Hyperspectral remote sensing of soil salinity for coastal saline soil in the Yellow River Delta based on HICO bands

An Deyu; Xing Qianguo; Zhao Gengxing

doi:10.3969/j.issn.0253-4193.2018.06.005

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An Deyu, Xing Qianguo, Zhao Gengxing. Hyperspectral remote sensing of soil salinity for coastal saline soil in the Yellow River Delta based on HICO bands[J]. Haiyang Xuebao, 2018, 40(6): 51-59. doi: 10.3969/j.issn.0253-4193.2018.06.005

Citation:

An Deyu, Xing Qianguo, Zhao Gengxing. Hyperspectral remote sensing of soil salinity for coastal saline soil in the Yellow River Delta based on HICO bands[J]. Haiyang Xuebao, 2018, 40(6): 51-59. doi: 10.3969/j.issn.0253-4193.2018.06.005

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Hyperspectral remote sensing of soil salinity for coastal saline soil in the Yellow River Delta based on HICO bands

doi: 10.3969/j.issn.0253-4193.2018.06.005

1.
Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China;University of Chinese Academy of Sciences, Beijing 100049, China
2.
National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resource and Environment, Shangdong Agricultural University, Tai'an 271018, China

Received Date: 2017-06-21
Rev Recd Date: 2017-10-18

Abstract

Abstract

This study aims to use hyperspectral reflectance to estimate soil salinity. Taking the coastal saline soil of the Yellow River Delta as an example, we collected in situ ground surface reflectance and soil samples for salinity analysis, and integrated with the HICO (Hyperspectral Imager for the Coastal Ocean) imagery data to map the distribution of salinity. The spectral features were established by band combination method. The sensitive features were selected by correlation analysis. The optimal models were selected by the determination coefficients R². The relationship between the in situ reflectance and the HICO hyperspectral reflectance is used to modify the model. And these models were applied to HICO images. The study showed that the models of band ratio (RI) and band difference (DI) with significantly high correlations with the soil salinity were established. The power function models established by DI_{(845, 473)}, DI_{(839, 490)}, DI_{(845, 496)}, DI_{(839, 501)} were the best ones (the determination coefficients R²>0.86, and the relative prediction deviation RPD>3). The inversion results in the HICO from these models were consistent with each other, and can reflect the distribution of soil salinity for the Yellow River Delta. This study suggests that it is feasible to estimate the soil salinity by using the hyperspectral data, which can provide a reference for quantitative inversion of soil salinity in the coastal region.
- the Yellow River Delta,
- saline soil,
- soil salinity,
- hyperspectral remote sensing

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References(25)

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