| 光谱学与光谱分析 |
|
|
|
|
|
| Study on the Relationship between Hyperspectral Polarized Information of Soil Salinization and Soil Line |
| XU Wen-ru, HAN Yang*, QIN Yan, JIN Lun |
| School of Geography Science, Northeast Normal University, Changchun 130024, China |
|
|
|
|
Abstract It has important significance to assess soil salinization correctly for agricultural production and ecological environment. Soil line can indicate soil salinization in a certain extent. But the soil spectral characteristics obtained at different angles will change with the changing of the soil line parameters. Base on polarized hyper-spectral reflectivity obtained in the laboratory, the study analyzes the relationship between the soil salinization and soil line parameters, explores preliminarily the best way to obtain soil line. The results show: (1) Soil spectral reflectance gradually increased slowly with increasing band. With the enhanced level of salinization, soil spectral reflectance of the first to be gradually reduced to a critical value and then gradually increased. (2) Soil salinization has a linear correlation with the soil slope and intercept. With the enhanced level of salinization, soil slope becomes smaller, and intercept becomes larger. (3) Viewing zenith angle affects the relationship between the polarization state and soil line parameters. When viewing zenith angle is fixed, there is a regularity between the polarization state and soil line parameters. When the viewing zenith angle is between 0°～50°, with the angle becoming larger, soil slope becomes larger, and intercept becomes smaller. (4) Polarization states affects degree of correlation between soil salinization and soil line parameters. When polarization angle is 90° and viewing zenith angle is 25°, the relationship model between soil salinization and soil line parameters is better. The research results can be used to evaluate the degree of salinization soil.
|
|
Received: 2014-08-29
Accepted: 2014-12-06
|
|
|
|
Corresponding Authors:
HAN Yang
E-mail: hany025@nenu.edu.cn
|
|
[1] LI Jian-guo, PU Li-jie, ZHU Ming, et al(李建国, 濮励杰, 朱 明). Acta Geographica Sinica(地理学报), 2012, 67(9): 1233.
[2] Weng Yonglin, Gong Peng, Zhu Zhiliang. Canadian Journal of Remote Sensing, 2008, 34(3): 259.
[3] YAO Yuan, DING Jian-li, ZHANG Fang, et al(姚 远，丁建丽，张 芳，等). Chinese Journal of Applied Ecology(应用生态学报)，2013, 24(11)：3213.
[4] LIU Huan-jun, ZHANG Bai, WANG Zong-ming, et al(刘焕军，张 柏，王宗明，等). J. Infrared Millim. Waves(红外与毫米波学报)，2008, 27(2): 138.
[5] ZHAO Zhen-liang, TASHPOLAT Tiyip, SUN Qian(赵振亮, 塔西甫拉提·特依拜, 孙 倩). Progress in Geography(地理科学进展), 2014, 33(2): 280.
[6] GUAN Hong, JIA Ke-li, ZHANG Zhi-nan, et al(关 红, 贾科利, 张至楠, 等). Remote Sensing For Land & Resources(国土资源遥感), 2015, 27(2): 100.
[7] ZHANG Qiao, SUN Xiao-bing, HONG Jin, et al(张 荞, 孙晓兵, 洪 津, 等). J. Infrared Millim. Waves(红外与毫米波学报), 2013, 32(6): 502.
[8] HAN Yang, QIN Wei-chao, WANG Ye-qiao(韩 阳, 秦伟超, 王野乔). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2014, 34(6): 1640.
[9] Maignan F, Breon F M, Fedele E, et al. Remote Sensing Environ., 2009, 113: 2642.
[10] Baret F, Jacquemoud S, Hanocq J. F. Adv. Space Res., 1993, 13(5): 281.
[11] LIU Huan-jun, ZHANG Bai, SONG Kai-shan, et al(刘焕军，张 柏，宋开山，等). Journal of Remote Sensing(遥感学报)，2008，12(1)：0119. |
| [1] |
WANG Cai-ling1，ZHANG Jing1，WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
| [2] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
| [3] |
LI Xin-quan1, 2，ZHANG Jun-qiang1, 3*，WU Cong-jun1，MA Jian1, 2，LU Tian-jiao1, 2，YANG Bin3. Optical Design of Airborne Large Field of View Wide Band Polarization Spectral Imaging System Based on PSIM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 250-257. |
| [4] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
| [5] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
| [6] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
| [7] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
| [8] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
| [9] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
| [10] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
| [11] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
| [12] |
QIAN Rui1, XU Wei-heng2, 3 , 4*, HUANG Shao-dong2, WANG Lei-guang2, 3, 4, LU Ning2, OU Guang-long1. Tea Plantations Extraction Based on GF-5 Hyperspectral Remote Sensing
Imagery in the Mountainous Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3591-3598. |
| [13] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
| [14] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
| [15] |
SUN Lin1, BI Wei-hong1, LIU Tong1, WU Jia-qing1, ZHANG Bao-jun1, FU Guang-wei1, JIN Wa1, WANG Bing2, FU Xing-hu1*. Identification Algorithm of Green Algae Using Airborne Hyperspectral and Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3637-3643. |
|
|
|
|
