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Combined Estimation of Chlorophyll Content in Cotton Canopy Based on Hyperspectral Parameters and Back Propagation Neural Network

文献类型： 外文期刊

作者： Ablet, Ershat 1 ; Maimaitiaili, Baidengsha 4 ; Sawut, Mamat 1 ; An Shenqun 5 ;

作者机构： 1.Xinjiang Univ, Coll Resource & Environm Sci, Urumqi 830046, Xinjiang, Peoples R China

2.Minist Educ, Key Lab Oasis Ecol, Urumqi 830046, Xinjiang, Peoples R China

3.Key Lab Xinjiang Gen Inst Higher Learning Smart C, Urumqi 830046, Xinjiang, Peoples R China

4.Xinjiang Acad Agr Sci, Inst Nucl & Biotechnol, Urumqi 830046, Xinjiang, Peoples R China

5.China Agr Univ, Coll Resources & Environm Sci, Beijing 100193, Peoples R China

关键词： spectroscopy; cotton; chlorophyll content; hyperspectral parameters; back propagation neural network

期刊名称：ACTA OPTICA SINICA （ 影响因子：1.6； ）

ISSN： 0253-2239

年卷期： 2019 年 39 卷 9 期

页码：

收录情况： SCI

摘要： Chlorophyll content in canopy plays an important role in reflecting the growing status of vegetation. To achieve high accuracy of chlorophyll content estimation based on hyperspectral data, the spectral reflectance and chlorophyll content in cotton canopy arc measured from field observation. Original spectral data is transformed to calculate the hyperspectral parameters. The correlation between hyperspectral parameters and chlorophyll content is analyzed and a back propagation (HP) neural network model for estimating chlorophyll content in cotton canopy is established. Results show that after continuum-removal transformation, the correlation between canopy reflectance and chlorophyll content improves by 10.7 % in the spectral bands of 560-710 nm, which is better than that of the original spectrum and the first-order differential spectrum. Vegetation indices, such as mSR, mND, NDI, and DD, which arc established using the original spectrum and continuum-removal spectrum, show a high correlation with chlorophyll content under both spectral conditions with a correlation coefficient of approximately 0. 8. In the BP neural network model, the model determination coefficient based on continuum spectral indices is 0. 85, and the root-mean-square error and relative error arc 1. 37 and 1. 97%, respectively. This result is better than that of the model based on red-edge parameters, original spectral vegetation indices, and first-order differential spectral indices. This study provides important theoretical basis and technical support for practical application of chlorophyll content estimation in crops.

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