Phenological Phase Identification of Oilseed Rape(Brassica napus L.)Using Typical Stokes Parameters

被引:0
作者
Zhang Y. [1 ]
Zhang W. [1 ]
Xu K. [2 ]
Li J. [3 ]
机构
[1] School of Forestry, Southwest Forestry University, Kunming
[2] Research Institute of Forest Resource Information Techniques, China Academy of Forestry, Beijing
[3] Kunming Real Estate Ownership Investigation Center, Kunming Institute of Surveying and Mapping of Land Planning and Prospecting, Kunming
来源
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2023年 / 48卷 / 08期
关键词
decision tree algorithm; oilseed rape; phenological identification; Stokes parameters;
D O I
10.13203/j.whugis20210394
中图分类号
学科分类号
摘要
Objectives: The key phenological information of oilseed rapeseed (Brassica napus L.) plays an important role in field management, viewing time prediction and yield estimation of the oilseed rape. It is also an important part of precision agriculture. Polarimetric synthetic aperture radar technology shows great potential in phenological phase identification with its all-weather monitoring capability and its sensitivity to the crop structural information. Methods: First, we identified the 5 phenological phases of the oilseed rape on the test area with 5 time series full-polarization Radarsat-2 data, which covers the whole growth period of the oilseed rape. 6 typical Stokes parameters are extracted and applied in the identification of oilseed rape phenological phases, the extracted Stokes parameters includ averaged intensity(g0), normalized average intensity(g0m), averaged degree of polarization(ρm), perimeter degree of zero orientation route(Pdor),inclination degree of zero aperture route(Idap), and arc asymmetry degree of zero aperture route(Aadap). Then,The phenological phases of oilseed rape is identified by the decision tree (DT) algorithm based on the comparative analysis of the dynamic response of the 6 special Stokes parameters to rape growth stages. Results and Conclusions: Among the extracted Stokes parameters applied in this study, except ρm and Aadap, other parameters show great sensitivity to the change of the oilseed rape phenological phases. The DT algorithm also perform well in the classification of the oilseed rape phenological phases. The classification results agree well with the field measured samples, and the overall classification accuracy is 87.4%, while the highest classification accuracy of each phenological phase is 94.3%. © 2023 Wuhan University. All rights reserved.
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页码:1322 / 1330
页数:8
相关论文
共 33 条
  • [1] Hanzhong Wang, Strategy for Rapeseed Industry Development Based on the Analysis of Rapeseed Production and Demand in China[J], Chinese Journal of Oil Crop Sciences, 29, 1, pp. 101-105, (2007)
  • [2] Chunming Liu, Study on Rapeseed Production and Biodiesel Development in China[D], (2008)
  • [3] Jia Zhai, Fenghui Yuan, Jiabing Wu, Research Progress on Vegetation Phenological Changes[J], Chinese Journal of Ecology, 34, 11, pp. 3237-3243, (2015)
  • [4] Wangfei Zhang, Erxue Chen, Zengyuan Li, Et al., Review of Applications of Radar Remote Sensing in Agriculture[J], Journal of Radars, 9, 3, pp. 444-461, (2020)
  • [5] Mingtao Xiang, Wei Wei, Wenbin Wu, Review of Vegetation Phenology Estimation by Using Remote Sensing[J], China Agricultural Information, 30, 1, pp. 55-66, (2018)
  • [6] Lopez-Sanchez J M,, Cloude S R,, David Ballester-Berman J., Rice Phenology Monitoring by Means of SAR Polarimetry at X-band[J], IEEE Transactions on Geoscience and Remote Sensing, 50, 7, pp. 2695-2709, (2012)
  • [7] Wang H., Crop Phenology Retrieval via Polarimetric SAR Decomposition and Random Forest Algorithm [J], Remote Sensing of Environment, 231, (2019)
  • [8] Francis C., Tracking Crop Phenological Development Using Multi-temporal Polarimetric Radarsat-2 Data[J], Remote Sensing of Environment, 210, pp. 508-518, (2018)
  • [9] Yue Yang, Analysis of SAR Scattering Signal Response and Phenological Monitoring Based on Stokes Parameters in Rape[D], (2019)
  • [10] Chen E X, Li Z Y,, Pang Y,, Et al., Quantitative Evaluation of Polarimetric Classification for Agricultural Crop Mapping[J], Photogrammetric Engineering & Remote Sensing, 73, 3, pp. 279-284, (2007)