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An Assessment of Snow Cover Duration Variability Among Three Basins of Songhua River in Northeast China Using Binary Decision Tree

YANG Qian,  SONG Kaishan,  HAO Xiaohua,  CHEN Shengbo,  ZHU Bingxue

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文章导航 > Chinese Geographical Science  > 2018 >  28(6): 946-956
YANG Qian, SONG Kaishan, HAO Xiaohua, CHEN Shengbo, ZHU Bingxue. An Assessment of Snow Cover Duration Variability Among Three Basins of Songhua River in Northeast China Using Binary Decision Tree[J]. 中国地理科学, 2018, 28(6): 946-956. doi: 10.1007/s11769-018-1004-0
引用本文: YANG Qian, SONG Kaishan, HAO Xiaohua, CHEN Shengbo, ZHU Bingxue. An Assessment of Snow Cover Duration Variability Among Three Basins of Songhua River in Northeast China Using Binary Decision Tree[J]. 中国地理科学, 2018, 28(6): 946-956. doi: 10.1007/s11769-018-1004-0
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YANG Qian, SONG Kaishan, HAO Xiaohua, CHEN Shengbo, ZHU Bingxue. An Assessment of Snow Cover Duration Variability Among Three Basins of Songhua River in Northeast China Using Binary Decision Tree[J]. Chinese Geographical Science, 2018, 28(6): 946-956. doi: 10.1007/s11769-018-1004-0
Citation: YANG Qian, SONG Kaishan, HAO Xiaohua, CHEN Shengbo, ZHU Bingxue. An Assessment of Snow Cover Duration Variability Among Three Basins of Songhua River in Northeast China Using Binary Decision Tree[J]. Chinese Geographical Science, 2018, 28(6): 946-956. doi: 10.1007/s11769-018-1004-0
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An Assessment of Snow Cover Duration Variability Among Three Basins of Songhua River in Northeast China Using Binary Decision Tree

doi: 10.1007/s11769-018-1004-0
  • 1.

    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;

  • 2.

    College of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130000, China;

  • 3.

    China Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China;

  • 4.

    College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China

基金项目: Under the auspices of National Natural Science Foundation of China (No. 41471291, 41801283, 41070104), Startup Foundation for Doctors of Jilin Jianzhu University (No. 861111), 13th Five-Year Plan of Technical and Social Research Project for Jilin Colleges (No. JJKH20170257KJ)
详细信息
    通讯作者:

    SONG Kaishan.E-mail:songks@neigae.ac.cn

An Assessment of Snow Cover Duration Variability Among Three Basins of Songhua River in Northeast China Using Binary Decision Tree

  • 1.

    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;

  • 2.

    College of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130000, China;

  • 3.

    China Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China;

  • 4.

    College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China

Funds: Under the auspices of National Natural Science Foundation of China (No. 41471291, 41801283, 41070104), Startup Foundation for Doctors of Jilin Jianzhu University (No. 861111), 13th Five-Year Plan of Technical and Social Research Project for Jilin Colleges (No. JJKH20170257KJ)
More Information
    Corresponding author: SONG Kaishan.E-mail:songks@neigae.ac.cn

  • 摘要: The dynamics of snow cover differs greatly from basin to basin in the Songhua River of Northeast China, which is attributable to the differences in the topographic shift as well as changes in the vegetation and climate since the hydrological year (HY) 2003. Daily and flexible multi-day combinations from the HY 2003 to 2014 were produced using Moderate Resolution Imaging Spectroradiometer (MODIS) from Terra and Aqua remote sensing satellites for the snow cover products in the three basins including the Nenjiang River Basin (NJ), Downstream Songhua River Basin (SD) and Upstream Songhua River Basin (SU). Snow cover duration (SCD) was derived from flexible multiday combination each year. The results showed that SCD was significantly associated with elevation, and higher SCD values were found out in the mountainous areas. Further, the average SCDs of NJ, SU and SD basins were 69.43, 98.14 and 88.84 d with an annual growth of 1.36, 2.04 and 2.71 d, respectively. Binary decision tree was used to analyze the nonlinear relationships between SCD and six impact factors, which were successfully applied to simulate the spatial distribution of depth and water equivalent of snow. The impact factors included three topographic factors (elevation, aspect and slope), two climatic factors (precipitation and air temperature) and one vegetation index (Normalized Difference Vegetation Index, NDVI). By treating yearly SCD values as dependent variables and six climatic factors as independent variables, six binary decision trees were built through the combination classification and regression tree (CART) with and without the consideration of climate effect. The results from the model show that elevation, precipitation and air temperature are the three most influential factors, among which air temperature is the most important and ranks first in two of the three studied basins. It is suggested that SCD in the mountainous areas might be more sensitive to climate warming, since precipitation and air temperature are the major factors controlling the persistence of snow cover in the mountainous areas.
    Abstract: The dynamics of snow cover differs greatly from basin to basin in the Songhua River of Northeast China, which is attributable to the differences in the topographic shift as well as changes in the vegetation and climate since the hydrological year (HY) 2003. Daily and flexible multi-day combinations from the HY 2003 to 2014 were produced using Moderate Resolution Imaging Spectroradiometer (MODIS) from Terra and Aqua remote sensing satellites for the snow cover products in the three basins including the Nenjiang River Basin (NJ), Downstream Songhua River Basin (SD) and Upstream Songhua River Basin (SU). Snow cover duration (SCD) was derived from flexible multiday combination each year. The results showed that SCD was significantly associated with elevation, and higher SCD values were found out in the mountainous areas. Further, the average SCDs of NJ, SU and SD basins were 69.43, 98.14 and 88.84 d with an annual growth of 1.36, 2.04 and 2.71 d, respectively. Binary decision tree was used to analyze the nonlinear relationships between SCD and six impact factors, which were successfully applied to simulate the spatial distribution of depth and water equivalent of snow. The impact factors included three topographic factors (elevation, aspect and slope), two climatic factors (precipitation and air temperature) and one vegetation index (Normalized Difference Vegetation Index, NDVI). By treating yearly SCD values as dependent variables and six climatic factors as independent variables, six binary decision trees were built through the combination classification and regression tree (CART) with and without the consideration of climate effect. The results from the model show that elevation, precipitation and air temperature are the three most influential factors, among which air temperature is the most important and ranks first in two of the three studied basins. It is suggested that SCD in the mountainous areas might be more sensitive to climate warming, since precipitation and air temperature are the major factors controlling the persistence of snow cover in the mountainous areas.
  • [1] Balk B, Elder K, 2000. Combining binary decision tree and geo-statistical methods to estimate snow distribution in a mountain watershed. Water Resources Research, 36(1):13-26. doi:10. 1029/1999WR900251
    [2] Breiman L, Friedman J H, Olshen J H et al., 1984. Classification and Regression Trees. New York:Chapman and Hall.
    [3] Brest C L, Rossow, W B, 1992. Radiometric calibration and mon-itoring of NOAA AVHRR data for ISCCP. International Journal of Remote Sensing, 13(2):235-273. doi: 10.1080/01431169208904037
    [4] Chang A T C, Rango A, 2000. Algorithm Theoretical Basis Doc-ument (ATBD) for the AMSR-E Snow Water Equivalent Algo-rithm. Greenbelt:NASA Goddard Space Flight Center.
    [5] Chang A T C, Foster J L, Hall D K, 2016. Nimbus-7 SMMR de-rived global snow cover parameters. Annals of Glaciology, 9:39-44. doi: 10.1017/S0260305500000355
    [6] Chen S B, Yang Q, Xie H J et al., 2014. Spatio-temporal variations of snow cover in Northeast China based on flexible multiday combinations of MODIS snow cover products. Journal of Applied Remote Sensing, 8(1):5230-5237. doi:10.1117/1. JRS.8. 084685
    [7] Daly C, Neilson R P, Phillips D L, 2010. A Statistical-topographic model for mapping climatological precipitation over moun-tainous terrain. Journal of Applied Meteorology, 33(33):140-158. doi:10.1175/1520-0450(1994)033<0140:ASTM FM>2.0.CO;2
    [8] Davis R E, Hardy J P, Ni W et al., 1997. Variation of snow cover ablation in the boreal forest:a sensitivity study on the effects of conifer canopy. Journal of Geophysical Research Atmospheres, 102(24):29389-29395. doi: 10.1029/97JD01335
    [9] Deems J S, Fassnacht S R, Elder K J, 2006. Fractal Distribution of Snow Depth from Lidar Data. Journal of Hydrometeorology, 7(2):285-297. doi: 10.1175/JHM487.1
    [10] Derksen C, 2008. The contribution of AMSR-E 18.7 and 10.7 GHz measurements to improved boreal forest snow water equivalent retrievals. Remote Sensing of Environment, 112(5):2701-2710.doi: 10.1016/j.rse.2008.01.001
    [11] Dozier J, 1980. A clear-sky spectral solar radiation model for snow-covered mountainous terrain. Water Resources Research, 16:709-718. doi: 10.1029/WR016i004p00709
    [12] Dozier J, 1989. Spectral signature of alpine snow cover from the landsat thematic mapper. Remote Sensing of Environment, 28(73):9-22. doi: 10.1016/0034-4257(89)90101-6
    [13] Elder K J, 1995. Snow Distribution in Alpine Watersheds. Santa Barbara:University of California.
    [14] Elder K, Michaelsen J, Dozier J, 1995. Small basin modeling of snow water equivalence using binary regression tree methods. In Biogeochemistry of Seasonally Snow-Covered Catchments. IAHS Publication, 228:129-139.
    [15] Elder K, Rosenthal W, Davis R E, 1998. Estimating the spatial distribution of snow water equivalence in a montane watershed. Hydrological Processes, 12 (10):1793-1808. doi:10.1002/(SICI)1099-1085(199808/09)12:10/11<1793::AID-HYP69 5>3.3. CO; 2-B
    [16] Foster J L, Hall D K, Chang A T C et al., 1999. Effects of snow crystal shape on the scattering of passive microwave radiation. Geoscience & Remote Sensing IEEE Transactions on Selected Topics, 37(2):1165-1168. doi: 10.1109/36.752235
    [17] Gafurov A, Bárdossy A, 2009. Cloud removal methodology from MODIS snow cover product. Hydrology & Earth System Sci-ences, 13(7):1361-1373. doi: 10.5194/hess-13-1361-2009
    [18] Gao Y, Xie H J, Lu N et al., 2010. Toward advanced daily cloud-free snow cover and snow water equivalent products from Terra-Aqua MODIS and Aqua AMSR-E measurement. Journal of Hydrology, 385(1):23-35. doi:10.1016/j.jhydrol.2010. 01.022
    [19] Gao Y, Xie H J, Yao T D et al., 2010. Integrated assessment on multi-temporal and multi-sensor combinations for reducing cloud obscuration of MODIS snow cover products of the Pa-cific Northwest USA. Remote Sensing of Environment, 114(8):1662-1675. doi:10.1016/j.rse. 2010.02.017
    [20] George J, Weiler M, Gluns D R et al., 2007. The influence of forest and topography on snow accumulation and melt at the watershed-scale. Journal of Hydrology, 347(1):101-115. doi: 10.1016/j.jhydrol.2007.09.006
    [21] Knowles N, Dettinger M D, Cayan D R, 2005. Trends in snowfall versus rainfall in the western United States. Journal of Climate, 19(18):4545-4559. doi: 10.1175/JCLI3850.1
    [22] Hall D K, Riggs G A, Salomonson V V, 1995. Development of methods for mapping global snow cover using moderate reso-lution imaging spectroradiometer data. Remote Sensing of En-vironment, 54(2):127-140. doi:10.1016/0034-4257(95) 00137-P
    [23] Hall, D K, Riggs G A, Salomonson V V et al., 2001. Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow and Sea Ice-Mapping Algorithms. Greenbelt:NASA Goddard Space Flight Center.
    [24] Hall D K, Riggs G A, Salomonson V V et al., 2002. MODIS snow-cover products. Remote Sensing of Environment, 83(1):181-194. doi: 10.1016/S0034-4257(02)00095-0
    [25] Hall D K, Riggs G A, 2010. Accuracy assessment of the MODIS snow products. Hydrological Processes, 21(12):1534-1547. doi: 10.1002/hyp.6715
    [26] Hall D K, Riggs G A, Foster J L et al., 2010. Development and evaluation of a cloud-gap-filled MODIS daily snow-cover product. Remote Sensing of Environment, 114(3):496-503. doi: 10.1016/j.rse.2009.10.007.
    [27] Ke C Q, Li L Y, 2014. Analysis of spatiotemporal snow cover variations in Northeast China based on moderate-resolution-imaging spectroradiometer data. Journal of Applied Remote Sensing, 8(1):084695. doi: 10.1117/1.JRS.8.084695
    [28] Lei Xiaochun, Song Kaishan, Wang Zongming et al., 2011. Ac-curacy evaluation of MODIS and AMSR-E snow cover prod-ucts in the Heilongjiang drainage basin. Journal of the Gradu-ate School of the Chinese Academy of Sciences, 28(1):43-50. (in Chinese)
    [29] Lettenmaier D P, 2005. Potential impacts of a warming climate on water availability in snow-dominated regions. Nature, 438(7066):303. doi: 10.1038/nature04141
    [30] Liang T G, Zhang X T, Xie H J et al., 2008. Toward improved daily snow cover mapping with advanced combination of MODIS and AMSR-E measurements. Remote Sensing of En-vironment, 112(10):3750-3761. doi:10.1016/j.rse.2008.05. 010
    [31] Litaor M I, Williams M, Seastedt T R, 2015. Topographic controls on snow distribution, soil moisture, and species diversity of herbaceous alpine vegetation, Niwot Ridge, Colorado. Journal of Geophysical Research Biogeosciences, 113(2):73-73. doi: 10.1029/2007JG000419
    [32] Li Zhen, Qi Chengliu and Sun Wenchang, 1993. Natural Geog-raphy in Northeast China. Beijing:Higher Education Press. (in Chinese)
    [33] Loveland T R, Belward A S, 1997. The IGBP-DIS global 1km land cover data set, DISCover:First results. International Journal of Remote Sensing, 18(15):3289-3295. doi: 10.1080/014311697217099
    [34] Loveland T R, Reed B C, Brown J F et al., 2000. Development of a global land cover characteristics database and IGBP DIS-Cover from 1 km AVHRR data. International Journal of Re-mote Sensing, 21(6):1303-1330. doi:10.1080/0143116002 10191
    [35] Lucas R M, Harrison, A R, 1990. Snow observation by satellite:A review. Remote Sensing Reviews, 4(2):285-348. doi: 10.1080/002757259009532109
    [36] Molotch N P, Meromy L, 2014. Physiographic and climatic con-trols on snow cover persistence in the Sierra Nevada Moun-tains. Hydrological Processes, 28(16):4573-4586. doi: 10.1002/hyp.10254
    [37] Molotch N P, Bales R C, 2005. Scaling snow observations from the point to the grid element:implications for observation network design. Water Resources Research, 41(11):1-17. doi: 10.1029/2005WR004229
    [38] Parajka J, Pepe M, Rampini A et al., 2010. A regional snow-line method for estimating snow cover from MODIS during cloud cover. Journal of Hydrology, 381(3):203-212. doi: 10.1029/2007WR006204
    [39] Paudel K P, Andersen P, 2011. Monitoring snow cover variability in an agropastoral area in the Trans Himalayan region of Nepal using MODIS data with improved cloud removal methodology. Remote Sensing of Environment, 115(5):1234-1246. doi: 10.1016/j.rse.2011.01.006
    [40] Prokop A, 2008. Assessing the applicability of terrestrial laser scanning for spatial snow depth measurements. Cold Regions Science & Technology, 54(3):155-163. doi:10.1016/j. colder-gions.2008.07.002
    [41] Pulliainen J, Hallikainen M, 2001. Retrieval of regional snow water equivalent from space-borne passive microwave obser-vations. Remote Sensing of Environment, 75(1):76-85. doi: 10.1016/S0034-4257(00)00157-7
    [42] Rosenthal W, Dozier J, 1996. Automated mapping of montane snow cover at subpixel resolution from the Landsat Thematic Mapper. Water Resources Research, 32(1):115-130. doi: 10.1029/95WR02718
    [43] Rutger D, Steven M, Jong D, 2004. Monitoring snow-cover dy-namics in Northern Fennoscandia with SPOT VEGETATION images. International Journal of Remote Sensing, 25(15):2933-2949. doi: 10.1080/01431160310001618374
    [44] Sacks W J, Schimel D S, Monson R K, 2007. Coupling between carbon cycling and climate in a high-elevation, subalpine forest:a model-data fusion analysis. Oecologia, 151(1):54-68. doi: 10.1007/s00442-006-0565-2
    [45] Song K S, Zhang Y Z, Jin C et al., 2008. Seasonal snow monitor-ing in Northeast China using space-borne sensors:preliminary results. Geographic Information Sciences, 14(2):113-119. doi: 10.1080/10824000809480646
    [46] Stocker T, Qin D H, Plattner G K et al., 2014. Climate Change, 2013:The Physical Science Basis. Cambridge and New York:Cambridge University Press.
    [47] Sun Xiuzhong, Sun Zhaobo, Luo Yong, 2010. Study on snow cover changes in Northeast China from 1960 to 2005, Journal of Meteorology and Environment, 26(1):1-5. (in Chinese)
    [48] Tong J, Déry S J, Jackson P L, 2009. Topographic control of snow distribution in an alpine watershed of western Canada inferred from spatially-filtered MODIS snow products. Hydrology and Earth System Sciences, 11(4):319-326. doi: 10.5194/hessd-5-2347-2008
    [49] Trujillo E, Molotch N P, Goulden M L et al., 2012. Eleva-tion-dependent influence of snow accumulation on forest greening. Nature Geoscience, 5(10):705-709. doi: 10.1038/ngeo1571
    [50] Wang X W, Xie H J, Liang T G et al., 2010. Comparison and validation of MODIS standard and new combination of Terra and Aqua snow cover products in northern Xinjiang, China. Hydrological Processes, 23(3):419-429. doi:10.1002/hyp. 7151
    [51] Wang X W, Xie H J, 2009. New methods for studying the spatio-temporal variation of snow cover based on combination prod-ucts of MODIS Terra and Aqua. Journal of Hydrology, 371(1):192-200. doi: 10.1016/j.jhydrol.2009.03.028
    [52] Winstral A, Elder K, Davis R E, 2002. Spatial snow modeling of wind-redistributed snow using terrain-based parameters. Journal of Hydrometeorology, 3(5):524-538. doi: 10.1175/1525-7541(2002)003<0524:SSMOWR>2.0.CO;2
    [53] Xiao X, Zhang Q, Boles S et al., 2004. Mapping snow cover in the pan-Arctic zone, using multi-year (1998-2001) images from optical VEGETATION sensor. International Journal of Remote Sensing, 25(24):5731-5744. doi:10.1080/0143116041 00017 19867
    [54] Yohannes Y, Hoddinott J, 1999. Classification and Regression Trees:An Introduction. Washington D.C:International Food Policy Research Institute.
    [55] Zhang G Q, Xie H J, Yao T D et al., 2012. Snow cover dynamics of four lake basins over Tibetan Plateau using time series MODIS data (2001-2010). Water Resources Research, 48(10):10529. doi: 10.1029/2012WR011971
    [56] Zhong Guixin, Song Kaishan, Wang Zongmin et al., 2010. Veri-fication and Comparison of the MODIS and AMSR-E Snow Cover Products in Northeast China. Journal of Glaciology & Geocryology, 32(6):1262-1269. (in Chinese)
    [57] Zhou X, Xie H, Hendrickx J M H, 2005. Statistical evaluation of remotely sensed snow-cover products with constraints from streamflow and SNOTEL measurements. Remote Sensing of Environment, 94(2):214-231. doi: 10.1016/j.rse.2004.10.00
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  • 收稿日期:  2017-09-28
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An Assessment of Snow Cover Duration Variability Among Three Basins of Songhua River in Northeast China Using Binary Decision Tree

doi: 10.1007/s11769-018-1004-0
  • 1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;
  • 2. College of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130000, China;
  • 3. China Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China;
  • 4. College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China
    • 基金项目:  Under the auspices of National Natural Science Foundation of China (No. 41471291, 41801283, 41070104), Startup Foundation for Doctors of Jilin Jianzhu University (No. 861111), 13th Five-Year Plan of Technical and Social Research Project for Jilin Colleges (No. JJKH20170257KJ)
    通讯作者: SONG Kaishan.E-mail:songks@neigae.ac.cn
  • 收稿日期: 2017-09-28
  • 修回日期: 2018-01-16
  • 刊出日期: 2018-12-27

摘要: The dynamics of snow cover differs greatly from basin to basin in the Songhua River of Northeast China, which is attributable to the differences in the topographic shift as well as changes in the vegetation and climate since the hydrological year (HY) 2003. Daily and flexible multi-day combinations from the HY 2003 to 2014 were produced using Moderate Resolution Imaging Spectroradiometer (MODIS) from Terra and Aqua remote sensing satellites for the snow cover products in the three basins including the Nenjiang River Basin (NJ), Downstream Songhua River Basin (SD) and Upstream Songhua River Basin (SU). Snow cover duration (SCD) was derived from flexible multiday combination each year. The results showed that SCD was significantly associated with elevation, and higher SCD values were found out in the mountainous areas. Further, the average SCDs of NJ, SU and SD basins were 69.43, 98.14 and 88.84 d with an annual growth of 1.36, 2.04 and 2.71 d, respectively. Binary decision tree was used to analyze the nonlinear relationships between SCD and six impact factors, which were successfully applied to simulate the spatial distribution of depth and water equivalent of snow. The impact factors included three topographic factors (elevation, aspect and slope), two climatic factors (precipitation and air temperature) and one vegetation index (Normalized Difference Vegetation Index, NDVI). By treating yearly SCD values as dependent variables and six climatic factors as independent variables, six binary decision trees were built through the combination classification and regression tree (CART) with and without the consideration of climate effect. The results from the model show that elevation, precipitation and air temperature are the three most influential factors, among which air temperature is the most important and ranks first in two of the three studied basins. It is suggested that SCD in the mountainous areas might be more sensitive to climate warming, since precipitation and air temperature are the major factors controlling the persistence of snow cover in the mountainous areas.

English Abstract

YANG Qian, SONG Kaishan, HAO Xiaohua, CHEN Shengbo, ZHU Bingxue. An Assessment of Snow Cover Duration Variability Among Three Basins of Songhua River in Northeast China Using Binary Decision Tree[J]. 中国地理科学, 2018, 28(6): 946-956. doi: 10.1007/s11769-018-1004-0
引用本文: YANG Qian, SONG Kaishan, HAO Xiaohua, CHEN Shengbo, ZHU Bingxue. An Assessment of Snow Cover Duration Variability Among Three Basins of Songhua River in Northeast China Using Binary Decision Tree[J]. 中国地理科学, 2018, 28(6): 946-956. doi: 10.1007/s11769-018-1004-0
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YANG Qian, SONG Kaishan, HAO Xiaohua, CHEN Shengbo, ZHU Bingxue. An Assessment of Snow Cover Duration Variability Among Three Basins of Songhua River in Northeast China Using Binary Decision Tree[J]. Chinese Geographical Science, 2018, 28(6): 946-956. doi: 10.1007/s11769-018-1004-0
Citation: YANG Qian, SONG Kaishan, HAO Xiaohua, CHEN Shengbo, ZHU Bingxue. An Assessment of Snow Cover Duration Variability Among Three Basins of Songhua River in Northeast China Using Binary Decision Tree[J]. Chinese Geographical Science, 2018, 28(6): 946-956. doi: 10.1007/s11769-018-1004-0
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