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纸质出版日期： 2021-03-07 ，
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吴铭，黄珏，宫丽娇，江涛.2021.长时序Landsat的北极Lena河DOC浓度变化及驱动力分析.遥感学报，25（3）： 830-845
Wu M，Huang J，Gong L J and Jiang T. 2021. Analysis of DOC concentration variation and driving forces in the Arctic River Lena based on long-term Landsat time series. National Remote Sensing Bulletin， 25（3）：830-845
北极Lena河是世界第十长河，也是北极地区第二大河，其年径流量约占北冰洋淡水总量的20％，同时将陆地生态系统上的大量有机物携带进海洋，并且在全球碳循环中起到非常重要的作用。卫星遥感数据是监测河流有机碳循环的重要数据源，特别是在无冰期的高纬度地区。本研究的目的是：（1）建立一种高精度反演算法估算北极Lena河的溶解有机碳DOC（Dissolved Organic Carbon）浓度；（2）分析长时间序列遥感影像中DOC浓度的变化特征；（3）探讨北极Lena河的DOC浓度变化的主要驱动因素。本文中主要构建了一种基于谷歌地球引擎GEE的遥感反演算法，利用1999年—2018年的Landsat影像反演得到了北极Lena河的有色溶解有机物CDOM（Chromophoric Dissolved Organic Matter）浓度。基于CDOM与DOC这两种水体成分的实地测量值之间的强相关性（
R
2
=0.873），本文将CDOM反演结果转换为DOC浓度。在此基础上，本文分析了近20年的北极Lena河无冰期DOC的时空动态变化。研究结果表明反演算法所表现的性能证实了使用Landsat系列不同传感器长时间监测河流DOC变化的能力。利用增强回归树模型分析了北极Lena河的DOC变化的是土地覆盖变化、流域坡度、气象因子、人类活动以及纬度地带性等众多驱动因素的共同影响，而季节性，地域性和规模性也会影响DOC浓度与上述驱动因素之间的定量关系。总之，本文的结果可以提高监测北极地区不同河流中DOC变化及其通量的能力，并加深对北极碳循环的了解。
The Arctic River Lena is the 10th longest river in the world and the second largest river in the Arctic region. The annual river discharge of the Arctic River Lena accounts for approximately 20% of the total freshwater in the Arctic Ocean. It also drains a large amount of organic matter from terrestrial ecosystems into the ocean and plays a very important role in the global carbon cycle. Satellite remote sensing data are considered a necessary supplement to the ground-based monitoring of riverine organic matter circulation
especially in high-latitude regions during the ice-free period.
The objectives of this study are to (1) construct a high-accuracy retrieval algorithm to estimate the Dissolved Organic Carbon （DOC） concentration of the Arctic River Lena
(2) analyze the variation characteristics of DOC concentration over a long time series using remote sensing images
and (3) discuss the main driving factors of DOC concentration variation in the Arctic River Lena.
In this paper
a remote sensing retrieval algorithm based on the Google Earth engine was constructed. Landsat images retrieved from 1999 to 2018 were used to obtain the concentration of Chromophoric Dissolved Organic Matter (CDOM) in the Arctic River Lena. Given the strong correlation between the field measurements of CDOM and dissolved organic carbon (
R
2
= 0.873)
the CDOM retrieval results were converted to DOC concentrations in this paper. Thus
this paper analyzes the temporal and spatial dynamics of DOC in the Arctic River Lena during the ice-free period over the last two decades.
Results showed that the performance of the retrieval algorithm supports the feasibility of using Landsat data of different sensors to monitor riverine DOC variations. The boosted regression tree model was used to analyze the doc variation of the Artic Lena River
which is influenced by many driving factors
including land cover change
watershed slope
meteorological factors
human activities
and latitudinal zonation. The seasonality
geography
and scale could affect quantitative relationships between DOC concentration and these influencing factors.
In conclusion
our results could improve the ability to monitor DOC fluxes in Arctic rivers and advance our understanding of the Arctic’s carbon cycle.
溶解有机碳DOC有色溶解有机物CDOM北极Lena河Landsat长时序谷歌地球引擎GEE增强回归树
DOCCDOMArctic River LenaLandsatlong-termGoogle Earth EngineBoosted Regression Tree (BRT)
Alcântara E, Bernardo N, Watanabe F, Rodrigues T, Rotta L, Carmo A, Shimabukuro M, Gonçalves S and Imai N. 2016. Estimating the CDOM absorption coefficient in tropical inland waters using OLI/Landsat-8 images. Remote Sensing Letters, 7(7): 661-670 [DOI: 10.1080/2150704X.2016.1177242http://dx.doi.org/10.1080/2150704X.2016.1177242]
Amon R M W, Rinehart A J, Duan S, Louchouarn P, Prokushkin A, Guggenberger G, Bauch D, Stedmon C, Raymond P A, Holmes R M, McClelland J W, Peterson B J, Walker S A and Zhulidov A V. 2012. Dissolved organic matter sources in large Arctic rivers. Geochimica et Cosmochimica Acta, 94: 217-237 [DOI: 10.1016/j.gca.2012.07.015http://dx.doi.org/10.1016/j.gca.2012.07.015]
Brezonik P L, Olmanson L G, Finlay J C and Bauer M E. 2015. Factors affecting the measurement of CDOM by remote sensing of optically complex inland waters. Remote Sensing of Environment, 157: 199-215 [DOI: 10.1016/j.rse.2014.04.033http://dx.doi.org/10.1016/j.rse.2014.04.033]
Cai H Y, Han D R, Yang L S, Chen M L and Yang X H. 2020. Spatiotemporal change in permafrost active layer thickness in the Pan-Arctic region. Journal of Remote Sensing, 24(8): 1045-1057
蔡红艳, 韩冬锐, 杨林生, 陈慕琳, 杨小唤. 2020. 泛北极地区多年冻土活动层厚度演变. 遥感学报, 24(8): 1045-1057 [DOI: 10.11834/jrs.20208469http://dx.doi.org/10.11834/jrs.20208469]
Chander G, Markham B L and Helder D L. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5): 893-903 [DOI: 10.1016/j.rse.2009.01.007http://dx.doi.org/10.1016/j.rse.2009.01.007]
Chen J, Zhu W N, Tian Y Q and Qian Y. 2017a. Estimation of colored dissolved organic matter from Landsat-8 imagery for complex inland water: case study of Lake Huron. IEEE Transactions on Geoscience and Remote Sensing, 55(4): 2201-2212 [DOI: 10.1109/TGRS.2016.2638828http://dx.doi.org/10.1109/TGRS.2016.2638828]
Chen J, Zhu W N, Tian Y Q, Yu Q, Zheng Y H and Huang L T. 2017b. Remote estimation of colored dissolved organic matter and chlorophyll-a in Lake Huron using Sentinel-2 measurements. Journal of Applied Remote Sensing, 11(3): 036007 [DOI: 10.1117/1.Jrs.11.036007http://dx.doi.org/10.1117/1.Jrs.11.036007]
Chevychelov A and Bosikov N P. 2010. Natural conditions//Troeva E I, Isaev A P, Cherosov M M and Karpov N S, eds. The Far North. Dordrecht: Springer: 1-23 [DOI: 10.1007/978-90-481-3774-9_1http://dx.doi.org/10.1007/978-90-481-3774-9_1]
Connolly C T, Khosh M S, Burkart G A, Douglas T A, Holmes R M, Jacobson A D and McClelland J W. 2018. Watershed slope as a predictor of fluvial dissolved organic matter and nitrate concentrations across geographical space and catchment size in the Arctic. Environmental Research Letters, 13: 104015 [DOI: 10.1088/1748-9326/aae35dhttp://dx.doi.org/10.1088/1748-9326/aae35d]
D’Amore D V, Edwards R T and Biles F E. 2016. Biophysical controls on dissolved organic carbon concentrations of Alaskan coastal temperate rainforest streams. Aquatic Sciences, 78(2): 381-393 [DOI: 10.1007/s00027-015-0441-4http://dx.doi.org/10.1007/s00027-015-0441-4]
Elith J, Leathwick J R and Hastie T. 2008. A working guide to boosted regression trees. Journal of Animal Ecology, 77(4): 802-813 [DOI: 10.1111/j.1365-2656.2008.01390.xhttp://dx.doi.org/10.1111/j.1365-2656.2008.01390.x]
Fichot C G and Benner R. 2012. The spectral slope coefficient of chromophoric dissolved organic matter (S275-295) as a tracer of terrigenous dissolved organic carbon in river-influenced ocean margins. Limnology and Oceanography, 57(5): 1453-1466 [DOI: 10.4319/lo.2012.57.5.1453http://dx.doi.org/10.4319/lo.2012.57.5.1453]
Frey K E and McClelland J W. 2009. Impacts of permafrost degradation on arctic river biogeochemistry. Hydrological Processes: An International Journal, 23(1): 169-182 [DOI: 10.1002/hyp.7196http://dx.doi.org/10.1002/hyp.7196]
Frey K E, Sobczak W V, Mann P J and Holmes R M. 2016. Optical properties and bioavailability of dissolved organic matter along a flow-path continuum from soil pore waters to the Kolyma River mainstem, East Siberia. Biogeosciences, 13(8): 2279-2290 [DOI: 10.5194/bg-13-2279-2016http://dx.doi.org/10.5194/bg-13-2279-2016]
Ge Y, Wang M X, Sun X W and Qi J D. 2017. Variation analysis of daily PM2.5 concentrations based on boosted regression tree: a case study in Changzhou. Environmental Science, 38(2): 485-494
葛跃, 王明新, 孙向武, 齐今笛. 2017. 基于增强回归树的城市PM2.5日均值变化分析: 以常州为例. 环境科学, 38(2): 485-494 [DOI: 10.13227/j.hjkx.201607067http://dx.doi.org/10.13227/j.hjkx.201607067]
Gong P, Liu H, Zhang M N, Li C C, Wang J, Huang H B, Clinton N, Ji L Y, Li W Y, Bai Y Q, Chen B, Xu B, Zhu Z L, Yuan C, Suen H P, Guo J, Xu N, Li W J, Zhao Y Y, Yang J, Yu C Q, Wang X, Fu H H, Yu L, Dronova I, Hui F M, Cheng X, Shi X L, Xiao F J, Liu Q F and Song L C. 2019. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Science Bulletin, 64(6): 370-373 [DOI: 10.1016/j.scib.2019.03.002http://dx.doi.org/10.1016/j.scib.2019.03.002]
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D and Moore R. 2017. Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202: 18-27 [DOI: 10.1016/j.rse.2017.06.031http://dx.doi.org/10.1016/j.rse.2017.06.031]
Griffin C G, Frey K E, Rogan J and Holmes R M. 2011. Spatial and interannual variability of dissolved organic matter in the Kolyma River, East Siberia, observed using satellite imagery. Journal of Geophysical Research, 116(G3): G03018 [DOI: 10.1029/2010jg001634http://dx.doi.org/10.1029/2010jg001634]
Griffin C G, McClelland J W, Frey K E, Fiske G and Holmes R M. 2018. Quantifying CDOM and DOC in major Arctic rivers during ice-free conditions using Landsat TM and ETM+ data. Remote Sensing of Environment, 209: 395-409 [DOI: 10.1016/j.rse.2018.02.060http://dx.doi.org/10.1016/j.rse.2018.02.060]
Han X X, Chen X L and Feng L. 2015. Four decades of winter wetland changes in Poyang Lake based on Landsat observations between 1973 and 2013. Remote Sensing of Environment, 156: 426-437 [DOI: 10.1016/j.rse.2014.10.003http://dx.doi.org/10.1016/j.rse.2014.10.003]
Hansen M C, Potapov P V, Moore R, Hancher M, Turubanova S A, Tyukavina A, Tyukavina A, Thau D, Stehman S V, Goetz S J, Loveland T R, Kommareddy A, Egorov A, Chini L, Justice C O and Townshend J R G. 2013. High-resolution global maps of 21st-century forest cover change. Science, 342(6160): 850-853 [DOI: 10.1126/science.1244693http://dx.doi.org/10.1126/science.1244693]
Harms T K, Edmonds J W, Genet H, Creed I F, Aldred D, Balser A and Jones J B. 2016. Catchment influence on nitrate and dissolved organic matter in Alaskan streams across a latitudinal gradient. Journal of Geophysical Research: Biogeosciences, 121(2): 350-369 [DOI: 10.1002/2015jg003201http://dx.doi.org/10.1002/2015jg003201]
Herrault P A, Gandois L, Gascoin S, Tananaev N, Le Dantec T and Teisserenc R. 2016. Using high spatio-temporal optical remote sensing to monitor dissolved organic carbon in the Arctic River Yenisei. Remote Sensing, 8(10): 803 [DOI: 10.3390/rs8100803http://dx.doi.org/10.3390/rs8100803]
Holmes R M, McClelland J W, Peterson B J, Tank S E, Bulygina E, Eglinton T I, Gordeev V V, Gurtovaya T Y, Raymond P A, Repeta D J, Staples R, Striegl R G, Zhulidov A V and Zimov S A. 2012. Seasonal and annual fluxes of nutrients and organic matter from large rivers to the Arctic Ocean and surrounding seas. Estuaries and Coasts, 35(2): 369-382 [DOI: 10.1007/s12237-011-9386-6http://dx.doi.org/10.1007/s12237-011-9386-6]
Holmes R M, McClelland J W, Tank S E, Spencer R G M and Shiklomanov A I. 2018. Arctic Great Rivers Observatory. Water Quality Dataset, Version 20181010. . https://www.arcticgreatrivers.org/data[2019-10-16]
Hugelius G, Strauss J, Zubrzycki S, Harden J W, Schuur E A G, Ping C L, Schirrmeister L, Grosse G, Michaelson G J, Koven C D, O'Donnell J A, Elberling B, Mishra U, Camill P, Yu Z, Palmtag J and Kuhry P. 2014. Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps. Biogeosciences, 11(23): 6573-6593 [DOI: 10.5194/bg-11-6573-2014http://dx.doi.org/10.5194/bg-11-6573-2014]
Jasechko S, Kirchner J W, Welker J M and McDonnell J J. 2016. Substantial proportion of global streamflow less than three months old. Nature Geoscience, 9(2): 126-129 [DOI: 10.1038/ngeo2636http://dx.doi.org/10.1038/ngeo2636]
Joshi I and D’Sa E J. 2015. Seasonal variation of colored dissolved organic matter in Barataria Bay, Louisiana, using combined Landsat and field data. Remote Sensing, 7(9): 12478-12502 [DOI: 10.3390/rs70912478http://dx.doi.org/10.3390/rs70912478]
Kaiser K, Canedo-Oropeza M, McMahon R and Amon R M W. 2017. Origins and transformations of dissolved organic matter in large Arctic rivers. Scientific Reports, 7(1): 13064 [DOI: 10.1038/s41598-017-12729-1http://dx.doi.org/10.1038/s41598-017-12729-1]
Kallio K, Attila J, Härmä P, Koponen S, Pulliainen J, Hyytiäinen U M and Pyhälahti T. 2008. Landsat ETM+ images in the estimation of seasonal lake water quality in boreal river basins. Environmental Management, 42(3): 511-522 [DOI: 10.1007/s00267-008-9146-yhttp://dx.doi.org/10.1007/s00267-008-9146-y]
Khosh M S, McClelland J W, Jacobson A D, Douglas T A, Barker A J and Lehn G O. 2017. Seasonality of dissolved nitrogen from spring melt to fall freezeup in Alaskan Arctic tundra and mountain streams. Journal of Geophysical Research: Biogeosciences, 122(7): 1718-1737 [DOI: 10.1002/2016jg003377http://dx.doi.org/10.1002/2016jg003377]
Kutscher L, Mörth C M, Porcelli D, Hirst C, Maximov T C, Petrov R E and Andersson P S. 2017. Spatial variation in concentration and sources of organic carbon in the Lena River, Siberia. Journal of Geophysical Research: Biogeosciences, 122(8): 1999-2016 [DOI: 10.1002/2017jg003858http://dx.doi.org/10.1002/2017jg003858]
Kutser T, Pierson D C, Tranvik L, Reinart A, Sobek S and Kallio K. 2005. Using satellite remote sensing to estimate the colored dissolved organic matter absorption coefficient in lakes. Ecosystems, 8(6): 709-720 [DOI: 10.1007/s10021-003-0148-6http://dx.doi.org/10.1007/s10021-003-0148-6]
Le Fouest V, Matsuoka A, Manizza M, Shernetsky M, Tremblay B and Babin M. 2018. Towards an assessment of riverine dissolved organic carbon in surface waters of the western Arctic Ocean based on remote sensing and biogeochemical modeling. Biogeosciences, 15(5): 1335-1346 [DOI: 10.5194/bg-15-1335-2018http://dx.doi.org/10.5194/bg-15-1335-2018]
Li C L, Liu M, Hu Y M, Xu Y Y and Sun F Y. 2014. Driving forces analysis of urban expansion based on boosted regression trees and Logistic regression. Acta Ecologica Sinica, 34(3): 727-737
李春林, 刘淼, 胡远满, 徐岩岩, 孙凤云. 2014. 基于增强回归树和Logistic回归的城市扩展驱动力分析. 生态学报, 34(3): 727-737 [DOI: 10.5846/stxb201212121790http://dx.doi.org/10.5846/stxb201212121790]
Lobo F L, Costa M P F and Novo E M L M. 2015. Time-series analysis of Landsat-MSS/TM/OLI images over Amazonian waters impacted by gold mining activities. Remote Sensing Environment, 157: 170-184 [DOI: 10.1016/j.rse.2014.04.030http://dx.doi.org/10.1016/j.rse.2014.04.030]
Lymburner L, Botha E, Hestir E, Anstee J, Sagar S, Dekker A and Malthus T. 2016. Landsat 8: providing continuity and increased precision for measuring multi-decadal time series of total suspended matter. Remote Sensing of Environment, 185: 108-118 [DOI: 10.1016/j.rse.2016.04.011http://dx.doi.org/10.1016/j.rse.2016.04.011]
Masek J G, Vermote E F, Saleous N E, Wolfe R, Hall F G, Huemmrich K F, Gao F, Kutler J and Lim T K. 2006. A Landsat surface reflectance dataset for North America, 1990-2000. IEEE Geoscience and Remote Sensing Letters, 3(1): 68-72 [DOI: 10.1109/LGRS.2005.857030http://dx.doi.org/10.1109/LGRS.2005.857030]
McClelland J W, Holmes R M, Dunton K H and Macdonald R W. 2012. The Arctic Ocean estuary. Estuaries and Coasts, 35(2): 353-368 [DOI: 10.1007/s12237-010-9357-3http://dx.doi.org/10.1007/s12237-010-9357-3]
Olmanson L G, Brezonik P L, Finlay J C and Bauer M E. 2016. Comparison of Landsat 8 and Landsat 7 for regional measurements of CDOM and water clarity in lakes. Remote Sensing of Environment, 185: 119-128 [DOI: 10.1016/j.rse.2016.01.007http://dx.doi.org/10.1016/j.rse.2016.01.007]
Osburn C L and Stedmon C A. 2011. Linking the chemical and optical properties of dissolved organic matter in the Baltic–North Sea transition zone to differentiate three allochthonous inputs. Marine Chemistry, 126(1/4): 281-294 [DOI: 10.1016/j.marchem.2011.06.007http://dx.doi.org/10.1016/j.marchem.2011.06.007]
Pokrovsky O S, Manasypov R M, Loiko S, Shirokova L S, Krickov I A, Pokrovsky B G, Kolesnichenko L G, Kopysov S G, Zemtzov V A, Kulizhsky S P, Vorobyev S N and Kirpotin S N. 2015. Permafrost coverage, watershed area and season control of dissolved carbon and major elements in western Siberian rivers. Biogeosciences, 12(21): 6301-6320 [DOI: 10.5194/bg-12-6301-2015http://dx.doi.org/10.5194/bg-12-6301-2015]
Prasad A M, Louis R I and Andy L. 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9(2): 181-99 [DOI: 10.1007/s10021-005-0054-1http://dx.doi.org/10.1007/s10021-005-0054-1]
Prokushkin A S, Pokrovsky O S, Korets M A, Rubtsov A V, Titov S V, Tokareva I V, Kolosov R A and Amon R M W. 2018. Sources of dissolved organic carbon in rivers of the Yenisei River Basin. Doklady Earth Sciences, 480(2): 763-766 [DOI: 10.1134/s1028334x18060077http://dx.doi.org/10.1134/s1028334x18060077]
Raymond P A, McClelland J W, Holmes R M, Zhulidov A V, Mull K, Peterson B J, Striegl R G, Aiken G R and Gurtovaya T Y. 2007. Flux and age of dissolved organic carbon exported to the Arctic Ocean: a carbon isotopic study of the five largest arctic rivers. Global Biogeochemical Cycles, 21(4):
GB4011 [DOI: 10.1029/2007gb002934http://dx.doi.org/10.1029/2007gb002934]
Romanovsky V, Drozdov D S, Oberman N G, Malkova G V, Kholodov A L, Marchenko S S, Moskalenko N G, Sergeev D O, Ukraintseva N G, Abramov A A, Gilichinsky D A and Vasiliev A A. 2010. Thermal state of permafrost in Russia. Permafrost and Periglacial Processes, 21(2): 136-155 [DOI: 10.1002/ppp.683http://dx.doi.org/10.1002/ppp.683]
Rose A N, Jacob J M, Marie L U and Eddie A B. 2018. LandScan 2017. Oak Ridge, TN: Oak Ridge National Laboratory. . https://landscan.ornl.gov/[2019-07-25]
Sakai T, Hatta S, Okumura M, Hiyama T, Yamaguchi Y and Inoue G. 2015. Use of Landsat TM/ETM+ to monitor the spatial and temporal extent of spring breakup floods in the Lena River, Siberia. International Journal of Remote Sensing, 36(3): 719-733 [DOI: 10.1080/01431161.2014.995271http://dx.doi.org/10.1080/01431161.2014.995271]
Shen W J, Li M S and Huang C Q. 2018. Review of remote sensing algorithms for monitoring forest disturbance from time series and multi-source data fusion. Journal of Remote Sensing, 22(6): 1005-1022
沈文娟, 李明诗, 黄成全. 2018. 长时间序列多源遥感数据的森林干扰监测算法研究进展. 遥感学报, 22(6): 1005-1022 [DOI: 10.11834/jrs.20187089http://dx.doi.org/10.11834/jrs.20187089]
Shiklomanov A I and Lammers R B. 2009. Record Russian river discharge in 2007 and the limits of analysis. Environmental Research Letters, 4(4): 045015 [DOI: 10.1088/1748-9326/4/4/045015http://dx.doi.org/10.1088/1748-9326/4/4/045015]
Singh S, Dash P, Silwal S, Feng G, Adeli A and Moorhead R J. 2017. Influence of land use and land cover on the spatial variability of dissolved organic matter in multiple aquatic environments. Environment Science Pollution Research International, 24(16): 14124-14141 [DOI: 10.1007/s11356-017-8917-5http://dx.doi.org/10.1007/s11356-017-8917-5]
Slonecker E T, Jones D K and Pellerin B A. 2016. The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM). Marine Pollution Bulletin, 107(2): 518-527 [DOI: 10.1016/j.marpolbul.2016.02.076http://dx.doi.org/10.1016/j.marpolbul.2016.02.076]
Stedmon C A, Amon R M W, Rinehart A J and Walker S A. 2011. The supply and characteristics of colored dissolved organic matter (CDOM) in the Arctic Ocean: pan Arctic trends and differences. Marine Chemistry, 124(1/4): 108-118 [DOI: 10.1016/j.marchem.2010.12.007http://dx.doi.org/10.1016/j.marchem.2010.12.007]
Tank S E, Raymond P A, Striegl R G, McClelland J W, Holmes R M, Fiske G J and Peterson B J. 2012. A land-to-ocean perspective on the magnitude, source and implication of DIC flux from major Arctic rivers to the Arctic Ocean. Global Biogeochemical Cycles, 26(4):
GB4018 [DOI: 10.1029/2011gb004192http://dx.doi.org/10.1029/2011gb004192]
Tank S E, Striegl R G, McClelland J W and Kokelj S V. 2016. Multi-decadal increases in dissolved organic carbon and alkalinity flux from the Mackenzie drainage basin to the Arctic Ocean. Environment Research Letters, 11: 054015 [DOI: 10.1088/1748-9326/11/5/054015http://dx.doi.org/10.1088/1748-9326/11/5/054015]
Tarnocai C, Canadell J G, Schuur E A G, Kuhry P, Mazhitova G and Zimov S. 2009. Soil organic carbon pools in the northern circumpolar permafrost region. Global Biogeochemical Cycles, 23: GB2023 [DOI: 10.1029/2008gb003327http://dx.doi.org/10.1029/2008gb003327]
Vermote E, Justice C, Claverie M and Franch B. 2016. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185: 46-56 [DOI: 10.1016/j.rse.2016.04.008http://dx.doi.org/10.1016/j.rse.2016.04.008]
Walker S A, Amon R M W and Stedmon C A. 2013. Variations in high-latitude riverine fluorescent dissolved organic matter: a comparison of large Arctic rivers. Journal of Geophysical Research: Biogeosciences, 118(4): 1689-1702 [DOI: 10.1002/2013jg002320http://dx.doi.org/10.1002/2013jg002320]
Yang D Q, Ye B S and Shiklomanov A. 2004. Discharge characteristics and changes over the Ob River Watershed in Siberia. Journal of Hydrometeorology, 5(4): 595-610 [DOI: 10.1175/1525-7541(2004)005<0595:DCACOT>2.0.CO;2http://dx.doi.org/10.1175/1525-7541(2004)005<0595:DCACOT>2.0.CO;2]
Zhu W N, Yu Q, Tian Y Q, Becker B L, Zheng T and Carrick H J. 2014. An assessment of remote sensing algorithms for colored dissolved organic matter in complex freshwater environments. Remote Sensing of Environment, 140: 766-778 [DOI: 10.1016/j.rse.2013.10.015http://dx.doi.org/10.1016/j.rse.2013.10.015]
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