应用条件植被温度指数预测县域尺度小麦单产

Wheat Yield Forecasting at County Scale Based on Time Series Vegetation Temperature Condition Index

  • 摘要: 选取关中平原2008-2016年的条件植被温度指数(vegetation temperature condition index,VTCI)遥感干旱监测结果,基于最优的干旱影响评估方法确定冬小麦各生育时期干旱对其单产的影响权重,构建县域尺度加权VTCI与小麦单产间的一元线性回归模型,并结合求和自回归移动平均模型(autoregressive integrated moving average,ARIMA)对各县(区)的冬小麦单产进行估测及向前一、二、三旬的预测。结果表明,基于改进的层次分析法与熵值法的最优组合赋权法对冬小麦各生育时期的权重确定较合理,以拔节期(0.489)最大,抽穗-灌浆期(0.427)次之,返青期(0.035)与乳熟期(0.049)较小;加权VTCI与小麦单产之间的相关性显著,单产估测精度较高;向前一、二、三旬的单产预测精度均较高,且以向前一旬的预测精度最高,有76.9%的相对误差小于2.0%,71.6%的均方根误差小于75.0 kg/hm2

     

    Abstract: Selecting the drought monitoring results of remotely sensed vegetation temperature condition index (VTCI) for winter wheat at the ten-day intervals from 2008 to 2016 in the Guanzhong Plain, the weights of drought impact on wheat yields at the 4 main growth stages were determined by applying the best weighting method. Linear regression analysis was employed to study the correlation between the weighted VTCIs and wheat yields of counties, and the yield prediction was carried out at 1-, 2-and 3-ten day intervals between 2008 and 2016 by using the monitored VTCIs and forecasted ones by the autoregressive integrated moving average models. The results show that the weights of drought impact on wheat yields at the turning green stage, the elongation stage, the heading-filling stage and the dough stage are 0.035, 0.489, 0.427 and 0.049 respectively based on the best combination weighting approach of the improved analytic hierarchy method and the entropy method. There is a significant correlation between the weighted VTCIs and the ground-measured yields published in the related statistical yearbooks, indicating the accuracy of the estimated yields is high. The forecasted yield accuracies are quite high and decreased with the increase of the forecasting intervals.

     

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