基于HICO波段的滨海土壤盐分遥感反演研究

安德玉; 邢前国; 赵庚星

doi:10.3969/j.issn.0253-4193.2018.06.005

基于HICO波段的滨海土壤盐分遥感反演研究

doi: 10.3969/j.issn.0253-4193.2018.06.005

1.
中国科学院烟台海岸带研究所海岸带环境过程与生态修复重点实验室, 山东烟台 264003;中国科学院大学, 北京 100049
2.
山东农业大学资源与环境学院土肥资源高效利用国家工程实验室, 山东泰安 271018

基金项目: 国家自然科学基金（41676171）；中国科学院科研仪器研制项目（YJKYYQ20170048）；青岛海洋科技国家实验室创新项目（2016ASKJ02）。

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出版历程
- 收稿日期: 2017-06-21
- 修回日期: 2017-10-18

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

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

摘要

摘要: 本研究以黄河三角洲滨海盐渍土为例，尝试使用HICO （Hyperspectral Imager for the Coastal Ocean）高光谱影像结合现场实测高光谱数据进行表层土壤全盐含量的反演。采用波段组合的方法建立光谱参量，通过相关分析筛选出敏感光谱参量，以决定系数R²选出最佳模型；利用HICO影像反射率与实测高光谱反射率之间的关系，对模型进行修正，并应用于影像。研究发现，比值（RI）、差值（DI）波段组合方法建立的光谱参量与表层土壤全盐含量的相关性明显提高。DI_{（845，473）}、DI_{（839，490）}、DI_{（845，496）}及DI_{（839，501）}的幂函数模型效果最好，且验证决定系数R²均大于0.86，相对分析误差RPD>3，RMSE较小。此外，HICO遥感影像的模型反演结果较为一致，能够反映表层土壤全盐含量的分布。研究显示，利用高光谱数据进行表层土壤全盐含量的反演建模具有可行性，可为区域表层土壤全盐含量的定量反演提供参考。
- 黄河三角洲 /
- 盐渍土 /
- 表层土壤全盐含量 /
- 高光谱遥感反演
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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