中国生态农业学报2018，Vol.26Issue(7)：999-1010,12.DOI:10.13930/j.cnki.cjea.170846
基于偏角光谱检索算法的油菜和水稻LAI反演研究
Estimation of leaf area index of rapeseed and rice based on deflection angle and spectral retrieval algorithm
摘要
Abstract
Leaf area index (LAI) provides insight into productivity, physiological and phenological status of vegetation. The quick and accurate estimation of LAI contributes to growth status diagnosis and yield prediction. A variety of methods have been used for the estimation of LAI, however, the specific spectral bands applied differ widely among the methods and data used. Based on the general shape of the canopy reflectance curve, the spectral angles are found to be of great importance for the LAI estimation. The general objectives of this study were (i) to find informative spectral angles extracted by deflection angle based spectral retrieval (DABSR) and spectral bands retained in the other two common methods, vegetation indices (VI) and principle component analysis (PCA), for estimating LAI in rapeseed and rice; (ii) to compare the accuracy of the three methods as well as determine whether a robust algorithm for LAI estimation of two various crops can be devised. As the two main crops in China, rapeseed and rice, with different leaf structures as well as canopy architecture, were taken as the experimental subjects. Different nitrogen application rates (0, 45, 90, 135, 180, 225, 270, 360 kg·hm-2) and planting treatments (directed sowing and transplanting) were set for rapeseed, while 45 varieties of rice under the same growing environment were employed in the experiment. It was revealed that, for LAI estimation of rapeseed, the model built with DABSR performed the best as the coefficient of determination (R2), root mean square error (RMSEP) and mean normalized bias (MNB) of the predictive model were 0.74, 0.47 and 0.16 respectively; the model built with PCA was of medium accuracy with 0.73, 0.48 and-0.04 for R2, RMSEP and MNB, respectively. The selected VI models were of significantly poorer accuracy with 0.61, 0.57 and 0.17 for R2, RMSEP and MNB respectively, as a result of the effect induced by flowers and pods on canopy reflectance spectrum. From the perspective of rice, the relationship model based on DABSR-STEPWISE was of the best accuracy, as the R2, RMSEP and MNB could reach up to 0.70, 0.80 and 0.05. The models built with VIs performed the worst among three methods (R2≤ 0.61, RMSEP ≤ 0.92 and MNB ≤ 0.04), while the PCA model performed in between with 0.63, 0.88 and 0.04 for R2, RMSEP and MNB individually. The red edge and the NIR bands were selected in most models and considered the most informative. Among the three methods, DABSR-STEPWISE, proposed on the basis of spectral angle, was the most suitable for estimating LAI of two kinds of crops under different growing environments. The analysis allowed development of universal algorithms for LAI estimation in various crops. Being of high accuracy and high computational efficiency, these findings have significant implications on the development of uniform and robust algorithms, which is crucial for LAI estimation of specie-specific crops.关键词
油菜/水稻/叶面积指数/高光谱/偏角光谱检索Key words
Rapeseed/Rice/Leaf area index/Hyperspectral remote sensing/Deflection angle based spectral retrieval分类
农业科技引用本文复制引用
刘怡晨,马驿,仝春艳,段博,蒋琦..基于偏角光谱检索算法的油菜和水稻LAI反演研究[J].中国生态农业学报,2018,26(7):999-1010,12.基金项目
国家高技术研究发展计划(863计划)项目(2013AA102401)资助 This study was supported by the National High-tech R&D Program of China (863 Program) (2013AA102401). （863计划）
