Progress and prospects of remote sensing of lake ice thickness
- Vol. 26, Issue 7, Pages: 1289-1301(2022)
Published： 07 July 2022
DOI： 10.11834/jrs.20221683
扫 描 看 全 文
浏览全部资源
扫码关注微信
- About Journal
- Journal Online
- Special Source
摘要
湖冰是对气候变化十分敏感的冰冻圈水文变量，起到调节区域气候和湖泊生态系统的作用，并作为一种自然资源服务于冰上生产生活。湖冰覆盖和湖冰厚度是研究湖冰过程的关键变量，目前大多数湖冰研究集中在湖冰覆盖领域，湖冰厚度领域由于缺乏实测资料和专门的遥感观测平台还存在诸多空白，大量缺资料地区湖冰厚度仍处于未知状态，亟待方法和数据上的突破与创新。本文综述了近20年国内外湖冰厚度遥感反演领域的进展，介绍了各类反演方法的原理机理及主要优缺点，其中被动微波遥感方法具有良好的时间分辨率但空间分辨率较粗，难以覆盖中小型湖泊；基于SAR影像的主动微波方法空间分辨率较高但物理机制复杂，可靠性有待进一步验证；基于测高雷达的主动微波方法观测时段较长，物理机制明确，易于拓展到无实测资料湖泊，但空间覆盖相对有限；热红外遥感方法时空分辨率较好，但容易受云和湖冰表面积雪的影响，反演精度和可靠性有待提升。在此基础上，本文分析湖冰厚度遥感反演领域面临的主要挑战和发展方向包括：（1）厘清湖冰表面积雪相关的物理过程；（2）实现大范围实测冰厚和遥感资料的集成；（3）实现多源遥感冰厚反演方法的交叉融合。
Abstract
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.
关键词
湖冰厚度亮度温度后向散射系数卫星测高热红外遥感
Keywords
lake ice thicknessbrightness temperaturebackscattering coefficientsatellite altimetrythermal infrared remote sensing
references
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 [DOI: 10.3390/rs8110903http://dx.doi.org/10.3390/rs8110903]
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 [DOI: 10.1109/tgrs.2015.2429917http://dx.doi.org/10.1109/tgrs.2015.2429917]
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 [DOI: 10.1109/TGRS.2013.2279799http://dx.doi.org/10.1109/TGRS.2013.2279799]
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 [DOI: 10.1029/2018wr024162http://dx.doi.org/10.1029/2018wr024162]
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 [DOI: 10.1109/tgrs.2017.2677583http://dx.doi.org/10.1109/tgrs.2017.2677583]
Brown L C and Duguay C R. 2011. The fate of lake ice in the North American Arctic. The Cryosphere, 5(4): 869-892 [DOI: 10.5194/tc-5-869-2011http://dx.doi.org/10.5194/tc-5-869-2011]
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 [DOI: 10.1016/j.scitotenv.2017.07.027http://dx.doi.org/10.1016/j.scitotenv.2017.07.027]
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 [DOI: 10.1029/2018JD028993http://dx.doi.org/10.1029/2018JD028993]
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
曹娟, 姚晓军, 靳惠安, 张调风, 高永鹏, 张大弘, 赵全宁. 2021. 基于实测与模拟的青海湖冰厚时空变化特征. 湖泊科学, 33(2): 607-621 [DOI: 10.18307/2021.0225http://dx.doi.org/10.18307/2021.0225]
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 [DOI: 10.1002/2016wr019656http://dx.doi.org/10.1002/2016wr019656]
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 [DOI: 10.5194/essd-13-3967-2021http://dx.doi.org/10.5194/essd-13-3967-2021]
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 [DOI: 10.1002/hyp.1394http://dx.doi.org/10.1002/hyp.1394]
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 [DOI: 10.1002/hyp.1026http://dx.doi.org/10.1002/hyp.1026]
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 [DOI: 10.5589/m12-054http://dx.doi.org/10.5589/m12-054]
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 [DOI: 10.1038/s41558-020-0762-8http://dx.doi.org/10.1038/s41558-020-0762-8]
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 [DOI: 10.1038/s41561-021-00833-xhttp://dx.doi.org/10.1038/s41561-021-00833-x]
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 [DOI: 10.1109/jstars.2015.2420411http://dx.doi.org/10.1109/jstars.2015.2420411]
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 [DOI: 10.1016/j.rse.2019.01.041http://dx.doi.org/10.1016/j.rse.2019.01.041]
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 [DOI: 10.1016/j.jhydrol.2021.125979http://dx.doi.org/10.1016/j.jhydrol.2021.125979]
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 [DOI: 10.1109/36.774700http://dx.doi.org/10.1109/36.774700]
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 [DOI: 10.1111/j.1752-1688.2007.00161.xhttp://dx.doi.org/10.1111/j.1752-1688.2007.00161.x]
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 [DOI: 10.1007/s10584-018-2178-2http://dx.doi.org/10.1007/s10584-018-2178-2]
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 [DOI: 10.1016/j.rse.2018.04.018http://dx.doi.org/10.1016/j.rse.2018.04.018]
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 [DOI: 10.5194/hess-23-2173-2019http://dx.doi.org/10.5194/hess-23-2173-2019]
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 [DOI: 10.1109/lgrs.2010.2044742http://dx.doi.org/10.1109/lgrs.2010.2044742]
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 [DOI: 10.1016/j.rse.2014.04.016http://dx.doi.org/10.1016/j.rse.2014.04.016]
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
柯长青, 蔡宇, 肖瑶. 2022. 1979年—2019年兴凯湖湖冰物候变化的被动微波遥感监测. 遥感学报, 26(1): 201-210 [DOI: 10.11834/jrs.20221267http://dx.doi.org/10.11834/jrs.20221267]
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 [DOI: 10.1117/1.JRS.7.073477http://dx.doi.org/10.1117/1.JRS.7.073477]
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 [DOI: 10.1016/j.envsoft.2010.12.004http://dx.doi.org/10.1016/j.envsoft.2010.12.004]
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 [DOI: 10.5194/tc-7-287-2013http://dx.doi.org/10.5194/tc-7-287-2013]
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
雷瑞波, 李志军, 秦建敏, 程言峰. 2009. 定点冰厚观测新技术研究. 水科学进展, 20(2): 287-292
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 [DOI: 10.1109/tgrs.2008.2006358http://dx.doi.org/10.1109/tgrs.2008.2006358]
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 [DOI: 10.5194/essd-11-1603-2019http://dx.doi.org/10.5194/essd-11-1603-2019]
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 [DOI: 10.1016/j.scib.2021.10.015http://dx.doi.org/10.1016/j.scib.2021.10.015]
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
廖静娟, 薛辉, 陈嘉明. 2020. 卫星测高数据监测青藏高原湖泊2010年—2018年水位变化. 遥感学报, 24(12): 1534-1547 [DOI: 10.11834/jrs.20209281http://dx.doi.org/10.11834/jrs.20209281]
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 [DOI: 10.1016/j.rse.2020.111863http://dx.doi.org/10.1016/j.rse.2020.111863]
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 [DOI: 10.3189/2013AoG62A166http://dx.doi.org/10.3189/2013AoG62A166]
Matzler C and Wegmuller U. 1987. Dielectric properties of freshwater ice at microwave frequencies. Journal of Physics D: Applied Physics, 20(12): 1623-1630 [DOI: 10.1088/0022-3727/20/12/013http://dx.doi.org/10.1088/0022-3727/20/12/013]
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 [DOI: 10.1016/j.rse.2021.112616http://dx.doi.org/10.1016/j.rse.2021.112616]
Murfitt J C, Brown L C and Howell S E L. 2018. Estimating lake ice thickness in Central Ontario. PLoS One, 13(12): e0208519 [DOI: 10.1371/journal.pone.0208519http://dx.doi.org/10.1371/journal.pone.0208519]
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 [DOI: 10.3176/eco.2014.4.06http://dx.doi.org/10.3176/eco.2014.4.06]
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 [DOI: 10.1109/tgrs.2017.2718533http://dx.doi.org/10.1109/tgrs.2017.2718533]
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
曲斌, 康世昌, 陈锋, 张拥军, 张国帅. 2012. 2006年—2011年西藏纳木错湖冰状况及其影响因素分析. 气候变化研究进展, 8(5): 327-333 [DOI: 10.3969/j.issn.1673-1719.2012.05.003http://dx.doi.org/10.3969/j.issn.1673-1719.2012.05.003]
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 [DOI: 10.1038/s41558-018-0393-5http://dx.doi.org/10.1038/s41558-018-0393-5]
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 [DOI: 10.1371/journal.pone.0241222http://dx.doi.org/10.1371/journal.pone.0241222]
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 [DOI: 10.1029/2020gl091108http://dx.doi.org/10.1029/2020gl091108]
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 [DOI: 10.1007/s11434-015-0826-8http://dx.doi.org/10.1007/s11434-015-0826-8]
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 [DOI: 10.1029/2012JC008251http://dx.doi.org/10.1029/2012JC008251]
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 [DOI: 10.1038/s41561-018-0114-8http://dx.doi.org/10.1038/s41561-018-0114-8]
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 [DOI: 10.1029/2009jc005857http://dx.doi.org/10.1029/2009jc005857]
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 [DOI: 10.1029/JC083iC08p04069http://dx.doi.org/10.1029/JC083iC08p04069]
Wei Q F and Ye Q H. 2010. Review of lake ice monitoring by remote sensing. Progress in Geography, 29(7): 803-810
魏秋方, 叶庆华. 2010. 湖冰遥感监测方法综述. 地理科学进展, 29(7): 803-810 [DOI: 10.11820/dlkxjz.2010.07.005http://dx.doi.org/10.11820/dlkxjz.2010.07.005]
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 [DOI: 10.1038/ngeo2578http://dx.doi.org/10.1038/ngeo2578]
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 [DOI: 10.1038/s43017-020-0067-5http://dx.doi.org/10.1038/s43017-020-0067-5]
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
吴艳红, 郭立男, 范兰馨, 文梦宣, 迟皓婧, 张兵. 2022. 青藏高原纳木错湖冰物候变化遥感监测与模拟. 遥感学报, 26(1): 193-200 [DOI: 10.11834/jrs.20221288http://dx.doi.org/10.11834/jrs.20221288]
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 [DOI: 10.1002/lno.11543http://dx.doi.org/10.1002/lno.11543]
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 [DOI: 10.5194/tc-15-5387-2021http://dx.doi.org/10.5194/tc-15-5387-2021]
Alert me when the article has been cited
提交
相关文章
相关作者
暂无数据
相关机构
Water Conservancy and Civil Engineering College, Inner Mongolia Key Laboratory of Water Ｒresource Protection and Utilization, Inner Mongolia Agricultural University
Southern Marine Science and Engineering Guangdong Laboratory
School of Geospatial Engineering and Science, Sun Yat-sen University
College of Geodesy and Geomatics, Shandong University of Science and Technology
School of Geodesy and Geomatics, Wuhan University
选择翻译语言
- Postal code：100190
- Tel：010-58887052 Email：nrsb@aircas.ac.cn
- Technical support is provided by Beijing Founder electronics co., LTD 京ICP备20021838号-9
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
0
Bulk Citation
Export to
TOP