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All issues Volume 338 (2022) E3S Web Conf., 338 (2022) 01029 References
Open Access
Issue
E3S Web Conf.
Volume 338, 2022
7th International Conference on Environmental Science and Material Application (ESMA 2021)
Article Number 01029
Number of page(s) 6
DOI https://doi.org/10.1051/e3sconf/202233801029
Published online 20 January 2022
  1. Zhao Chunjiang. Progress of agricultural remote sensing research and application[J]. Journal of Agricultural Machinery, 2014, 45(12):277–293. [Google Scholar]
  2. Chen Zhongxin, Ren Jianqiang, Tang Huajun, et al. Progress and prospects of agricultural remote sensing research applications[J]. Journal of Remote Sensing, 2016, 20(5):748–767. [Google Scholar]
  3. Shi Zhou, Liang Zongzheng, Yang Yuanyuan, et al. Current status and outlook of agricultural remote sensing research[J]. Journal of Agricultural Machinery, 2015, 46(2):247–260. [Google Scholar]
  4. Xing Suli, Zhang Guanglu. Current situation and prospect of agricultural remote sensing application in China[J]. Journal of Agricultural Engineering, 2003, 19(6):174–178. [Google Scholar]
  5. Wu B.F., Zhang L., Li Q.Z., et al. Current status and prospects of agricultural and ecological remote sensing[J]. Journal of Remote Sensing, 2009, 13(s1):266–271. [Google Scholar]
  6. Zhang Zengxiang, Wang Xiao, Wen Qingke, et al. Research progress of remote sensing application of land resources[J]. Journal of Remote Sensing, 2016, 20(5):1243–1258. [Google Scholar]
  7. Liu Haiying, Gao Jixi, Li Zhenghai. Progress of remote sensing research on land cover and land use[J]. Remote Sensing of Land Resources, 2001(4):7–12. [Google Scholar]
  8. Zhang Ruolin, Wan Li, Zhang Fawang, et al. Progress of land use remote sensing classification methods[J]. South-North Water Transfer and Water Conservancy Science and Technology, 2006, 4(2):39–42. [Google Scholar]
  9. Tian M.-L., Ban S.-T., Chang Q.-R., Ma W.-J., Yin Z., Wang L. Inversion of chlorophyll content of cotton based on UAV imaging spectrometer data[J]. Journal of Agricultural Machinery, 2016, 47(11):285–293. [Google Scholar]
  10. Yan Lin. Research on physiological and biochemical parameters of rice in Ningxia Yellow Irrigation Area based on hyperspectral remote sensing[D]. Northwest University of Agriculture and Forestry Science and Technology, 2017. [Google Scholar]
  11. Wang Ji-Hua, Huang Wen-Jiang, Laocai-Lian et al. 2007. Inversion of nitrogen vertical distribution from wheat canopy reflectance spectra using PLS algorithm. Spectroscopy and Spectral Analysis, 27(7):1319–1322. [Google Scholar]
  12. Tong Q.X., Zhang B., Zhang L.F. Frontier progress of hyperspectral remote sensing in China[J]. Journal of Remote Sensing, 2016, 20(05):689–707. [Google Scholar]
  13. Fang Xiuqin, Zhang Wanchang. 2003. A review of remote sensing quantitative methods for leaf area index (LAI). Remote Sensing of Land Resources, 15(3):58–62. [Google Scholar]
  14. Hui Fengming, Tian Qingjiu, Jin Zhenyu, et al. 2003. Study on the relationship between vegetation index and leaf area index and quantitative analysis. Remote Sensing Information,(2):10–13. [Google Scholar]
  15. Li Kaili, Jiang J.J., Mao R.Z., et al. 2005. Remote sensing monitoring model of vegetation leaf area index. Journal of Ecology, 25(6):1491–1496. [Google Scholar]
  16. Xie Qiaoyun. Study on the generalizability of leaf area index inversion method [Master’s thesis]. Anhui University, 2014. [Google Scholar]
  17. Wang Shuo. Research on the estimation of maize agronomic parameters in Guanzhong area based on hyperspectral remote sensing [D]. Northwest University of Agriculture and Forestry Science and Technology, 2018. [Google Scholar]
  18. Li D., He J., Liu Guishan et al. 2014. Nondestructive detection of cucumber moisture based on hyperspectral imaging technology. Infrared and Laser Engineering, 43(7):2393–2397. [Google Scholar]
  19. Zhang Xiaoyan, Wang Lili, Liu Feng et al. 2012. Research on monitoring model of peanut leaf nitrogen accumulation based on hyperspectral remote sensing Shandong Agricultural Science, 44(3):7–12, 16. [Google Scholar]
  20. Li Na. Construction of a model for monitoring agronomic parameters and seed protein content of wheat based on hyperspectral remote sensing[D]. Northwest University of Agriculture and Forestry Science and Technology, 2017. [Google Scholar]
  21. Jiang H. L., Yang H., Chen S. P., Wang Sh D., Li Xue Ke, Liu, K., Cen, Y. 2015. Research on the accuracy and stability of inversion of vegetation chlorophyll content using spectral indices. Spectroscopy and Spectral Analysis(04):975–981. [Google Scholar]
  22. Yang J., Tian Y.C., Yao X., Cao Z., Zhang Y.S., Zhu Y. 2009a. Hyperspectral estimation model of chlorophyll content in upper leaves of rice. Journal of Ecology(12):6561–6571. [Google Scholar]
  23. Liang L., Yang M.H., Zhang L.P., Lin H. 2011b. Hyperspectral inversion of leaf area index of wheat. Spectroscopy and Spectral Analysis.(06):1658–1662. [Google Scholar]
  24. Feng W., Zhu Y., Tian Y.C., Yao X., Guo T.C., Cao Z. 2007. Research on wheat seed yield prediction model based on hyperspectral remote sensing. Journal of Wheat Crops (06):1076–1084. [Google Scholar]

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