李 艳，王鹏新，刘峻明,张树誉，李 俐.基于条件植被温度指数的冬小麦主要生育时期干旱监测效果评价——I.因子权重排序法和熵值法组合赋权[J].干旱地区农业研究,2013,31(6):159~163
基于条件植被温度指数的冬小麦主要生育时期干旱监测效果评价——I.因子权重排序法和熵值法组合赋权
Application of temperature condition index to evaluate the drought monitoring effect in main growing period of winter wheat ——Ⅰ. factor weight sorting method and entropy method
  
DOI：10.7606/j.issn.1000-7601.2013.06.028
中文关键词:  条件植被温度指数  因子权重排序法  熵值法  组合赋权法  小麦单产  关中平原
英文关键词:vegetation temperature condition index  factor weight sorting method  entropy method  combination weighting method  wheat yield  Guanzhong Plain
基金项目:国家自然科学基金资助项目（41071235）；“十二五”国家科技支撑计划资助项目（2012BAD20B0103）和高等学校博士学科点专项科研基金项目（20100008110031）
作者单位
李 艳1,2，王鹏新1*，刘峻明1,张树誉3，李 俐1 （1.中国农业大学信息与电气工程学院， 北京 100083； 2.河北科技师范学院城市建设学院， 河北 秦皇岛 066004；3.陕西省气象局， 陕西 西安 710014） 
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中文摘要:
      选取关中平原冬小麦越冬后2002—2009年每年3—5月共9旬的条件植被温度指数（VTCI）遥感干旱监测结果，将冬小麦越冬后分为四个主要生育时期，分别运用因子权重排序法、熵值法及组合赋权法确定各生育时期干旱对产量的影 响权重，计算关中平原各市冬小麦每年的加权VTCI，并建立加权VTCI与冬小麦单产间的一元线性回归模型。结果表明，关中平原大部分地区的加权VTCI与小麦单产密切相关。其中，熵值法确定的加权VTCI与小麦单产的线性相关性不显著，渭南、咸阳及铜川的决定系数R2值均低于0.5；因子权重排序法和组合赋权法确定的加权VTCI与小麦单产的线性相关性显著，咸阳、宝鸡及西安的R2值接近或高于0.6，渭南接近 0.5，铜川较差。且组合赋权法的结果中宝鸡和西安的R2值接近0.7，优于因子权重排序法和熵值法的结果。
英文摘要:
      Selected the drought monitoring results of vegetation temperature condition index (VTCI) of winter wheat at the ten-days interval from March to May of year 2002—2009 in Guanzhong Plain, the main growth and development period of winter wheat was divided into four stages, and the impact ri ghts of each stage's drought to the wheat yield was determined by using the factor weight sorting method, entropy method and combination weighting methods, calculated the rights VTCI and established a linear regression model between the rights VTCI and yield of winter wheat. The results shown that: There were significant linear correlations between the yield and the weights VTCI in most region of Guanzhong Plain. Among them, the linear correlations was not significant by the entropy method, the R2 values of Weinan, Xianyang and Tongchuan total was less than 0.5. The linear correlations between the yield and the weights VTCI was significant by using the factor weight sorting method and combination weighting method, the R2 values of Xianyang, Baoji and Xi'an were close or higher than 0.6, Weinan was close to 0.5, and Tongchuan was rather poor. While by using the combination weighting method, the R2 values of Baoji and Xi'an were all close to 0.7, this result was better than the combination weighting method and entropy method.
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