| 摘要: |
| 【目的】收获指数(HI)可有效反映作物群体光合同化物转化为籽粒累积物的能力,是评价作物品种产量水平高低的关键性指标。收获指数可以实际测量,通过遥感反演的方法测量,可以节省时间和人力,但需要提高精度。【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；接着,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；最后,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,最终构建了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相较于单一植被指数的确有所改进,但仍有待于提高；(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,R<sub><sup>2</sup></sub>达到0.55,相较于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。 |
| 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 |
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| 基金项目:国家自然科学基金项目（面上项目，重点项目，重大项目） |
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| Estimation of winter harvest index based on PLS and combined prediction method |
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Chen Guo1, Xu Xingang2, Du Xiaochu1, Yang Guijun2, Zhao Xiaoqing2, Wei Pengfei1, Wang Yulong2, Fan Lingling2
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1.College of Resources and Environment,Hubei University;2.Beijing Agricultural Information Technology Research Center
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| Abstract: |
| [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. [Methods] 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. Select 5 optimal typical vegetation indexes in each growth stage. Then, partial least squares (PLS) modeling was applied to obtain HI remote sensing estimation models based on 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 of all single growth periods, and finally a HI spectral combination prediction model based on the data of multiple growth periods was constructed.[Results] (1) After using PLS, the modeling results of single growth period did improve, 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 R<sup>2</sup> 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. |
| Key words: harvest index PLS winter wheat combined forecasting model remote sensing spectrum growth durations |