本文選題：條件植被溫度指數(shù) + 空間尺度上推��； 參考：《農業(yè)機械學報》2017年02期

【摘要】：基于關中平原Aqua MODIS條件植被溫度指數(shù)(VTCI)的干旱監(jiān)測結果,分別采用分布式和聚合式的主導類變異權重法(DCVW)、算術平均值變異權重法(AAVW)和中值變異權重法(MPVW)對市域單元內VTCI進行空間尺度上推,以獲取冬小麥主要生育期聚合后的加權VTCI;以加權VTCI與冬小麥產量間的回歸分析精度為參考,選擇最為合適的空間尺度上推方法。結果表明:采用分布式獲得的加權VTCI與冬小麥產量的回歸分析結果整體優(yōu)于聚合式獲得的結果。在分布式的上推過程中,MPVW獲得的加權VTCI與冬小麥產量間的回歸分析精度較低,DCVW和AAVW的精度均較高,其中DCVW獲得的加權VTCI與冬小麥產量間回歸分析的決定系數(shù)R2達0.64,精度最高,說明采用分布式DCVW對市域單元內VTCI進行空間尺度上推得到的加權VTCI最為合理。
[Abstract]:Based on the drought monitoring results of Aqua MODIS condition vegetation temperature index in Guanzhong Plain, Using distributed and aggregated dominant class variation weight method, arithmetic mean variation weight method and median variation weight method to push up the spatial scale of VTCI in the city area, respectively. Taking the weighted VTCI after the aggregation of the main growth stages of winter wheat and the precision of regression analysis between weighted VTCI and winter wheat yield as the reference, the most suitable spatial scale push-up method was selected. The results showed that the result of regression analysis of weighted VTCI and winter wheat yield obtained by distributed method was better than that obtained by aggregate formula. The precision of regression analysis between weighted VTCI obtained by MPVW and winter wheat yield was lower than that of AAVW, and the coefficient of determination between weighted VTCI obtained by DCVW and winter wheat yield was 0.64, the precision was the highest. It is shown that the weighted VTCI with distributed DCVW is the most reasonable in spatial scale for the VTCI in the city area.
【作者單位】： 中國農業(yè)大學信息與電氣工程學院;陜西省氣象局;
【分類號】：S127
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本文編號：1902530