Volume 30 Issue 5
Dec.  2020
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LI Xijia, ZHANG Hongyan, QU Ying. Land Surface Albedo Variations in Sanjiang Plain from 1982 to 2015: Assessing with GLASS Data[J]. Chinese Geographical Science, 2020, 30(5): 876-888. doi: 10.1007/s11769-020-1152-x
Citation: LI Xijia, ZHANG Hongyan, QU Ying. Land Surface Albedo Variations in Sanjiang Plain from 1982 to 2015: Assessing with GLASS Data[J]. Chinese Geographical Science, 2020, 30(5): 876-888. doi: 10.1007/s11769-020-1152-x

Land Surface Albedo Variations in Sanjiang Plain from 1982 to 2015: Assessing with GLASS Data

doi: 10.1007/s11769-020-1152-x
Funds:

Under the auspices of the National Key R&D Program of China (No. 2016YFA0602301), National Natural Science Foundation of China (No. 41971287, 41601349), Science and Technology Development Program of Jilin Province (No. 20180520220JH, 20180623058TC), Fundamental Research Funds for the Central Universities (No. 2412019FZ003)

  • Received Date: 2020-03-02
  • Rev Recd Date: 2020-05-18
  • As a key parameter for indicating the fraction of surface-reflected solar incident radiation, land surface albedo plays an important role in the Earth's surface energy budget (SEB). Since the Sanjiang Plain has been severely affected by human activities (e.g., reclamation and shrinking of wetlands), it is important to assess the spatiotemporal variations of surface albedo in this region using a long-term remote sensing dataset. In order to investigate the surface albedo climatology, trends, and mechanisms of change, we evaluated the surface albedo variations in the Sanjiang Plain, China from 1982 to 2015 using the Global LAnd Surface Satellite (GLASS) broadband surface albedo product. The results showed that:1) an increasing annual trend (+0.000 58/yr) of surface albedo was discovered in the Sanjiang Plain based on the GLASS albedo dataset, with a much stronger increasing trend (+0.001 26/yr) occurring during the winter. Most of the increasing trends occurred over the cultivated land, unused land, and land use conversion types located in the northeastern Sanjiang Plain. 2) The increasing trend of land surface albedo in Sanjiang Plain can be largely explained by the changes of both snow cover extent and land use. The surface albedo in winter is highly correlated with the snow cover extent in the Sanjiang Plain, and the increasing trend of surface albedo can be further enhanced by the land use changes.
  • [1] Bala G, Caldeira K, Wickett M et al., 2007. Combined climate and carbon-cycle effects of large-scale deforestation. Proceedings of the National Academy of Sciences of the United States of America, 104(16):6550-6555. doi:10.1073/pnas. 0608998104
    [2] Betts R A, 2000. Offset of the potential carbon sink from boreal forestation by decreases in surface albedo. Nature, 408(6809):187-190. doi: 10.1038/35041545
    [3] Cescatti A, Marcolla B, Vannan S K S et al., 2012. Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network. Remote Sensing of En-vironment, 121:323-334. doi: 10.1016/j.rse.2012.02.019
    [4] Chen W W, Wang Y Y, Zhao Z C et al., 2013. The effect of plant-ing density on carbon dioxide, methane and nitrous oxide emissions from a cold paddy field in the Sanjiang Plain, Northeast China. Agriculture, Ecosystems & Environment, 178:64-70. doi: 10.1016/j.agee.2013.05.008
    [5] Donohoe A, Battisti D S, 2011. Atmospheric and surface contri-butions to planetary albedo. Journal of Climate, 24(16):4402-4418. doi: 10.1175/2011JCLI3946.1
    [6] Essery R, 2013. Large-scale simulations of snow albedo masking by forests. Geophysical Research Letters, 40(20):5521-5525. doi: 10.1002/grl.51008
    [7] Gao F, He T, Wang Z S et al., 2014. Multiscale climatological albedo look-up maps derived from moderate resolution imaging spectroradiometer BRDF/albedo products. Journal of Applied Remote Sensing, 8(1):083532. doi: 10.1117/1.JRS.8.083532
    [8] Gelaro R, McCarty W, Suárez M J et al., 2017. The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Journal of Climate, 30(14):5419-5454. doi: 10.1175/JCLI-D-16-0758.1
    [9] Ghimire B, Williams C A, Masek J et al., 2014. Global albedo change and radiative cooling from anthropogenic land cover change, 1700 to 2005 based on MODIS, land use harmoniza-tion, radiative kernels, and reanalysis. Geophysical Research Letters, 41(24):9087-9096. doi: 10.1002/2014GL061671
    [10] He T, Liang S L, Song D X, 2014. Analysis of global land surface albedo climatology and spatial-temporal variation during 1981-2010 from multiple satellite products. Journal of Geo-physical Research:Atmospheres, 119(17):10281-10298. doi: 10.1002/2014JD021667
    [11] Hu Y H, Jia G S, Pohl C et al., 2016. Assessing surface albedo change and its induced radiation budget under rapid urbaniza-tion with Landsat and GLASS data. Theoretical and Applied Climatology, 123(3):711-722. doi: 10.1007/s00704-015-1385-2
    [12] Hu Y H, Hou M T, Zhao C L et al., 2019. Human-induced changes of surface albedo in Northern China from 1992-2012. In-ternational Journal of Applied Earth Observation and Geoin-formation, 79:184-191. doi: 10.1016/j.jag.2019.03.018
    [13] Huang X D, Deng J, Ma X F et al., 2016. Spatiotemporal dynam-ics of snow cover based on multi-source remote sensing data in China. The Cryosphere, 10(5):2453-2463. doi: 10.5194/tc-10-2453-2016
    [14] Jaagus J, 2006. Climatic changes in Estonia during the second half of the 20th century in relationship with changes in large-scale atmospheric circulation. Theoretical and Applied Climatology, 83(1-4):77-88. doi: 10.1007/s00704-005-0161-0
    [15] Jiao T, Williams C A, Ghimire B et al., 2017. Global climate forcing from albedo change caused by large-scale deforestation and reforestation:quantification and attribution of geographic variation. Climatic Change, 142(3-4):463-476. doi: 10.1007/s10584-017-1962-8
    [16] Li X J, Qu Y, 2018. Evaluation of vegetation responses to climatic factors and global vegetation trends using GLASS LAI from 1982 to 2010. Canadian Journal of Remote Sensing, 44(4):357-372. doi: 10.1080/07038992.2018.1526064
    [17] Li X J, Yan H B, Fan X L et al., 2018. Validation of global land surface satellite phase-2 surface broadband albedo product. In:IEEE International Geoscience and Remote Sensing Sympo-sium. Valencia, Spain:IEEE. doi: 10.1109/IGARSS.2018.8519449
    [18] Liang S L, Wang K C, Zhang X T et al., 2010. Review on estima-tion of land surface radiation and energy budgets from ground measurement, remote sensing and model simulations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(3):225-240. doi: 10.1109/JSTARS.2010.2048556
    [19] Liang S L, Zhao X, Liu S H et al., 2013. A Long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies. International Journal of Digital Earth, 6(S1):5-33. doi: 10.1080/17538947.2013.805262
    [20] Ling F, Zhang T J, 2003. Impact of the timing and duration of seasonal snow cover on the active layer and permafrost in the Alaskan Arctic. Permafrost and Periglacial Processes, 14(2):141-150. doi: 10.1002/ppp.445
    [21] Liu J Y, Liu M L, Deng X Z et al., 2002. The land use and land cover change database and its relative studies in China. Journal of Geographical Sciences, 12(3):275-282. doi: 10.1007/BF02837545
    [22] Liu Q, Wen J G, Qu Y et al., 2012. Broadband albedo. In:Liang S L et al. (eds). Advanced Remote Sensing:Terrestrial Infor-mation Extraction and Applications. San Diego:Academic Press. doi: 10.1016/B978-0-12-815826-5.00006-4
    [23] Liu N F, Liu Q, Wang L Z et al., 2013a. A statistics-based temporal filter algorithm to map spatiotemporally continuous shortwave albedo from MODIS data. Hydrology and Earth System Sciences, 17(6):2121-2129. doi: 10.5194/hess-17-2121-2013
    [24] Liu Q, Wang L Z, Qu Y et al., 2013b. Preliminary evaluation of the long-term GLASS albedo product. International Journal of Digital Earth, 6(S1):69-95. doi: 10.1080/17538947.2013.804601
    [25] Loranty M M, Berner L T, Goetz S J et al., 2014. Vegetation con-trols on northern high latitude snow-albedo feedback:observa-tions and CMIP 5 model simulations. Global Change Biology, 20(2):594-606. doi: 10.1111/gcb.12391
    [26] Lucht W, Schaaf C B, Strahler A H, 2000. An algorithm for the retrieval of albedo from space using semiempirical BRDF models. IEEE Transactions on Geoscience and Remote Sens-ing, 38(2):977-998. doi: 10.1109/36.841980
    [27] Mann H B, 1945. Nonparametric tests against trend. Economet-rica, 13(3):245-259. doi: 10.2307/1907187
    [28] Qu Y, Liang S L, Liu Q et al., 2015. Mapping surface broadband albedo from satellite observations:a review of literatures on algorithms and products. Remote Sensing, 7(1):990-1020. doi: 10.3390/rs70100990
    [29] Qu Y, Liang S L, Liu Q et al., 2016. Estimating Arctic sea-ice shortwave albedo from MODIS data. Remote Sensing of Envi-ronment, 186:32-46. doi: 10.1016/j.rse.2016.08.015
    [30] Qu Y, Liu Q, Liang S L et al., 2014. Direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 52(2):907-919. doi: 10.1109/TGRS.2013.2245670
    [31] Riihelä A, Manninen T, Laine V et al., 2013. CLARA-SAL:a global 28 yr timeseries of Earth's black-sky surface albedo. Atmospheric Chemistry and Physics, 13(7):3743-3762. doi: 10.5194/acp-13-3743-2013
    [32] Savitzky A, Golay M, 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8):1627-1639. doi: 10.1021/ac60214a047
    [33] Schaaf C, Gao F, Strahler A et al., 2002. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens-ing of Environment, 83(1-2):135-148. doi: 10.1016/S0034-4257(02)00091-3
    [34] Sen P K, 1968. Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 63(324):1379-1389. doi: 10.1080/01621459.1968.10480934
    [35] Song C C, Xu X F, Sun X X et al., 2012. Large methane emission upon spring thaw from natural wetlands in the northern per-mafrost region. Environmental Reseach Letters, 7(3):034009. doi: 10.1088/1748-9326/7/3/034009
    [36] Song Kaishan, Liu Dianwei, Wang Zongming et al., 2008. Land use change in sanjiang plain and its driving forces analysis since 1954. Acta Geographica Sinica, 63(1):93-104. (in Chinese)
    [37] Tan X J, Wu Z N, Mu X M et al., 2019. Spatiotemporal changes in snow cover over China during 1960-2013. Atmospheric Research, 218:183-194. doi: 10.1016/j.atmosres.2018.11.018
    [38] Tanaka H L, Tamura M, 2016. Relationship between the Arctic oscillation and surface air temperature in multi-decadal time-scale. Polar Science, 10(3):199-209. doi:10.1016/j.polar. 2016.03.002
    [39] Theil H, 1950. A rank-invariant method of linear and polynomial regression analysis. In:Raj B and Koerts J (eds). Henri Theil's Contributions to Economics and Econometrics:Econometric Theory and Methodology. Dordrecht:Springer, 345-381. doi: 10.1007/978-94-011-2546-8_20
    [40] Trenberth K E, Fasullo J T, Kiehl J, 2009. Earth's global energy budget. Bulletin of the American Meteorological Society, 90(3):311-324. doi: 10.1175/2008BAMS2634.1
    [41] Wang Z M, Zhang B, Zhang S Q et al., 2006. Changes of land use and of ecosystem service values in Sanjiang Plain, Northeast China. Environmental Monitoring and Assessment, 112(1-3):69-91. doi: 10.1007/s10661-006-0312-5
    [42] Zhai Jun, Liu Ronggao, Liu Jiyuan et al., 2014. Radiative forcing over China due to albedo change caused by land cover change during 1990-2010. Journal of Geographical Sciences, 24(5):789-801. doi: 10.1007/s11442-014-1120-4
    [43] Zhai J, Liu R G, Liu J Y, et al., 2015. Human-induced landcover changes drive a diminution of land surface albedo in the Loess Plateau (China). Remote Sensing, 7(3):2926-2941. doi: 10.3390/rs70302926
    [44] Zhang Ruonan, Zhang Renhe, Zuo Zhiyan, 2015. Winter snow cover variability over China and its relation to arctic oscillation. Chinese Journal of Atmospheric Sciences, 39(3):634-642. (in Chinese)
    [45] Zhang S Q, Na X D, Kong B et al., 2009. Identifying wetland change in China's Sanjiang Plain using remote sensing. Wet-lands, 29(1):302-313. doi: 10.1672/08-04.1
    [46] Zhang Xuezhen, Wang Wei-chyung, Fang Xiuqi et al., 2012. Agriculture development-induced surface albedo changes and climatic implications across northeastern China. Chinese Ge-ographical Science, 22(3):264-277. doi: 10.1007/s11769-012-0535-z
    [47] Zuo J Q, Li W J, Ren H L, 2013. Representation of the Arctic oscillation in the CMIP5 models. Advances in Climate Change Research, 4(4):242-249. doi: 10.3724/SP.J.1248.2013.242.
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Land Surface Albedo Variations in Sanjiang Plain from 1982 to 2015: Assessing with GLASS Data

doi: 10.1007/s11769-020-1152-x
Funds:

Under the auspices of the National Key R&D Program of China (No. 2016YFA0602301), National Natural Science Foundation of China (No. 41971287, 41601349), Science and Technology Development Program of Jilin Province (No. 20180520220JH, 20180623058TC), Fundamental Research Funds for the Central Universities (No. 2412019FZ003)

Abstract: As a key parameter for indicating the fraction of surface-reflected solar incident radiation, land surface albedo plays an important role in the Earth's surface energy budget (SEB). Since the Sanjiang Plain has been severely affected by human activities (e.g., reclamation and shrinking of wetlands), it is important to assess the spatiotemporal variations of surface albedo in this region using a long-term remote sensing dataset. In order to investigate the surface albedo climatology, trends, and mechanisms of change, we evaluated the surface albedo variations in the Sanjiang Plain, China from 1982 to 2015 using the Global LAnd Surface Satellite (GLASS) broadband surface albedo product. The results showed that:1) an increasing annual trend (+0.000 58/yr) of surface albedo was discovered in the Sanjiang Plain based on the GLASS albedo dataset, with a much stronger increasing trend (+0.001 26/yr) occurring during the winter. Most of the increasing trends occurred over the cultivated land, unused land, and land use conversion types located in the northeastern Sanjiang Plain. 2) The increasing trend of land surface albedo in Sanjiang Plain can be largely explained by the changes of both snow cover extent and land use. The surface albedo in winter is highly correlated with the snow cover extent in the Sanjiang Plain, and the increasing trend of surface albedo can be further enhanced by the land use changes.

LI Xijia, ZHANG Hongyan, QU Ying. Land Surface Albedo Variations in Sanjiang Plain from 1982 to 2015: Assessing with GLASS Data[J]. Chinese Geographical Science, 2020, 30(5): 876-888. doi: 10.1007/s11769-020-1152-x
Citation: LI Xijia, ZHANG Hongyan, QU Ying. Land Surface Albedo Variations in Sanjiang Plain from 1982 to 2015: Assessing with GLASS Data[J]. Chinese Geographical Science, 2020, 30(5): 876-888. doi: 10.1007/s11769-020-1152-x
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