文献类型： 外文期刊

作者： Tian, Zezhong ¹ ; Zhang, Yao ¹ ; Zhang, Haiyang ¹ ; Li, Zhenhai ² ; Li, Minzan ¹ ; Wu, Jiangmei ¹ ; Liu, Kaidi ¹ ;

作者机构： 1.China Agr Univ, Key Lab Smart Agr Syst Integrat Res, Minist Educ, Beijing 100083, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Minist Agr & Rural Afairs, Key Lab Quantitat Remote Sensing, Beijing 100097, Peoples R China

关键词： Total nitrogen; Spectral feature fusion; GWO; NMF; Winter wheat; Soil; Integrated monitoring

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 201 卷

页码：

收录情况： SCI

摘要： Nitrogen, as a key element for crop reproductive and nutritional growth, plays an important role in overall agricultural production. Hyperspectral remote sensing (RS) offers possibility for monitoring farm nitrogen content by its non-destructive and high throughput advantages. However, the integrated high-precision acquisition of nitrogen content in crops and soils is still a technical challenge that needs to be solved urgently. This study proposed a novel approach combining feature selection based on improved grey wolf optimizer (IGWO) and feature fusion with projected gradient nonnegative matrix factorization and matrix cross recombination (PNMF-MCR) to realize the integrated monitoring of total nitrogen (TN) content in winter wheat and soil based on crop canopy hyperspectral data. Therein, the IGWO originated from GWO, additionally considered a comprehensive feature selection criterion which are the relevance with TN, the representative ability of the entire spectra, and the redundancy of the selected wavebands. The selected TN sensitive wavebands are 487, 611, 706, 817, 920 nm for winter wheat and 478, 587, 699, 812, 892 nm for soil, which could effectively represent the TN information of full spectrum from the aspects of nitrogen metabolism enzymes, chlorophyll photosynthesis, and the structure of cellular arrangements within the leaf. To further integrate the TN sensitive spectral information of crop and soil, PNMF-MCR was firstly proposed to generate a feature matrix VF by fusing the winter wheat and soil TN sensitive wavelengths. The TN inversion results based on the fused feature matrix VF achieved a high accuracy for both winter wheat and soil simultaneously. On the ground-based RS platform, the R2 and RMSE of the validation dataset for winter wheat are 0.7920 and 0.1431 g/kg. The validation results for soil are 0.7928 and 0.0037 g/kg. On the Airborne-based RS platform, the R-2 and RMSE of the validation dataset for winter wheat are 0.6934 and 0.1490 g/kg. The validation results for soil are 0.6988 and 0.0040 g/kg. To verify the proposed method, predictive performances for TN of winter wheat and soil were validated in different time, at different locations for different winter wheat varieties on different platforms (ground-based platform and airborne-based platform). All above validations demonstrated the good universality and predictive ability of proposed hybrid method, which provide a feasible solution for integrated monitoring of crop and soil.