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[1]HOU Liqin,ZHANG Jin.Spatiotemporal Variation of Surface Soil Moisture Based on Temperature Vegetation Dryness Index (TVDI) in Qinshui Coalfield[J].Research of Soil and Water Conservation,2017,24(03):177-182.

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Spatiotemporal Variation of Surface Soil Moisture Based on Temperature Vegetation Dryness Index (TVDI) in Qinshui Coalfield

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 24 Number of periods: 2017 03 Page number: 177-182 Column: Public date: 2017-06-28

Title:: Spatiotemporal Variation of Surface Soil Moisture Based on Temperature Vegetation Dryness Index (TVDI) in Qinshui Coalfield

Author(s):: HOU Liqin, ZHANG Jin; College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China

Keywords:: soil moisture; normalized difference vegetation index (NDVI); temperature vegetation dryness index (TVDI); space distribution; Qinshui coalfield

CLC:: S152.7;TP79

DOI:: -

Abstract:: Temperature Vegetation Dryness Index (TVDI) is a valid indicator to reflect the surface drought. TVDI and the spatiotemporal variation of soil moisture are research topics of global environmental change and the surface soil drought. The variation affects the surface cover change. This method can be used to research the change of ecological environment in coal-field. Taking Qinshui coalfield, Shanxi Province, as an example, combining MODND1M and MODLT1M data of 2000—2010, we built NDVI-Ts feature space and analyzed the dry and wet edges. According to TVDI, the spatiotemporal and variation characteristics of soil moisture were studied. The results showed that the soil moisture of coal surface increased in the past 11 years, and in 2005 before and after the change was bigger. Within the year, monthly average the temperature vegetation dryness index was at around 0.5, and it was obvious fluctuation in July. In space, the distribution of soil moisture was consistent with vegetation distribution in the study area. The two characteristics were basically identical. Coalfield gives priority to arid and extremely arid in the north and east, the southern and eastern mountainous areas were given priority to arid and extreme drought. Simultaneously, in the northwest mountainous areas, soil moisture was mainly normal or moist. The content of soil moisture in mined-out areas was significantly lower than the other regions. The drought phenomenon near the mine is more prominent than distant region.

References:: [1] 黄森旺,李晓松,吴炳方,等.近25年三北防护林工程区土地退化及驱动力分析[J].地理学报,2012,67(5):589-598.
[2] 匡薇,马勇刚,李宏,等.中亚1999-2012年间土地退化强度与趋势分析[J].国土资源遥感,2014,26(4):163-169.
[3] 张建彪,闫美芳,上官铁梁.山西采煤的主要生态问题及恢复和重建对策[J].安徽农业科学,2008,36(24):10668-10670.
[4] 王家强,梁继业,李志军,等.利用植被指数-地表温度特征空间反演干旱区土壤干湿状况[J].土壤通报,2014,45(1):39-46.
[5] 邓辉,周清波.土壤水分遥感监测方法进展[J].中国农业资源与区划,2004,25(3):46-49.
[6] 王利民,刘佳,邓辉,等.我国农业干旱遥感监测的现状与展望[J].中国农业资源与区划,2008,29(6):4-8.
[7] 陈书林,刘元波,温作民.卫星遥感反演土壤水分研究综述[J].地球科学进展,2012,27(11):1192-1203.
[8] Watson K, Rowan L C, Offield T W. Application of thermal modeling in the geologic interpretation of IR images(Thermal modeling for IR images geologic interpretation, discussing physical parameters role in materials natural environmental diurnal temperature behavior)[C]//International Symposium on Remote Sensing of Environment, 7th, University of Michigan, Ann Arbor, Mich.,1971:2017-2041.
[9] Carlson T N. Regional-scale estimates of surface moisture availability and thermal inertia using remote thermal measurements[J]. Remote Sensing Reviews,1986,1(2):197-247.
[10] Jackson R D, Pinter P J. Detection of water stress in wheat by measurement of reflected solar and emitted thermal IR radiation[J]. Spectral Signatures of Objects In Remote Sensing,1981:399-406.
[11] Gillies R R, Kustas W P, Humes K S. A verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface[J]. International Journal of Remote Sensing,1997,18(15):3145-3166.
[12] Sandholt I, Rasmussen K, Andersen J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[J]. Remote Sensing of Environment,2002,79(2):213-224.
[13] 姜琳,冯文兰,刘志红,等.FY-3A/MERSI与MODIS的温度植被干旱指数反演及对比分析[J].水土保持研究,2014,21(3):231-234.
[14] 王海,杨祖祥,王麟,等.TVDI在云南2009/2010年干旱监测中的应用[J].云南大学学报:自然科学版,2014,36(1):59-65.
[15] 鲍艳松,严婧,闵锦忠,等.基于温度植被干旱指数的江苏淮北地区农业旱情监测[J].农业工程学报,2014,30(7):163-172.
[16] Liang L, Zhao S, Qin Z, et al. Drought change trend using MODIS TVDI and its relationship with climate factors in China from 2001 to 2010[J]. Journal of Integrative Agriculture,2014,13(7):1501-1508.
[17] 刘冰冰,曾永年.湖南省严重农业旱情时空变化遥感监测与影响分析[J].自然灾害学报,2015,24(6):72-79.
[18] 杜灵通,候静,胡悦,等.基于遥感温度植被干旱指数的宁夏2000-2010年旱情变化特征[J].农业工程学报,2015,31(14):209-216.
[19] 杨玲,杨艳昭.基于TVDI的西辽河流域土壤湿度时空格局及其影响因素[J].干旱区资源与环境,2016(2):76-81.
[20] 王华,邵瀚.基于TVDI的江苏省淮北地区干旱监测技术研究[J].地理空间信息,2016,14(2):53-55.
[21] 刘英.神东矿区地表植被与土壤湿度遥感监测研究[D].北京:中国矿业大学,2013.

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