摘要
【目的】通过遥感反演测量收获指数(HI),可节省时间和人力,但需要提高精度。通过权重最优组合算法改善收获指数估算精度,为基于多时相多光谱信息的HI遥感估算提供新方法参考。【方法】利用测定的冬小麦多个关键生育期的冠层光谱数据,对筛选的44种常用植被指数与实测收获指数进行相关性分析,挑选出每个育期中5种最优的典型植被指数;应用偏最小二乘(PLS)的方法建模,分别得到基于单个生育期光谱信息的HI遥感估测模型;借鉴组合预测原理,应用组合预测方法对全部单生育期的各HI光谱模型赋予最优权重,最终构建基于多生育期数据的HI光谱组合预测模型。【结果】(1)利用PLS后,单一生育期的建模结果较单一植被指数有所改进,但仍有待提高;(2)应用组合预测原理的HI组合预测模型,显著改善了HI的估测精度,R2达到0.55,较单生育期的建模预测,提升了13%。【结论】基于多生育期信息的组合预测方法,对各单一生育期HI预测模型赋予最优权重进行优化组合,实质间接利用了各生育期对作物HI形成的贡献,显著提高冬小麦收获指数的估测精度,是一种新颖的作物HI遥感估测方法。
[Purpose]Harvest index can effectively reflect the ability of crop population photohyalates to be transformed into grain accumulation. Also it is a key index to evaluate the yield level of crop varieties. Harvest index can be measured in practice. Remote sensing inversion can save time and manpower,but the accuracy needs to be improved.[ Method]The canopy spectral data of winter wheat in several key growth stages were used to analyze the correlation between the selected 44 common vegetation indexes and the measured harvest indexes. Five optimal typical vegetation indexes are selected in each growth stage. Then,the partial least squares( PLS) modeling was applied to obtain the HI remote sensing estimation models based on the spectral information of single growth period. Finally,the combination prediction theory was used to apply the combination prediction method to assign the optimal weight to each HI spectral model for each single growth periods,and finally a HI spectral combination prediction model based on the data of multiple growth periods was constructed.[ Result](1)After using the PLS,the modeling results of single growth period have improved,compared with that of single vegetation index. However it still needs to be improved(. 2)The HI combination prediction model based on the combination prediction principle significantly improved the estimation accuracy of HI,with R2 up to 0.55,which was 13% higher than the modeling prediction of single growth period.[ Conclusion]Based on the combination prediction method of information of multiple growth stages,the optimal weight was given to the HI prediction model of each single growth stage to optimize the combination. In essence,the contribution of each growth stage to the HI formation of crops was indirectly utilized to significantly improve the estimation accuracy of winter wheat harvest index. It is a novel remote sensing estimation method of crop HI.
作者
陈帼
徐新刚
杜晓初
杨贵军
赵晓庆
魏鹏飞
王玉龙
范玲玲
Chen Guo;Xu Xingang;Du Xiaochu;Yang Guijun;Zhao Xiaoqing;Wei Pengfei;Wang Yulong;Fan Lingling(College of Resources and Environment,Hubei University,Wuhan 430062,China;Beijing AgriculturalInformation Technology Research Center,Beijing 100097,China;National Agricultural Information Engineering Technology Research Center,Beijing 100097,China)
出处
《中国农业信息》
2019年第2期28-38,共11页
China Agricultural Informatics
基金
国家自然科学基金项目(41571416)
国家重点研发计划项目(2017YFD0201501)
关键词
收获指数
偏最小二乘法
冬小麦
组合预测法
遥感光谱
多生育期
harvest index
PLS
winter wheat
combined forecasting model
remote sensing spectrum
growth durations
