春玉米温度适宜度动态空间化方法研究

THE METHOD OF TEMPERATURE SUITABILITY INDEX DYNAMIC SPATIALIZATION OF SPRING CORN

为了利用新数据、新方法提高温度适宜度指标在河套地区空间化精度,该研究利用区域站数据开展玉米温度适宜度动态空间化方法研究。首先对区域站逐小时气温数据进行质量控制,计算日平均气温并采用全局多项式插值法进行空间分布拟合;绘制河套地区玉米播种普期空间分布图,根据播种后日尺度的温度适宜度模型,利用GIS栅格计算功能获得最适温度、最高温度、最低温度等指标的空间分布;建立空间分析计算流程,利用GIS条件函数构造日尺度空间温度适宜度分段函数,实现逐日温度适宜度指标的空间化计算;采用常规气象站进行误差分析。结果表明:(1)日平均气温拟合结果与站点观测值温差0.69℃,平均相对误差3.91%,区域站点密集,日平均气温的全局多项式插值效果较好;(2)温度适宜度平均绝对误差0.08,平均相对误差0.41%;(3)利用Model Builder工具建立的播种后第i日温度适宜度指标自动处理流程,实现了复杂模型与数据的综合快速应用。综上,区域站数据在实现玉米温度适宜度动态空间化方面具有较高的业务应用价值。

In order to improve the spatial accuracy of the temperature suitability index in Hetao area,which is used to characterize the suitability of temperature on crop growth indicators,new data and new methods were used to study the temperature suitability of corn space method in this study. Under the quality control of the regional station hourly temperature,the average daily temperature was calculated using global fitting polynomial interpolation method. The distribution of corn sowing period was drawn using IDW interpolation method. Optimum temperature,maximum temperature and minimum temperature indicators were used to obtain the spatial distribution according to the daily temperature suitability model after sowing. Condition function in Arc GIS software was used to construct the spatial distribution of the daily temperature suitability section function and conventional data of weather station was used for error analysis. The results showed that： the temperature difference between the fitting result of the average daily temperature and the site measurements was 0. 69 ℃,with the average relative error 3. 91%. The average daily temperature fitting effect was satisfactory by using the dense area station data and global polynomial interpolation method. The mean absolute error of the temperature suitability was 0. 08 with the average relative error of 0. 41%.The automatic process of suitable temperature index which was established by the model builder tools achieved the rapid application of complex models and data. In summary,the regional station data had a high value in the application of dynamic spatialization of maize temperature suitability.