光谱学与光谱分析 |
|
|
|
|
|
Quantitative Retrieval of Soil Salinity Using Hyperspectral Data in the Region of Inner Mongolia Hetao Irrigation District |
QU Yong-hua1, DUAN Xiao-liang2, GAO Hong-yong2, CHEN Ai-ping2, AN Yong-qing2, SONG Jin-ling1, ZHOU Hong-min1, HE Tao1 |
1. Research Center for Remote Sensing and GIS, Dept. Geography, Beijing Normal University, China State key Laboratory ofRemote Sensing Science, Beijing 100875, China 2. Administration of Inner Mongolia Hetao Irrigation District, Wuyuan 015100, China |
|
|
Abstract In the present paper, to investigate the spectral property of salinized soil and the relationship between the soil salinity and the hyperspectral data, the field soil samples were collected in the region of Hetao irrigation, Neimeng in the northwest China from the end of July to the beginning of August. The partial least squares regression (PLSR) model was established based on the statistical analysis of the soil ions and the reflectance of hyperspectra. The independent validation using data which are not included in the calibration model reveals that the proposed model can predicate the main soil components such as the content of total ions (S%), SO2+4, PH and K++Na+ with higher determination coefficients (R2) of 0.728, 0.801, 0.715 and 0.734 respectively. And the ratio of prediction to deviation (RPD) of the above predicted value is larger than 1.6, which indicates that the calibrated PLSR model can be used as a tool to retrieve soil salinity with accurate results. When the PLSR model’s regression coefficients were aggregated according to the wavelength of visual (blue, green and red) and near infrared bands of LandSat Thematic Mapper(TM) sensor, some significant response values were observed, which indicates that the proposed method in this paper can be used to analyse the remotely sensed data from the space-boarded platform.
|
Received: 2008-02-12
Accepted: 2008-05-16
|
|
Corresponding Authors:
QU Yong-hua
E-mail: qyh@bnu.edu.cn
|
|
[1] WENG Yong-ling, GONG Peng(翁永玲, 宫 鹏). Geographical Sciences(地理科学), 2006, 26(3): 369. [2] KANG Qing, ZHANG Zeng-xiang,ZHAO Xiao-li, et al(亢 庆, 张增祥, 赵晓丽, 等). Journal of Arid Land Resources and Environment(干旱区资源与环境), 2006, 20(3): 144. [3] Metternicht G I, Zinck J A. Remote Sensing of Environment, 2003, 85(1): 1. [4] Ben D R, Patkin A, Banin A, et al. International Journal of Remote Sensing, 2002, 23(6): 1043. [5] Farifteh J, Meer F V D, Atzberger C, et al. Remote Sensing of Environment, 2007, 110(1): 59. [6] Csillag F, Pasztor L, Biehl L L. Remote Sensing of Environment, 1993, 43(3): 231. [7] LI Hai-tao, Brunner P, LI Wen-peng, et al(李海涛, Brunner P, 李文鹏, 等). Hydrogeology and Engineering Geology(水文地质工程地质), 2006, 23(5): 75. [8] FU Qing-hua, NI Shao-xiang, WANG Shi-xing, et al(扶卿华, 倪绍祥, 王世新, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2007, 27(1): 48. [9] Nguyen H T, Lee B W. European Journal of Agronomy, 2006, 24(4): 349. [10] Zhou Y P, Jiang J H, Lin W Q, et al. Talanta, 2007, 71(2): 848. [11] Wu J, Aluko R E, Corke H. Journal of Cereal Science, 2006, 44(2): 117. [12] Durand A , Devos O, Ruckebusch C, et al. Analytica Chimica Acta, 2007, 595(1-2): 72. [13] WANG Hui-wen(王惠文). Partial Least Square Regression Method and Its Application(偏最小二乘回归方法及其应用). Beijing: National Defence Industry Press(北京: 国防工业出版社), 1999. 45. [14] WANG Ji-hua, HUANG Wen-jiang, LAO Cai-lian, et al(王纪华, 黄文江, 劳彩莲, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(7): 1319. [15] JI Hai-yan, WANG Peng-xin, YAN Tai-lai(吉海彦, 王鹏新, 严泰来). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(3): 514. [16] Rossel V R A. Chemometrics and Intelligent Laboratory Systems, 2008, 90(1): 72. [17] MEI An-xin, PENG Wang-lu, QIN Qi-ming, et al(梅安新, 彭望琭, 秦其明, 等). Introduction to Remote Sensing(遥感导论). Beijing: Higher Education Press(北京: 高等教育出版社), 2001. 156.
|
[1] |
LIANG Ye-heng1, DENG Ru-ru1, 2*, LIANG Yu-jie1, LIU Yong-ming3, WU Yi4, YUAN Yu-heng5, AI Xian-jun6. Spectral Characteristics of Sediment Reflectance Under the Background of Heavy Metal Polluted Water and Analysis of Its Contribution to
Water-Leaving Reflectance[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 111-117. |
[2] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[3] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[4] |
ZHU Wen-jing1, 2,FENG Zhan-kang1, 2,DAI Shi-yuan1, 2,ZHANG Ping-ping3,JI Wen4,WANG Ai-chen1, 2,WEI Xin-hua1, 2*. Multi-Feature Fusion Detection of Wheat Lodging Information Based on UAV Multispectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 197-206. |
[5] |
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. |
[6] |
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. |
[7] |
LIANG Shou-zhen1, SUI Xue-yan1, WANG Meng1, WANG Fei1, HAN Dong-rui1, WANG Guo-liang1, LI Hong-zhong2, MA Wan-dong3. The Influence of Anthocyanin on Plant Optical Properties and Remote Sensing Estimation at the Scale of Leaf[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 275-282. |
[8] |
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. |
[9] |
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. |
[10] |
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. |
[11] |
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. |
[12] |
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. |
[13] |
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. |
[14] |
LI Si-yuan, JIAO Jian-nan, WANG Chi*. Specular Reflection Removal Method Based on Polarization Spectrum
Fusion and Its Application in Vegetation Health Monitoring[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3607-3614. |
[15] |
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. |
|
|
|
|