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不同灌水处理下冬小麦地上干生物量的高光谱监测

杨晨波,冯美臣*,孙慧,王超,杨武德,谢永凯,靖秉翰   

  1. (山西农业大学农学院, 山西太谷 030801)
  • 出版日期:2019-06-10 发布日期:2019-06-10

Hyperspectral monitoring of aboveground dry biomass of winter wheat under different irrigation treatments.

YANG Chen-bo, FENG Mei-chen*, SUN Hui, WANG Chao, YANG Wu-de, XIE Yong-kai, JING Bing-han   

  1. (College of Agronomy, Shanxi Agricultural University, Taigu 030801, Shanxi, China).
  • Online:2019-06-10 Published:2019-06-10

摘要: 地上干生物量是反映作物生长发育和产量的重要指标。本试验通过不同的灌溉处理,研究了冬小麦生育期地上干生物量的变化规律,分别利用多元线性回归(MLR)和连续投影算法MLR(SPA-MLR)构建了冬小麦地上干生物量光谱监测模型。结果表明:拔节期+孕穗期+开花期+灌浆期的灌溉方案有利于生物量积累;基于SPA-MLR构建的预测模型精度均高于MLR预测模型,其中,以开花期模型最优,R2达到了0.96,RMSE为0.092,验证集的R2为0.76,RMSE为0.18;综合冬小麦主要生育时期(拔节期至灌浆期)的预测模型的R2达到了0.64,RMSE为0.30,验证集的R2为0.54,RMSE为0.26,可以实现拔节期至灌浆期冬小麦地上干生物量的预测。本研究可为利用高光谱遥感技术预测冬小麦地上干生物量提供技术支持。

关键词: 旱作农田, 人工降水, 土壤CH4通量, 短期响应

Abstract: Aboveground dry biomass is an important indicator for crop growth and yield. An experiment with different irrigation treatments was carried out to examine the changes of aboveground dry biomass of winter wheat. The predictive models for monitoring aboveground dry biomass were established using the methods of MLR and SPA-MLR. The results showed that irrigation at the jointing stage, booting stage, flowering stage and filling stages was a favorable method for aboveground biomass accumulation of winter wheat. The predictive models established with the method of SPA-MLR were more accurate than those built with the MLR method. Among them, the predictive model at the flowering stage of winter wheat performed best, with R2=0.96, RMSE=0.092 and R2=0.76, RMSE=0.18 for the calibration set and validation set, respectively. The predictive model covering all the growth stages from the jointing stage to filling stage of winter wheat achieved good prediction with R2=0.64, RMSE=0.30 and R2=0.54, RMSE=0.26 for the calibration set and validation set, respectively. Such a result indicated that this model could be potentially used to monitor the aboveground dry biomass in the winter wheat field as good predictive accuracy under extensive growth stages was achieved. Our results provide technical support for predicting the aboveground dry biomass of winter wheat using the hyperspectral technology.

Key words: dryland farming, precipitation simulation, soil CH4 flux, short-term response.