[1] LI G H. High quality and high efficiency fertilization of rice. Journal of Integrative Agriculture, 20, 1435-1437(2021).

[2] Eric A et al. Managing nitrogen for sustainable development. Nature, 52, 51-59(2015).

[3] VIGNEAU N, ECARNOT M, RABATEL G et al. Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in Wheat. Field Crops Research, 122, 25-31(2011).

[4] LI B B, ROLEY S S, DUNCAN D S et al. Long-term excess nitrogen fertilizer increases sensitivity of soil microbial community to seasonal change revealed by ecological network and metagenome analyses. Soil Biology and Biochemistry, 160, 108349(2021).

[5] Singh P, Pandey P C, Petropoulos G P et al. Hyperspectral remote sensing in precision agriculture: Present status, challenges, and future trends[M]. Hyperspectral Remote Sensing, 121-146(2020).

[6] PASCUCCI S, PIGNATTI S, CASA R et al. Remote sensing special issue "Hyperspectral Remote Sensing of Agriculture and Vegetation". Remote Sensing, 12, 3665(2020).

[7] XU X, NIE C, JIN X et al. A comprehensive yield evaluation indicator based on an improved fuzzy comprehensive evaluation method and hyperspectral data. Field Crops Research, 270, 108204(2021).

[8] ZHANG C, JOHN K, MARK W et al. Relationship between hyperspectral measurements and mangrove leaf nitrogen concentrations. Remote Sensing, 5, 891-908(2013).

[9] CURRAN P J. Remote sensing of foliar chemistry. Remote Sensing of Environment, 30, 271-278(1989).

[10] WANG Xiuzhen, HUANG Jingfeng and LI Yunmei et al. Correlation between chemical contents of leaves and characteristic variables of hyperspectra on rice filed. Transactions of the Chinese Society of Agricultural Engineering, 19, 144-148(2003).

[11] QIN Zhanfei, CHANG Qingrui, XIE Baoni et al. Rice leaf nitrogen content estimation based on hysperspectral imagery of UAV in Yellow River diversion irrigation district. Transactions of the Chinese Society of Agricultural Engineering, 32, 77-85(2016).

[12] CHEN Yunhao, WANG Sijia, ZHAO Yifei et al. Progress and development trend of agricultural hyperspectral remote sensing research. Geography and Geo-information Science, 35, 1-8(2019).

[13] ZHANG Guosheng, XU Tongyu, YU Fenghua et al. Nitrogen content inversion of rice leaf based on the hyperspectral data. Acta Agriculturae Zhejiangensis, 29, 845-849(2017).

[14] ZHAO Xiaomin, SUN Xiaoxiang, WANG Fangdong et al. A Summary of the Researches on Hyperspectral Remote Sensing Monitoring of Rice. Acta Agriculturae Universitis Jiangxiensis, 41, 1-12(2019).

[15] YU Fenghua, CAO Yingli, Xu Tongyu et al. Precision fertilization by UAV for rice at tillering stage in cold region based on hyperspectral remote sensing prescription map. Transactions of the Chinese Society of Agricultural Engineering, 36, 103-110(2020).

[16] FENG Shuai, XU Tongyu, YU Fenghua et al. Researchof Method for inverting nitrogen content in canopy leaves of Jja⁃ponica rice in Northeastern China based on hyperspectral re⁃mote sensing of Unmanned Aerial Vehicle[J]. Spectroscopy and Spectral Analysis, 39, 3281-3287(2019).

[17] CHEN Chunling, ZHOU Changxian, YU Fenghua et al. A study on inversion method of nitrogen content in japonica rice based on spectral characteristic parameters. Journal of Shen-yang Agricultural University, 51, 218-224(2020).

[18] YU F, FENG S, DU W et al. A Study of nitrogen deficiency inversion in rice leaves based on the hyperspectral reflectance differential. Frontiers in Plant Science, 11, 573272(2020).

[19] SUN Jia, YANG Jian, SHI Shuo et al. Estimating rice leaf nitrogen concentration： Influence of regression algorithms based on passive and active leaf reflectance. Remote Sensing, 9, 951(2017).

[20] ZHOU Z H, FENG J. Deep forest. Nat Sci, 6, 74-86(2019).

[21] LI Jinmin, CHEN Xiuqing, YANG Qi et al. Deep learning models for estimation of paddy rice leaf nitrogen concentration based on canopy hyperspectral data. Acta Agronomica Sinica, 47, 1342-1350(2021).