Feasibility Analysis of Stokes Related Parameters for Oilseed Rape Growth Monitoring

ZHANG Wangfei; WEN Zhe; ZHANG Yahong; ZHANG Tingwei; LI Yun

doi:10.13203/j.whugis20180375

Volume 45 Issue 2

Feb. 2020

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Geomatics and Information Science of Wuhan University > 2020 > 45(2): 242-249. > DOI: 10.13203/j.whugis20180375

ZHANG Wangfei, WEN Zhe, ZHANG Yahong, ZHANG Tingwei, LI Yun. Feasibility Analysis of Stokes Related Parameters for Oilseed Rape Growth Monitoring[J]. Geomatics and Information Science of Wuhan University, 2020, 45(2): 242-249. DOI: 10.13203/j.whugis20180375

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Feasibility Analysis of Stokes Related Parameters for Oilseed Rape Growth Monitoring

1.
Southwest Forestry University, Kunming 650224, China
2.
Henan Branch of the Central Route Construction Management Bureau of South to North Water Transfer Project, Zhengzhou 467300, China

Funds:

The National Natural Science Foundation of China 31860240

the National Key Research and Development Program of China 2017YFD0600900

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Author Bio:
ZHANG Wangfei, PhD, associate professor, specializes in microwave remote sensing technology application in agriculture and forestry. E-mail:mewhff@163.com
Received Date: September 05, 2019
Published Date: February 04, 2020

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Abstract

Abstract

Synthetic aperture radar (SAR) has been proved as an effective tool for agricultural monitoring and its effectiveness depends on the accurate and appropriate interpretation of SAR Information. Stokes parameters, which is proposed on the dichotomy principle of electromagnetic wave, describe the changes of the incident electromagnetic wave affected by objects which are radiated by electromagnetic wave, and then obtain the information from the objects. However, to our best knowledge, few reports focus on crop phenology monitoring using Stokes parameters. This study aims to explore the feasibility of Stokes related parameters for crop growth monitoring. In this paper, Stokes parameters and their subparameters are calculated based on assumption of wave transmitted in horizontal and received in both horizontal and vertical polarization. Then these Stokes parameters are derived from 5 multi-temporal Radarsat-2 images and averaged relying on each oilseed rape field area. The correlation between all of the Stokes parameters and oilseed rape growth parameters including above ground biomass (AGB), height and leaf area index (LAI) are computed by Pearson product moment correlation coefficient. The significance of these Stokes parameters for oilseed rape AGB, height and LAI inversion are derived from random forest (RF) model. The results indicate the potential of Stokes parameter for crop growth monitoring and growth parameters inversion. It demonstrates that scattering power related Stokes parameters reveal better performance for crop AGB inversion, but scattering mechanism related Stokes parameters such as degree of polarization (m), the degree of linear polarization (m_l) and the ratio of linear polarization (μ_l) are more sensitive to crop height and LAI. Moreover, the results suggest that it is necessary to analyze the influence of crop structure on scattering power when crop growth parameters inversion is performed with Stokes parameters.
- Stokes parameters,
- oilseed rape,
- AGB,
- height,
- LAI

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References (21)

References

[1]	王雪松.宽带极化信息处理的研究[D].长沙: 国防科学技术大学, 1999 Wang Xuesong. Study on Wideband Polarizaiton Informaiton Processing[D]. Changsha: National University of Defense Technology, 1999
[2]	杨浩.基于时间序列全极化与简缩极化SAR的作物定量监测研究[D].北京: 中国林业科学研究院, 2015 Yang Hao. Study on Quantitative Crop Monitoring by Time Series of Fully Polarimetric and Compact Polarimetric SAR Imagery[D]. Beijing: Chinese Academy of Forestry, 2015
[3]	Chen E, Li Z, Pang Y, et al. Quantitative Evaluation of Polarimetric Classification for Agricultural Crop Mapping[J]. Photogrammetric Engineering & Remote Sensing, 2007, 73(3):279-284 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=a12aacc2e9c7b614d5692b7148051f84
[4]	Lohmann P, Soergel U, Tavakkoli M, et al. Multi-temporal Classification for Crop Discrimination Using TerraSAR-X Spotlight Images[C]. ISPRS Hannover Workshop on High-Resolution Earth Imaging for Geospatial Information, Hannover, Germany, 2009
[5]	Wiseman G, Mcnairn H, Homayouni S, et al. RADARSAT-2 Polarimetric SAR Response to Crop Biomass for Agricultural Production Monitoring[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2014, 7(11):4 461-4 471 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=7799c76471a28e963f90900020b2b563
[6]	Mascolo L, Lopez-Sanchez J M, Vicente-Guijalba F, et al. Retrieval of Phenological Stages of Onion Fields During the First Year of Growth by Means of C-band Polarimetric SAR Measurements[J]. International Journal of Remote Sensing, 2015, 36(12):3 077-3 096 doi: 10.1080/01431161.2015.1055608
[7]	Jiao X, Mcnairn H, Shang J, et al. The Sensitivity of RADARSAT-2 Polarimetric SAR Data to Corn and Soybean Leaf Area Index[J]. Canadian Journal of Remote Sensing, 2011, 37(1):69-81 doi: 10.5589/m11-023
[8]	Zhang W, Chen E, Li Z, et al. Rape (Brassica Napus L.) Growth Monitoring and Mapping Based on Radarsat-2 Time-Series Data[J]. Remote Sensing, 2018, 10(2):206 doi: 10.3390/rs10020206
[9]	Shang F, Hirose A. Averaged Stokes Vector Based Polarimetric SAR Data Interpretation[J]. IEEE Transactions on Geoscience & Remote Sensing, 2015, 53(8):4 536-4 547
[10]	Zhang W, Li Z, Chen E, et al. Compact Polarimetric Response of Rape (Brassica Napus L.) at C-Band: Analysis and Growth Parameters Inversion[J]. Remote Sensing, 2017, 9(6):591 doi: 10.3390/rs9060591
[11]	Yang H, Li Zengyuan, Chen E, et al. Temporal Polarimetric Behavior of Oilseed Rape (Brassica Napus L.) at C-Band for Early Season Sowing Date Monitoring[J]. Remote Sensing, 2014, 6(11):10 375-10 394 doi: 10.3390/rs61110375
[12]	Raney R K. Hybrid-Polarity SAR Architecture[J]. IEEE Transactions on Geoscience & Remote Sensing, 2007, 45(11):3 397-3 404 http://d.old.wanfangdata.com.cn/Periodical/ldxb201606001
[13]	Born M, Wolf E. Principles of Optics[M]. England:Cambridge Press, 2016
[14]	Raney R K. Dual-polarized SAR and Stokes Parameters[J]. IEEE Geoscience & Remote Sensing Letters, 2006, 3(3):317-319 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ02747783/
[15]	贾玉秋, 李冰, 程永政, 等.基于GF-1与Landsat-8多光谱遥感影像的玉米LAI反演比较[J].农业工程学报, 2015, 31(9):173-179 http://d.old.wanfangdata.com.cn/Periodical/nygcxb201509027 Jia Yuqiu, Li Bing, Cheng Yongzheng, et al. Comparison Between GF-1 Images and Landsat-8 Images in Monitoring Maize LAI[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(9):173-179 http://d.old.wanfangdata.com.cn/Periodical/nygcxb201509027
[16]	李平湘, 刘致曲, 杨杰, 等.利用随机森林回归进行极化SAR土壤水分反演[J].武汉大学学报·信息科学版, 2019, 44(3): 405-412 http://ch.whu.edu.cn/CN/abstract/abstract6382.shtml Li Pingxiang, Liu Zhiqu, Yang Jie, et al. Soil Moisture Retrieval of Winter Wheat Fields Based on Random Forest Regression Using Quad-Polarimetric SAR Images[J]. Geomatics and Information Science of Wuhan University, 2019, 44(3): 405-412 http://ch.whu.edu.cn/CN/abstract/abstract6382.shtml
[17]	Breiman L. Random Forests[J]. Machine Learning, 2001, 45(1):5-32 doi: 10.1023/A:1010933404324
[18]	姚登举, 杨静, 詹晓娟.基于随机森林的特征选择算法[J].吉林大学学报(工学版), 2014, 44(1): 137-141 http://d.old.wanfangdata.com.cn/Periodical/jlgydxzrkxxb201401024 Yao Dengju, Yang Jing, Zhan Xiaojuan. Feature Selection Algorithm Based on Random Forest[J]. Journal of Jilin University (Engineering and Technology Edition), 2014, 44(1): 137-141 http://d.old.wanfangdata.com.cn/Periodical/jlgydxzrkxxb201401024
[19]	Lopez-Sanchez J M, Cloude S R, Ballester-Berman J D. Rice Phenology Monitoring by Means of SAR Polarimetry at X-Band[J]. IEEE Transactions on Geoscience & Remote Sensing, 2012, 50(7):2 695-2 709
[20]	张亚红.基于极化信息的油菜长势监测[D].昆明: 西南林业大学, 2017 Zhang Yahong. Rape (Brassica Napus L.) Growth Mornitoring Using Polarisation Information[D]. Kunming: Southwest Forestry University, 2017
[21]	杨浩, 杨贵军, 顾晓鹤, 等.小麦倒伏的雷达极化特征及其遥感监测[J].农业工程学报, 2014, 30(7):1-8 doi: 10.3969/j.issn.1002-6819.2014.07.001 Yang Hao, Yang Guijun, Gu Xiaohe, et al. Radar Polarimetric Response Features and Remote Sensing Monitoring of Wheat Lodging[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(7):1-8 doi: 10.3969/j.issn.1002-6819.2014.07.001

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