estimation of ice thickness on large northern lakes from AMSR-E brightness temperature measurements. Remote Sensing of Environment, 150: 1-19
  27. 27.
    Ke C Q, Cai Y and Xiao Y. 2022. Monitoring ice phenology variations in Khanka Lake based on passive remote sensing data from 1979 to 2019. National Remote Sensing Bulletin, 26(1): 201-210
  28. 28.
    Ke C Q, Tao A Q and Jin X. 2013. Variability in the ice phenology of Nam Co Lake in central Tibet from scanning multichannel microwave radiometer and special sensor microwave/imager: 1978 to 2013. Journal of Applied Remote Sensing, 7(1): 073477
  29. 29.
    Kirillin G, Hochschild J, Mironov D, Terzhevik A, Golosov S and Nützmann G. 2011. FLake-Global: online lake model with worldwide coverage. Environmental Modelling and Software, 26(5): 683-684
  30. 30.
    Kropáček J, Maussion F, Chen F, Hoerz S and Hochschild V. 2013. Analysis of ice phenology of lakes on the Tibetan Plateau from MODIS data. The Cryosphere, 7(1): 287-301
  31. 31.
    Lei R B, Li Z J, Qin J M and Cheng Y F. 2009. Investigation of new technologies for in-situ ice thickness observation. Advances in Water Science, 20(2): 287-292
  32. 32.
    Lemmetyinen J, Derksen C, Pulliainen J, Strapp W, Toose P, Walker A, Tauriainen S, Pihlflyckt J, Karna J P and Hallikainen M T. 2009. A comparison of airborne microwave brightness temperatures and snowpack properties across the boreal forests of Finland and Western Canada. IEEE Transactions on Geoscience and Remote Sensing, 47(3): 965-978
  33. 33.
    Li X D, Long D, Huang Q, Han P F, Zhao F Y and Wada Y. 2019. High-temporal-resolution water level and storage change data sets for lakes on the Tibetan Plateau during 2000-2017 using multiple altimetric missions and Landsat-derived lake shoreline positions. Earth System Science Data, 11(4): 1603-1627
  34. 34.
    Li X D, Long D, Huang Q and Zhao F Y. 2022. The state and fate of lake ice thickness in the Northern Hemisphere. Science Bulletin, 67(5): 537-546
  35. 35.
    Liao J J, Xue H and Chen J M. 2020. Monitoring lake level changes on the Tibetan Plateau from 2000 to 2018 using satellite altimetry data. Journal of Remote Sensing (Chinese), 24(12): 1534-1547
  36. 36.
    Long D, Yan L, Bai L L, Zhang C J, Li X Y, Lei H M, Yang H B, Tian F Q, Zeng C, Meng X Y and Shi C X. 2020. Generation of MODIS-like land surface temperatures under all-weather conditions based on a data fusion approach. Remote Sensing of Environment, 246: 111863
  37. 37.
    Mäkynen M, Cheng B and Similä M. 2013. On the accuracy of thin-ice thickness retrieval using MODIS thermal imagery over Arctic first-year ice. Annals of Glaciology, 54(62): 87-96
  38. 38.
    Matzler C and Wegmuller U. 1987. Dielectric properties of freshwater ice at microwave frequencies. Journal of Physics D: Applied Physics, 20(12): 1623-1630
  39. 39.
    Murfitt J and Duguay C R. 2021. 50 years of lake ice research from active microwave remote sensing: progress and prospects. Remote Sensing of Environment, 264: 112616
  40. 40.
    Murfitt J C, Brown L C and Howell S E L. 2018. Estimating lake ice thickness in Central Ontario. PLoS One, 13(12): e0208519
  41. 41.
    Orru K, Kangur K, Kangur P, Ginter K and Kangur A. 2014. Recreational ice fishing on the large Lake Peipsi: socioeconomic importance, variability of ice-cover period, and possible implications for fish stocks. Estonian Journal of Ecology, 63(4): 282-298
  42. 42.
    Pour H K, Duguay C R, Scott K A and Kang K K. 2017. Improvement of lake ice thickness retrieval from MODIS satellite data using a thermodynamic model. IEEE Transactions on Geoscience and Remote Sensing, 55(10): 5956-5965
  43. 43.
    Qu B, Kang S C, Chen F, Zhang Y J and Zhang G S. 2012. Lake ice and its effect factors in the Nam Co Basin, Tibetan Plateau. Progressus Inquisitiones de Mutatione Climatis, 8(5): 327-333
  44. 44.
    Sharma S, Blagrave K, Magnuson J J, O’Reilly C M, Oliver S, Batt R D, Magee M R, Straile D, Weyhenmeyer G A, Winslow L and Woolway R I. 2019. Widespread loss of lake ice around the Northern Hemisphere in a warming world. Nature Climate Change, 9(3): 227-231
  45. 45.
    Sharma S, Blagrave K, Watson S R, O’Reilly C M, Batt R, Magnuson J J, Clemens T, Denfeld B A, Flaim G, Grinberga L, Hori Y, Laas A, Knoll L B, Straile D, Takamura N and Weyhenmeyer G A. 2020. Increased winter drownings in ice-covered regions with warmer winters. PLoS One, 15(11): e0241222
  46. 46.
    Sharma S, Blagrave K, Filazzola A, Imrit M A and Hendricks Franssen H J. 2021. Forecasting the permanent loss of lake ice in the Northern Hemisphere within the 21st century. Geophysical Research Letters, 48(1): e2020GL091108
  47. 47.
    Song C Q, Ye Q H and Cheng X. 2015. Shifts in water-level variation of Namco in the central Tibetan Plateau from ICESat and CryoSat-2 altimetry and station observations. Science Bulletin, 60(14): 1287-1297
  48. 48.
    Su H and Wang Y P. 2012. Using MODIS data to estimate sea ice thickness in the Bohai Sea (China) in the 2009-2010 winter. Journal of Geophysical Research: Oceans, 117(C10): C10018
  49. 49.
    Wang W, Lee X, Xiao W, Liu S D, Schultz N, Wang Y W, Zhang M and Zhao L. 2018. Global lake evaporation accelerated by changes in surface energy allocation in a warmer climate. Nature Geoscience, 11(6): 410-414
  50. 50.
    Wang X J, Key J R and Liu Y H. 2010. A thermodynamic model for estimating sea and lake ice thickness with optical satellite data. Journal of Geophysical Research: Oceans, 115(C12): C12035
  51. 51.
    Weeks W F, Fountain A G, Bryan M L and Elachi C. 1978. Differences in radar return from ice-covered North Slope lakes. Journal of Geophysical Research: Oceans, 83(C8): 4069-4073
  52. 52.
    Wei Q F and Ye Q H. 2010. Review of lake ice monitoring by remote sensing. Progress in Geography, 29(7): 803-810
  53. 53.
    Wik M, Varner R K, Anthony K W, MacIntyre S and Bastviken D. 2016. Climate-sensitive northern lakes and ponds are critical components of methane release. Nature Geoscience, 9(2): 99-105
  54. 54.
    Woolway R I, Kraemer B M, Lenters J D, Merchant C J, O’Reilly C M and Sharma S. 2020. Global lake responses to climate change. Nature Reviews Earth and Environment, 1(8): 388-403
  55. 55.
    Wu Y H, Guo L N, Fan L X, Wen M X, Chi H J and Zhang B. 2022. Lake ice phenology of the Nam Co at Tibetan Plateau: remote sensing and modelling. National Remote Sensing Bulletin, 26(1): 193-200
  56. 56.
    Yang B, Wells M G, Li J Z and Young J. 2020. Mixing, stratification, and plankton under lake-ice during winter in a large lake: implications for spring dissolved oxygen levels. Limnology and Oceanography, 65(11): 2713-2729
  57. 57.
    Zakharova E, Agafonova S, Duguay C, Frolova N and Kouraev A. 2021. River ice phenology and thickness from satellite altimetry. Potential for ice bridge road operation. The Cryosphere, 15(12): 5387-5407

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website