留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Con-ditions in the Heihe River Basin

ZHANG Yuan LIU Shaomin HU Xiao WANG Jianghao LI Xiang XU Ziwei MA Yanfei LIU Rui XU Tongren YANG Xiaofan

ZHANG Yuan, LIU Shaomin, HU Xiao, WANG Jianghao, LI Xiang, XU Ziwei, MA Yanfei, LIU Rui, XU Tongren, YANG Xiaofan. Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Con-ditions in the Heihe River Basin[J]. 中国地理科学, 2020, 30(5): 855-875. doi: 10.1007/s11769-020-1151-y
引用本文: ZHANG Yuan, LIU Shaomin, HU Xiao, WANG Jianghao, LI Xiang, XU Ziwei, MA Yanfei, LIU Rui, XU Tongren, YANG Xiaofan. Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Con-ditions in the Heihe River Basin[J]. 中国地理科学, 2020, 30(5): 855-875. doi: 10.1007/s11769-020-1151-y
ZHANG Yuan, LIU Shaomin, HU Xiao, WANG Jianghao, LI Xiang, XU Ziwei, MA Yanfei, LIU Rui, XU Tongren, YANG Xiaofan. Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Con-ditions in the Heihe River Basin[J]. Chinese Geographical Science, 2020, 30(5): 855-875. doi: 10.1007/s11769-020-1151-y
Citation: ZHANG Yuan, LIU Shaomin, HU Xiao, WANG Jianghao, LI Xiang, XU Ziwei, MA Yanfei, LIU Rui, XU Tongren, YANG Xiaofan. Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Con-ditions in the Heihe River Basin[J]. Chinese Geographical Science, 2020, 30(5): 855-875. doi: 10.1007/s11769-020-1151-y

Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Con-ditions in the Heihe River Basin

doi: 10.1007/s11769-020-1151-y
基金项目: 

Under the auspices of National Natural Science Foundation of China (No. 41531174), National Basic Research Program of China (No. 2015CB953702)

详细信息
    通讯作者:

    LIU Shaomin.E-mail:smliu@bnu.edu.cn

Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Con-ditions in the Heihe River Basin

Funds: 

Under the auspices of National Natural Science Foundation of China (No. 41531174), National Basic Research Program of China (No. 2015CB953702)

  • 摘要: Land surface hydrothermal conditions (LSHCs) reflect land surface moisture and heat conditions, and play an important role in energy and water cycles in soil-plant-atmosphere continuum. Based on comparison of four evaluation methods (namely, the classic statistical method, geostatistical method, information theory method, and fractal method), this study proposed a new scheme for evaluating the spatial heterogeneity of LSHCs. This scheme incorporates diverse remotely sensed surface parameters, e.g., leaf area index-LAI, the normalized difference vegetation index-NDVI, net radiation-Rn, and land surface temperature-LST. The LSHCs can be classified into three categories, namely homogeneous, moderately heterogeneous and highly heterogeneous based on the remotely sensed LAI data with a 30 m spatial resolution and the combination of normalized information entropy (S') and coefficient of variation (CV). Based on the evaluation scheme, the spatial heterogeneity of land surface hydrothermal conditions at six typical flux observation stations in the Heihe River Basin during the vegetation growing season were evaluated. The evaluation results were consistent with the land surface type characteristics exhibited by Google Earth imagery and spatial heterogeneity assessed by high resolution remote sensing evapotranspiration data. Impact factors such as precipitation and irrigation events, spatial resolutions of remote sensing data, heterogeneity in the vertical direction, topography and sparse vegetation could also affect the evaluation results. For instance, short-term changes (precipitation and irrigation events) in the spatial heterogeneity of LSHCs can be diagnosed by energy factors, while long-term changes can be indicated by vegetation factors. The spatial heterogeneity of LSHCs decreases when decreasing the spatial resolution of remote sensing data. The proposed evaluation scheme would be useful for the quantification of spatial heterogeneity of LSHCs over flux observation stations toward the global scale, and also contribute to the improvement of the accuracy of estimation and validation for remotely sensed (or model simulated) evapotranspiration.
  • [1] Alfieri J G, Niyogi D, Zhang H et al., 2009. Quantifying the spatial variability of surface fluxes using data from the 2002 In-ternational H2O Project. Boundary-Layer Meteorology, 133(3):323. doi: 10.1007/s10546-009-9406-2
    [2] Bhattacharya B K, Mallick K, Patel N K et al. 2010. Regional clear sky evapotranspiration over agricultural land using remote sensing data from Indian geostationary meteorological satellite. Journal of Hydrology, 387(1-2):65-80. doi: 10.1016/j.jhydrol.2010.03.030
    [3] Boudreault L-É, Bechmann A, Tarvainen L et al., 2015. A LiDAR method of canopy structure retrieval for wind modeling of heterogeneous forests. Agricultural Forest Meteorology, 201:86-97. doi: 10.1016/j.agrformet.2014.10.014
    [4] Clarke K C. 1986. Computation of the fractal dimension of topo-graphic surfaces using the triangular prism surface area method. Computers & Geosciences, 12(5):713-722. doi: 10.1016/0098-3004(86)90047-6
    [5] Cressie N, Zimmerman D L, 1992. On the stability of the geosta-tistical method. Mathematical Geology, 24(1):45-59
    [6] Chen R S, Song Y X, Kang E S et al., 2014. A cry-osphere-hydrology observation system in a small alpine wa-tershed in the Qilian Mountains of China and its meteorological gradient. Arctic Antarctic Alpine Research, 46(2):505-523. doi: 10.1657/1938-4246-46.2.505
    [7] Ding Y, Zhao K, Zheng X et al., 2014. Temporal dynamics of spatial heterogeneity over cropland quantified by time-series NDVI, near infrared and red reflectance of Landsat 8 OLI im-agery. International Journal of Applied Earth Observation Geoinformation, 30:139-145.doi: 10.1016/j.jag.2014.01.009
    [8] Gao G, Chen D, Ren G et al., 2006. Spatial and temporal variations and controlling factors of potential evapotranspiration in China:1956-2000. Journal of Geographical Sciences, 16(1):3-12. doi: 10.1007/s11442-006-0101-7
    [9] Gao J, Wu S, Dai E F et al., 2015. The progresses and prospects of research on water and heat balance at land surface in the Karst region of Southwest China. Advances in Earth Science, 30(6):647-653. doi: 10.11867/j.issn.1001-8166.2015.06.0647
    [10] Garrigues S, Allard D, Baret F et al., 2006. Quantifying spatial heterogeneity at the landscape scale using variogram models. Remote Sensing of Environment, 103(1):81-96. doi: 10.1016/j.rse.2006.03.013
    [11] Ge Y, Jin Y, Stein A et al., 2019. Principles and methods of scaling geospatial Earth science data. Earth-Science Reviews:102897. doi: 10.1016/j.earscirev.2019.102897
    [12] Giannico V, Chen J, Shao C et al., 2018. Contributions of land-scape heterogeneity within the footprint of eddy-covariance towers to flux measurements. Agricultural Forest Meteorology, 260:144-153. doi: 10.1016/j.agrformet.2018.06.004
    [13] Han D, Wang G, Liu T et al., 2018. Hydroclimatic response of evapotranspiration partitioning to prolonged droughts in semi-arid grassland. Journal of Hydrology, 563:766-777. doi: 10.1016/j.jhydrol.2018.06.048
    [14] Hu T, Liu Q, Du Y et al., 2015. Analysis of land surface tempera-ture spatial heterogeneity using variogram model. Paper pre-sented at the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). doi: 10.1109/IGARSS.2015.7325716
    [15] Jia Z, Liu S, Xu Z et al., 2012. Validation of remotely sensed evapotranspiration over the Hai River Basin, China. Journal of Geophysical Research:Atmospheres, 117(D13). doi: 10.1029/2011JD017037
    [16] Jin R, Li X, Yan B et al., 2014. A nested ecohydrological wireless sensor network for capturing the surface heterogeneity in the midstream areas of the Heihe River Basin, China. IEEE Geo-science Remote Sensing Letters, 11(11):2015-2019. doi: 10.1109/lgrs.2014.2319085
    [17] Kormann R, Meixner F X, 2001. An analytical footprint model for non-neutral stratification. Boundary:Layer Meteorology, 99(2):207-224. doi: 10.1023/a:1018991015119
    [18] Kong J, Hu Y, Yang L et al., 2019. Estimation of evapotranspira-tion for the blown-sand region in the Ordos basin based on the SEBAL model. International Journal of Remote Sensing, 40(5-6):1945-1965. doi: 10.1080/01431161.2018.1508919
    [19] Li Band Avissar R, 1994. The impact of spatial variability of land-surface characteristics on land-surface heat fluxes. Journal of Climate, 7(4):527-537. doi:10.1175/1520-0442(1994) 007<0527:tiosvo>2.0.co;2
    [20] Li Hand Reynolds J, 1995. On definition and quantification of heterogeneity. Oikos:280-284. doi: 10.2307/3545921
    [21] Li M, Zhou J, Peng Z et al., 2019. Component radiative tempera-tures over sparsely vegetated surfaces and their potential for upscaling land surface temperature. Agricultural Forest Mete-orology, 276:107600. doi: 10.1016/j.agrformet.2019.05.031
    [22] Li X, Li X, Li Z et al., 2009. Watershed allied telemetry experi-mental research. Journal of Geophysical Research:Atmos-pheres, 114(D22):2191-2196. doi: 10.1029/2008JD011590
    [23] Li X, Cheng G, Liu S et al., 2013. Heihe watershed allied telemetry experimental research (HiWATER):scientific objectives and experimental design. Bulletin of the American Meteorological Society, 94(8):1145-1160. doi: 10.1175/BAMS-D-12-00154.1
    [24] Li X, Liu S, Li H et al., 2018a. Intercomparison of six upscaling evapotranspiration methods:from site to the satellite pixel. Journal of Geophysical Research:Atmospheres, 123(13):6777-6803. doi: 10.1029/2018JD028422
    [25] Li X, Liu S, Xiao Q et al., 2017. A multiscale dataset for under-standing complex eco-hydrological processes in a heteroge-neous oasis system. Scientific Data, 4:170083. doi: 10.1038/sdata.2017.83
    [26] Li X, Xin X, Peng Z et al., 2018b. Analysis of the spatial variabil-ity of land surface variables for ET estimation:case study in HiWATER Campaign. Remote Sensing, 10(1):91. doi: 10.3390/rs12010010091
    [27] Liu R, Liu S, Yang X et al., 2018a. Wind dynamics over a highly heterogeneous oasis area:an experimental and numerical study. Journal of Geophysical Research:Atmospheres, 123(16):8418-8440. doi: 10.1029/2018JD028397
    [28] Liu S, Xu Z, Wang W et al., 2011. A comparison of ed-dy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem. Hydrology Earth System Sciences, 15(4):1291-1306. doi: 10.5194/hess-15-1291-2011
    [29] Liu S, Xu Z, Song L et al., 2016. Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces. Agricultural and Forest Meteor-ology, 230-231:97-113. doi: 10.1016/j.agrformet.2016.04.008
    [30] Liu S, Li X, Xu Z et al., 2018b. The Heihe integrated observatory network:a basin-scale land surface processes observatory in China. Vadose Zone Journal, 17(1). doi: 10.2136/vzj2018.04.0072
    [31] Ma Y, Liu S, Song L et al., 2018. Estimation of daily evapotran-spiration and irrigation water efficiency at a Landsat-like scale for an arid irrigation area using multi-source remote sensing data. Remote Sensing of Environment, 216:715-734. doi: 10.1016/j.rse.2018.07.019
    [32] Ma Y, Li X, Liu L et al., 2019. Evapotranspiration and its dominant controls along an elevation gradient in the Qinghai Lake watershed, northeast Qinghai-Tibet Plateau. Journal of Hy-drology, 575:257-268. doi: 10.1016/j.jhydrol.2019.05.019
    [33] Matheron G, 1963. Principles of geostatistics. Economic geology, 58(8):1246-1266
    [34] Meijninger W, Hartogensis O, Kohsiek W et al., 2002. Determi-nation of area-averaged sensible heat fluxes with a large aper-ture scintillometer over a heterogeneous surface-Flevoland field experiment. Boundary:Layer Meteorology, 105(1):37-62. doi: 10.1023/A:1019647732027
    [35] Nakayama T. 2013. Effect of evapotranspiration on hydrothermal changes in regional scale. In:Evapotranspiration:An Overview. doi:10.5772/52808. Available at:https://www.intechopen.com/books/evapotranspiration an overview/effect of evapotranspiration on hydrothermal changes in regional scale
    [36] Odongo V, Hamm Nand Milton E, 2014. Spatio-temporal assess-ment of Tuz Gölü, Turkey as a potential radiometric vicarious calibration site. Remote Sensing, 6(3):2494-2513. doi: 10.3390/rs6032494
    [37] Qu Y, Zhu Y, Han W et al., 2013. Crop leaf area index observa-tions with a wireless sensor network and its potential for vali-dating remote sensing products. IEEE Journal of Selected Topics in Applied Earth Observations Remote Sensing, 7(2):431-444. doi: 10.1109/JSTARS.2013.2289931
    [38] Shannon C E, 1948. A mathematical theory of communication. Bell System Technical Journal, 27(3):379-423. doi: 10.1002/j.1538-7305.1948.tb01338.x
    [39] Sun W, Xu G, Gong P et al., 2006. Fractal analysis of remotely sensed images:a review of methods and applications. Interna-tional Journal of Remote Sensing, 27(22):4963-4990. doi: 10.1080/01431160600676695
    [40] Twine T E, Kustas W, Norman J et al., 2000. Correcting ed-dy-covariance flux underestimates over a grassland. Agricul-tural Forest Meteorology, 103(3):279-300. doi: 10.1016/S0168-1923(00)00123-4
    [41] Wang J, Ge Y, Heuvelink G B et al., 2013. Spatial sampling design for estimating regional GPP with spatial heterogeneities. IEEE Geoscience Remote Sensing Letters, 11(2):539-543. doi: 10.1109/LGRS.2013.2274453
    [42] Wang J, Zhang Tand Fu B,2016. A measure of spatial stratified heterogeneity. Ecological Indicators, 67:250-256. doi: 10.1016/j.ecolind.2016.02.052
    [43] Wang Z, Wu Q, Fan B, et al. 2019. Effects of mulching biode-gradable films under drip irrigation on soil hydrothermal con-ditions and cotton (Gossypium hirsutum L.) yield. Agricultural water management, 213:477-485. doi: 10.1016/j.agwat.2018.10.036
    [44] Wu X, Xiao Q, Wen J, et al., 2019. Advances in quantitative re-mote sensing product validation:overview and current status. Earth-Science Reviews, 102875. doi: 10.1016/j.earscirev.2019.102875
    [45] Xiao Q, Wen J, 2013. HiWATER:Wide-angle infrared dual-mode line/area array scanner, WIDAS (3th, August, 2012). Heihe Plan Science Data Center, Heihe, China. Datasets available at:http://westdc.westgis.ac.cn
    [46] Xu B, Li J, Liu Q et al., 2016. Evaluating spatial representativeness of station observations for remotely sensed leaf area index products. IEEE Journal of Selected Topics in Applied Earth Observations Remote Sensing, 9(7):3267-3282. doi: 10.1109/JSTARS.2016.2560878
    [47] Xu T, Guo Z, Liu S et al., 2018. Evaluating different machine learning methods for upscaling evapotranspiration from flux towers to the regional scale. Journal of Geophysical Research:Atmospheres, 123(16):8674-8690. doi:10.1029/2018JD 028447
    [48] Xu T, He X, Bateni S M et al., 2019. Mapping regional turbulent heat fluxes via variational assimilation of land surface tem-perature data from polar orbiting satellites. Remote Sensing of Environment, 221:444-461. doi: 10.1016/j.rse.2018.11.023
    [49] Xu Z, Liu S, Li X et al., 2013. Intercomparison of surface energy flux measurement systems used during the HiWATER-MUSOEXE. Journal of Geophysical Research:Atmospheres, 118(23):13,140-113,157. doi:10.1002/2013JD 020260
    [50] Zhang K, Kimball J S, and Running S W. 2016. A review of remote sensing based actual evapotranspiration estimation. Wiley Interdisciplinary Reviews:Water, 3(6):834-853. doi: 10.1002/wat2.1168
    [51] Zhang X, Zhou J, Göttsche F M et al., 2019. A method based on temporal component decomposition for estimating 1-km all-weather land surface temperature by merging satellite thermal infrared and passive microwave observations. IEEE Transactions on Geoscience Remote Sensing, 57(7):4670-4691. doi: 10.1109/TGRS.2019.2892417
    [52] Zhu X, Chen J, Gao F et al., 2010. An enhanced spatial and tem-poral adaptive reflectance fusion model for complex hetero-geneous regions. Remote Sensing of Environment, 114(11):2610-2623. doi: 10.1016/j.rse.2010.05.032
    [53] Zhong B, Ma P, Nie A H et al., 2014. Land cover mapping using time series HJ-1/CCD data. Science China Earth Sciences, 57(8):1790-1799. doi: 10.1007/s11430-014-4877-5
  • [1] WU Zongfan, ZHANG Lihua, LIU Dandan, ZHANG Kang, ZHU Zhiru, FU Yasheng, MA Yongming.  Simulation of Evapotranspiration Based on BEPS-TerrainLab V2.0 from 1990 to 2018 in the Dajiuhu Basin . Chinese Geographical Science, 2020, 30(6): 1095-1110. doi: 10.1007/s11769-020-1160-x
    [2] GAN Zuoxian, FENG Tao, YANG Min, Harry TIMMERMANS, LUO Jinyu.  Analysis of Metro Station Ridership Considering Spatial Heterogeneity . Chinese Geographical Science, 2019, 29(6): 1065-1077. doi: 10.1007/s11769-019-1065-8
    [3] YANG Jing, CHENG Changxiu, SONG Changqing, SHEN Shi, ZHANG Ting, NING Lix-in.  Spatial-temporal Distribution Characteristics of Global Seismic Clusters and Associated Spatial Factors . Chinese Geographical Science, 2019, 20(4): 614-625. doi: 10.1007/s11769-019-1059-6
    [4] WANG Xuecheng, YANG Fei, GAO Xing, WANG Wei, ZHA Xinjie.  Evaluation of Forest Damaged Area and Severity Caused by Ice-snow Frozen Disasters over Southern China with Remote Sensing . Chinese Geographical Science, 2019, 20(3): 405-416. doi: 10.1007/s11769-019-1041-3
    [5] DU Jia, SONG Kaishan.  Validation of Global Evapotranspiration Product (MOD16) Using Flux Tower Data from Panjin Coastal Wetland, Northeast China . Chinese Geographical Science, 2018, 28(3): 420-429. doi: 10.1007/s11769-018-0960-8
    [6] YU Lingxue, ZHANG Shuwen, LIU Tingxiang, TANG Junmei, BU Kun, YANG Jiuchun.  Spatio-temporal Pattern and Spatial Heterogeneity of Ecotones Based on Land Use Types of Southeastern Da Hinggan Mountains in China . Chinese Geographical Science, 2015, 25(2): 184-197. doi: 10.1007/s11769-014-0671-8
    [7] DONG Minghui, ZOU Bin, PU Qiang, WAN Neng, YANG Lingbin, LUO Yanqing.  Spatial Pattern Evolution and Casual Analysis of County Level Economy in Changsha-Zhuzhou-Xiangtan Urban Agglomeration, China . Chinese Geographical Science, 2014, 0(5): 620-630. doi: 10.1007/s11769-014-0685-2
    [8] QIU Bingwen, ZENG Canying, CHENG Chongcheng, TANG Zhenghong, GAO Jianyang, SUI Yinpo.  Characterizing Landscape Spatial Heterogeneity in Multisensor Images with Variogram Models . Chinese Geographical Science, 2014, 0(3): 317-327. doi: 10.1007/s11769-013-0649-y
    [9] XIA Jun, CHEN Junxu, WENG Jianwu, YU Lei, QI Junyu, LIAO Qiang.  Vulnerability of Water Resources and Its Spatial Heterogeneity in Haihe River Basin, China . Chinese Geographical Science, 2014, 0(5): 525-539. doi: 10.1007/s11769-014-0720-3
    [10] DU Jia, SONG Kaishan, WANG Zongming, ZHANG Bai, LIU Dianwei.  Evapotranspiration Estimation Based on MODIS Products and Surface Energy Balance Algorithms for Land (SEBAL) Model in Sanjiang Plain, Northeast China . Chinese Geographical Science, 2013, 23(1): 73-91.
    [11] QUAN Bin, M J M RÖMKENS, TAO Jianjun, LI Bichen, LI Chaokui, YU Guanghui, CHEN Qichun.  Spatial-temporal Pattern and Population Driving Force of Land Use Change in Liupan Mountains Region, Southern Ningxia, China . Chinese Geographical Science, 2008, 18(4): 323-330. doi: 10.1007/s11769-008-0323-y
    [12] WANG Hao, XU Shiguo, SUN Leshi.  Effects of Climatic Change on Evapotranspiration in Zhalong Wetland, Northeast China . Chinese Geographical Science, 2006, 16(3): 265-269.
    [13] WU Jin-kui, DING Yong-jian, WANG Gen-xu, SHEN Yong-ping, Yusuke YAMAZAKI, Jumpei KUBOTA.  EVAPOTRANSPIRATION OF LOW-LYING PRAIRIE WETLAND IN MIDDLE REACHES OF HEIHE RIVER IN NORTHWEST CHINA . Chinese Geographical Science, 2005, 15(4): 325-329.
    [14] XU Han-qiu.  AN ASSESSMENT OF LAND USE CHANGES IN FUQING COUNTY OF CHINA USING REMOTE SENSING TECHNOLOGY . Chinese Geographical Science, 2002, 12(2): 126-135.
    [15] HU Yuan-man, JIANG Yan, CHANG Yu, BU Ren-cang, LI Yue-hui, XU Chong-gang.  THE DYNAMIC MONITORING OF HORQIN SAND LAND USING REMOTE SENSING . Chinese Geographical Science, 2002, 12(3): 238-243.
    [16] GAO Zhi-qiang, DENG Xiang-zheng.  ANALYSIS ON SPATIAL FEATURES OF LUCC BASED ON REMOTE SENSING AND GIS IN CHINA . Chinese Geographical Science, 2002, 12(2): 107-113.
    [17] LIU Ming-liang, ZHUANG Da-fang, LIU Ji-yuan.  FARMLAND AND URBAN AREA DYNAMICS MONITORING IN CHINA USING REMOTE SENSING AND SPATIAL STATISTICS METHODOLOGY . Chinese Geographical Science, 2001, 11(1): 42-49.
    [18] 黄铁青, 刘兆礼, 潘瑜春, 张养贞.  LAND COVER SURVEY IN NORTHEAST CHINA USING REMOTE SENSING AND GIS . Chinese Geographical Science, 1998, 8(3): 264-270.
    [19] 张养贞, 常丽萍, 张柏, 张树文, 黄铁青, 刘雅琴.  LAND RESOURCES SURVEY BY REMOTE SENSING AND ANALYSIS OF LAND CARRYING CAPACITY FOR POPULATION IN TUMEN RIVER REGION . Chinese Geographical Science, 1996, 6(4): 342-350.
    [20] 陈刚起, 吕宪国.  A STUDY ON MARSH EVAPOTRANSPIRATION IN THE SANJIANG PLAIN . Chinese Geographical Science, 1994, 4(2): 159-167.
  • 加载中
计量
  • 文章访问数:  82
  • HTML全文浏览量:  5
  • PDF下载量:  22
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-01-20
  • 修回日期:  2020-05-04

Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Con-ditions in the Heihe River Basin

doi: 10.1007/s11769-020-1151-y
    基金项目:

    Under the auspices of National Natural Science Foundation of China (No. 41531174), National Basic Research Program of China (No. 2015CB953702)

    通讯作者: LIU Shaomin.E-mail:smliu@bnu.edu.cn

摘要: Land surface hydrothermal conditions (LSHCs) reflect land surface moisture and heat conditions, and play an important role in energy and water cycles in soil-plant-atmosphere continuum. Based on comparison of four evaluation methods (namely, the classic statistical method, geostatistical method, information theory method, and fractal method), this study proposed a new scheme for evaluating the spatial heterogeneity of LSHCs. This scheme incorporates diverse remotely sensed surface parameters, e.g., leaf area index-LAI, the normalized difference vegetation index-NDVI, net radiation-Rn, and land surface temperature-LST. The LSHCs can be classified into three categories, namely homogeneous, moderately heterogeneous and highly heterogeneous based on the remotely sensed LAI data with a 30 m spatial resolution and the combination of normalized information entropy (S') and coefficient of variation (CV). Based on the evaluation scheme, the spatial heterogeneity of land surface hydrothermal conditions at six typical flux observation stations in the Heihe River Basin during the vegetation growing season were evaluated. The evaluation results were consistent with the land surface type characteristics exhibited by Google Earth imagery and spatial heterogeneity assessed by high resolution remote sensing evapotranspiration data. Impact factors such as precipitation and irrigation events, spatial resolutions of remote sensing data, heterogeneity in the vertical direction, topography and sparse vegetation could also affect the evaluation results. For instance, short-term changes (precipitation and irrigation events) in the spatial heterogeneity of LSHCs can be diagnosed by energy factors, while long-term changes can be indicated by vegetation factors. The spatial heterogeneity of LSHCs decreases when decreasing the spatial resolution of remote sensing data. The proposed evaluation scheme would be useful for the quantification of spatial heterogeneity of LSHCs over flux observation stations toward the global scale, and also contribute to the improvement of the accuracy of estimation and validation for remotely sensed (or model simulated) evapotranspiration.

English Abstract

ZHANG Yuan, LIU Shaomin, HU Xiao, WANG Jianghao, LI Xiang, XU Ziwei, MA Yanfei, LIU Rui, XU Tongren, YANG Xiaofan. Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Con-ditions in the Heihe River Basin[J]. 中国地理科学, 2020, 30(5): 855-875. doi: 10.1007/s11769-020-1151-y
引用本文: ZHANG Yuan, LIU Shaomin, HU Xiao, WANG Jianghao, LI Xiang, XU Ziwei, MA Yanfei, LIU Rui, XU Tongren, YANG Xiaofan. Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Con-ditions in the Heihe River Basin[J]. 中国地理科学, 2020, 30(5): 855-875. doi: 10.1007/s11769-020-1151-y
ZHANG Yuan, LIU Shaomin, HU Xiao, WANG Jianghao, LI Xiang, XU Ziwei, MA Yanfei, LIU Rui, XU Tongren, YANG Xiaofan. Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Con-ditions in the Heihe River Basin[J]. Chinese Geographical Science, 2020, 30(5): 855-875. doi: 10.1007/s11769-020-1151-y
Citation: ZHANG Yuan, LIU Shaomin, HU Xiao, WANG Jianghao, LI Xiang, XU Ziwei, MA Yanfei, LIU Rui, XU Tongren, YANG Xiaofan. Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Con-ditions in the Heihe River Basin[J]. Chinese Geographical Science, 2020, 30(5): 855-875. doi: 10.1007/s11769-020-1151-y
参考文献 (53)

目录

    /

    返回文章
    返回