| 孙媛,贾萍萍,尚天浩,张俊华.基于地表高光谱与OLI影像的土壤含盐量和pH值估测[J].干旱地区农业研究,2021,39(1):164~174 |
| 基于地表高光谱与OLI影像的土壤含盐量和pH值估测 |
| Estimation of soil salinity and pH value based on surface hyperspectral and OLI images |
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| DOI：10.7606/j.issn.1000-7601.2021.01.22 |
| 中文关键词: 盐碱化土壤 含盐量 pH值 植被指数 敏感波段 高光谱 Landsat 8 OLI影像 |
| 英文关键词:saline\|alkali soil salinity pH value vegetation index sensitive band hyper\|spectral Landsat 8 OLI image |
| 基金项目:宁夏回族自治区自然科学基金（2018AAC03007） |
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| 中文摘要: |
| 针对宁夏银北地区大面积土壤盐碱化监测的需要，利用实测植被冠层光谱与Landsat 8 OLI影像相结合进行土壤含盐量和pH值估测研究。对实测植被冠层高光谱与影像多光谱反射率进行倒数、对数、三角函数及其一阶微分等一系列变换，确定最佳光谱变换形式，筛选敏感植被指数和敏感波段，分别建立基于实测植被光谱与Landsat 8 OLI影像光谱的土壤含盐量与pH值估测模型，并基于高光谱数据模型对影像盐分和pH值模型进行校正，以提高影像估测土壤盐碱化的精度。结果表明：经倒数对数变换建立的实测植被高光谱EVI模型和经平滑后敏感波段建立的实测植被高光谱模型对土壤pH值的估测精度较高，模型决定系数分别为0.6257和0.5975；基于实测高光谱植被指数和敏感波段分别对Landsat 8 OLI影像含盐量、pH值估测模型进行校正，影像敏感植被指数和敏感波段含盐量模型决定系数分别提高了0.3207和0.3762，pH值估测模型决定系数分别提高了0.2065和0.2487。采用敏感植被指数和敏感波段同时估测土壤含盐量和pH值，实现了从实地测量高光谱向遥感多光谱尺度的转换。 |
| 英文摘要: |
| The purpose of this paper is to improve the precision of salinity and alkalinity monitoring model with measured vegetation canopy spectrum and Landsat 8 OLI multi\|spectral image on Northern Yinchuan Plain of Ningxia. In this paper, the authors took six transformations on the measured vegetation canopy hyperspectral and image multi\|spectral reflectance, chose the best spectral transformation form, the most sensitive vegetation index and bands to establish the soil salt content and pH value estimation model using the resampled actual measurement data and corrected Landsat 8 OLI image inversion of soil salinity and alkalinity. The results showed that the measured vegetation hyperspectral EVI model established by inverse logarithmic transformation and the measured vegetation hyperspectral model established by smoothed sensitive bands have higher accuracy in estimating soil pH value, and the model determination coefficients are 0.6257 and 0.5975, respectively. The measured hyperspectral vegetation index and sensitive bands corrected the salt content and pH estimation models of the Landsat 8 OLI image, respectively. The coefficients of determination of the sensitive vegetation index and the salt content model of the sensitive bands were increased by 0.3207 and 0.3762, respectively. The model determination coefficients were increased by 0.2065 and 0.2487, respectively. The study used sensitive vegetation index and sensitive bands to estimate soil salinity and pH value simultaneously, and realized the scale transformation of the soil salt content and pH value spectral estimation model from field measurements of spectral scales to spectral scale of multi\|spectral remote sensing, and the results could provide a theoretical reference for further improvement of the accuracy of quantitative remote sensing monitoring of soil salt content and pH value at the local and similar regions. |
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