基于高光谱遥感的冬小麦叶水势估算模型

doi:10.3864/j.issn.0578-1752.2017.05.010

摘要/Abstract

摘要： 【目的】采用高光谱技术，建立快速、无损与准确获取冬小麦叶水势的估算模型，为小麦灌溉的精确管理提供科学依据。【方法】利用不同水分处理的大田试验，于小麦主要生育期同步测定冠层光谱反射率、叶水势、土壤水分等信息，并探讨高光谱植被指数与冬小麦叶水势之间的定量关系。通过相关性分析、回归分析等方法，基于不同水分处理，构建4种植被指数与冬小麦叶水势的估算模型。【结果】不同水分处理和不同生育期的冬小麦，其冠层光谱反射率具有显著的变化特征。在可见光波段，冬小麦冠层反射率随着水分含量的增加而逐渐降低，而在近红外波段，其冠层反射率则随着土壤水分含量的增加而升高。随着小麦生育期的推进，在近红外波段，抽穗期的冠层反射率比拔节期的高，在灌浆期之后，红波段（670 nm）、蓝波段（450 nm）的反射率上升加快；4种植被指数与叶水势显著相关（P＜0.05），相关系数|r|均在0.711以上，四者均可用于冬小麦叶片水势的定量监测。在充分供水条件下（70% FC），植被指数OSAVI和EVI2与叶水势的相关系数|r|（分别为0.75和0.771）均低于植被指数NDVI和RVI与叶水势的相关系数|r|（分别为0.808和0.896），而在重度水分亏缺条件下（50% FC），植被指数OSAVI和EVI2与叶水势的相关系数|r|（分别为0.857和0.853）均高于植被指数NDVI和RVI与叶水势的相关系数|r|（分别为0.711和0.792）；所建模型对45个未知样的预测结果与实测值相似度较高，其回归模型R²、验证模型MRE、RMSE的范围分别为0.616—0.922、-17.50%—-12.52%、0.102—0.133。在70% FC水分处理下，基于EVI2（enhanced vegetation index）所得叶水势估算模型的R²最高，为0.922,而在60% FC和50% FC水分处理下，由于考虑了土壤背景的影响，基于OSAVI所建模型的R²最高，分别为0.922和0.856。【结论】4种植被指数均可用于冬小麦叶水势的定量监测。但是，在构建不同水分处理的叶水势估算模型时，应考虑土壤背景对冠层光谱的影响。研究结果可以为小麦精准灌溉管理提供技术依据，为星载数据的参数反演提供模型支持。

关键词: 高光谱遥感, 冬小麦, 植被指数, 叶水势, 估算

Abstract: 【Objective】A model for fast, non-destructive and accurately monitoring leaf water potential of winter wheat was established with hyperspectra technology, it will provide a scientific basis for the precision irrigation management of winter wheat.【Method】Using the field trials of different water treatments, the canopy spectral reflectance, leaf water potential and soil moisture were synchronously determined in the growth period of winter wheat. Then the correlation between the hyperspectral vegetation indices and leaf water potential was analyzed. Using correlation analysis, regression analysis and other methods, four inversion models were constructed for estimating leaf water potential based on different water treatments.【Result】The canopy spectral reflectance of winter wheat had significant change characteristics in different water treatments and growth periods. In the visible wave band, the canopy reflectance of winter wheat was reduced gradually along with the increase of soil water content. But, in the near-infrared wave band, the canopy reflectance was increased with the increase of soil water content. With the development of wheat growth period, in the near-infrared wave band, the canopy reflectance at heading stage was higher than the reflectance at jointing stage. And after the filling stage, the reflectance of red and blue band was rose faster. The correlation between four vegetation indices and leaf water potential was all reached the significant level (P<0.05), and its absolute values of correlation coefficient were all above 0.711. Four vegetation indices could be used for quantitative monitoring leaf water potential of winter wheat. Under the field capacity of 70%, the absolute values of correlation coefficient |r| between the vegetation indexes of OSAVI and EVI and the leaf water potential were 0.75 and 0.771, respectively, they were lower than the |r| between the vegetation indexes of NDVI and RVI and leaf water potential, which the values of |r| were 0.808 and 0.896, respectively. But, under the field capacity of 50%, the results were just the opposite. The |r| between the vegetation indexes of OSAVI and EVI2 and the leaf water potential were 0.857 and 0.853, respectively, which were higher than the |r| between the vegetation indexes of NDVI and RVI and the leaf water potential, which the values of |r| were 0.711 and 0.792, respectively. The estimation values of 45 samples in prediction set were close to the measured values, the range of R², MRE, and RMSE were 0.616-0.616, -17.50%—-12.52% and 0.102-0.133, respectively. Under the 70% FC water treatment, the estimating model of leaf water potential based on EVI2 had the highest R², the value of R² was 0.922, and under the 60% FC and 50% FC water treatments, because of considering the influence of soil background, the inversion models of leaf water potential based on OSAVI had the highest R², the values of R2 were 0.922 and 0.856, respectively.【Conclusion】All the four vegetation indices could be used for quantitative monitoring leaf water potential of winter wheat. But, when the leaf water potential estimating models were built for different water treatments, the influence of soil background on canopy spectral should be considered. The research results could provide a technical basis for wheat precision irrigation management and also provide supporting models for the parametric inversion of the onboard data.

Key words: hyperspectral remote sensing, winter wheat, vegetation index, leaf water potential, estimation

陈智芳，宋妮，王景雷，孙景生. 基于高光谱遥感的冬小麦叶水势估算模型[J]. 中国农业科学, 2017, 50(5): 871-880.

CHEN ZhiFang, SONG Ni, WANG JingLei, SUN JingSheng. Leaf Water Potential Estimating Models of Winter Wheat Based on Hyperspectral Remote Sensing[J]. Scientia Agricultura Sinica, 2017, 50(5): 871-880.

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