سابِق|تال

Analysis of DOC concentration variation and driving forces in the Arctic River Lena based on long-term Landsat time series

  • role： First author第一作者
  • Affiliation：

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

  • Email：wuming1016@sdust.edu.cn
  • Introduction：吴铭，1996年生，男，硕士研究生，研究方向为水色遥感。E-mail: wuming1016@sdust.edu.cn
WU Ming,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

  • Email：huangjue@sdust.edu.cn
  • Introduction：黄珏，1987年生，女，副教授，研究方向为水环境遥感。E-mail: huangjue@sdust.edu.cn
HUANG Jue*,  
  • Affiliation：

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

GONG Lijiao,  
  • Affiliation：

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

JIANG Tao

ملخص

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 (R2 = 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.

مفهوم

DOC;CDOM;Arctic River Lena;Landsat;long-term;Google Earth Engine;Boosted Regression Tree (BRT)

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    Elith J, Leathwick J R and Hastie T. 2008. A working guide to boosted regression trees. Journal of Animal Ecology, 77(4): 802-813
  12. 12.
    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
  13. 13.
    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
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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
  18. 18.
    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
  19. 19.
    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
  20. 20.
    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
  21. 21.
    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
  22. 22.
    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
  23. 23.
    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
  24. 24.
    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
  25. 25.
    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]
  26. 26.
    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
  27. 27.
    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
  28. 28.
    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
  29. 29.
    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
  30. 30.
    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
  31. 31.
    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
  32. 32.
    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
  33. 33.
    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
  34. 34.
    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
  35. 35.
    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
  36. 36.
    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
  37. 37.
    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
  38. 38.
    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
  39. 39.
    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
  40. 40.
    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
  41. 41.
    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
  42. 42.
    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
  43. 43.
    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
  44. 44.
    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
  45. 45.
    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):
  46. 46.
    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
  47. 47.
    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]
  48. 48.
    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
  49. 49.
    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
  50. 50.
    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
  51. 51.
    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
  52. 52.
    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
  53. 53.
    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
  54. 54.
    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):
  55. 55.
    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
  56. 56.
    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
  57. 57.
    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
  58. 58.
    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
  59. 59.
    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
  60. 60.
    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

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