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国土资源遥感  2018, Vol. 30 Issue (1): 87-94    DOI: 10.6046/gtzyyg.2018.01.12
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基于Landsat8 OLI影像光谱的土壤盐分估算模型研究
张雅莉1,2(), 塔西甫拉提·特依拜2(), 阿尔达克·克里木1,2, 张东1,2, 依力亚斯江·努尔麦麦提1,2, 张飞1,2
1.新疆大学资源与环境科学学院,乌鲁木齐 830046
2.新疆大学绿洲生态教育部重点实验室,乌鲁木齐 830046
Estimation model of soil salinization based on Landsat8 OLI image spectrum
Yali ZHANG1,2(), Tashpolat·Teyibai2(), Ardak·Kelimu1,2, Dong ZHANG1,2, Ilyas·Nuermaimaiti1,2, Fei ZHANG1,2
1. College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China
2. Key Laboratory of Oasis Ecology under Ministry of Education, Xinjiang University, Urumqi 830046, China
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摘要 

针对艾比湖流域盐渍化土壤盐分定量监测的需要,利用Landsat8 OLI 多光谱影像进行土壤盐分估算模型研究,以提高土壤盐分反演的精度。通过分析不同含盐量土壤的影像光谱反射率特征和不同变换形式的光谱反射率与盐分的相关性,寻求对盐分含量敏感的光谱波段; 采用多元逐步回归算法,分别建立基于OLI影像光谱与ASD光谱仪重采样光谱的土壤盐分估算模型,并对影像光谱模型进行校正。结果表明: ASD重采样光谱数据的对数倒数一阶微分变换的土壤盐分估算模型精度较高,模型的决定系数(R2)为0.779; 校正后的OLI影像光谱土壤盐分估算模型的R2从0.28提高到0.777 6,且均方根误差值仅为0.281。本研究实现了从实地测量光谱尺度向遥感多光谱尺度的转换,为土壤盐渍化的遥感定量监测提供了科学参考。

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张雅莉
塔西甫拉提·特依拜
阿尔达克·克里木
张东
依力亚斯江·努尔麦麦提
张飞
关键词 高光谱Landsat8 OLI影像盐渍化相关系数多元逐步回归    
Abstract

The purpose of this paper is to improve the precision of salinity monitoring model with Landsat8 OLI multi-spectral images in the oasis of arid area. In this paper, the authors chose the Ebinur Lake region as the study area, and reflectivity of saline soil based on OLI image and spectral reflectivity from resampled ASD data were measured respectively. According to the findings of the correlation analysis of twelve transforms of soil spectral reflectance with soil salt content, multiple stepwise regression analysis algorithm was used. Based on the analysis, the authors chose the most sensitive band ranges to establish a soil salinization monitoring model using the ASD actual measurement data and corrected OLI image inversion of soil salinity. The results show that the soil salt content inversion model based on the measured field spectral is satisfying, the first-order of the logarithm of the reciprocal with the best accuracy and the R2 is 0.779. Spectral reflectivity after resampling data performed better than those monitoring models with OLI spectral data, the coefficient of determination (R2) is raised from 0.28 to 0.777 6, and the RMSE is 0.281. The authors realized the scale transformation of the soil salt content spectral inversion 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 at the regional scale.

Key wordshyperspectral    Landsat8 OLI image    soil salinization    correlation coefficient    multiple stepwise regression
收稿日期: 2016-06-15      出版日期: 2018-02-08
:  TP79  
  S126  
基金资助:国家自然科学基金项目“干旱区湖泊流域陆面过程及人类活动适应性—以艾比湖为例”(编号: 41130531)、“新疆于田绿洲土壤盐渍化风险遥感定量评估研究”(编号: 41561089)和“变化环境下干旱区内陆艾比湖流域景观格局演变与水资源的相互作用机理研究”(编号: 41361045)共同资助
作者简介:

第一作者: 张雅莉(1990-),女,硕士研究生,主要研究方向为干旱区土壤遥感应用。Email:yalii_zhang@163.com

引用本文:   
张雅莉, 塔西甫拉提·特依拜, 阿尔达克·克里木, 张东, 依力亚斯江·努尔麦麦提, 张飞. 基于Landsat8 OLI影像光谱的土壤盐分估算模型研究[J]. 国土资源遥感, 2018, 30(1): 87-94.
Yali ZHANG, Tashpolat·Teyibai, Ardak·Kelimu, Dong ZHANG, Ilyas·Nuermaimaiti, Fei ZHANG. Estimation model of soil salinization based on Landsat8 OLI image spectrum. Remote Sensing for Land & Resources, 2018, 30(1): 87-94.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2018.01.12      或      https://www.gtzyyg.com/CN/Y2018/V30/I1/87
Fig.1  研究区及野外采样点分布

(影像底图为Landsat8 OLI B1(R),B2(G),B3(B)假彩色合成影像)

Fig.2  不同盐分含量的OLI影像光谱反射率
Fig.3  OLI光谱反射率及其变换结果与土壤含盐量的相关关系
变换形式 回归方程 R2 RMSE
B Y=18.32X0.49+13.457 0.210 0.276
B Y=226.15X0.49-208.74X0.68+10.33 0.280 0.216
lg B Y=1.44X0.49+0.493X0.68+21.82 0.181 0.452
1/lg B Y=-1.014X0.87+0.404X0.68+2.773 0.084 0.871
1/B Y=0.947X0.91-1.151X0.68+217.3 0.090 0.535
B' Y=3.039X0.62+9.392X0.68+3.664 0.148 0.751
(B) ' Y=4 040.193X0.82+7 754.244X0.68+12.405 0.260 0.873
(lg B)' Y=80.78X0.57+276.949X0.68+6.045 0.190 0.545
(1/lg B)' Y=102.5X0.92+154.661X0.68+6.410 0.241 0.257
(1/B) ' Y=100.4X0.62+7.1X0.68+87 0.188 0.398
B″ Y=232.566X1.69+154.661X0.68+6.410 0.200 0.331
(lg B) Y=56.6X1.69+47X0.68+250 0.169 0.387
Tab.1  OLI土壤盐分估算模型
Fig.4  用实测盐分值验证OLI影像预测盐分值
Fig.5  ASD光谱反射率及其变换结果与土壤含盐量的相关关系
变换形式 回归方程 R2 RMSE
B Y = 229.6X0.433-275.7X0.62+29.55 0.465 0.351
B Y=258X0.433-289.8X0.62+28.3 0.431 0.354
lg B Y=17.54X0.433-16.2X0.62+9.6 0.161 0.812
(1/lg B) Y=-1.014X0.433+0.404X0.68+2.773 0.409 0.331
1/B Y=0.947X0.433-1.151X0.62+6.071 0.034 0.745
(B)' Y=3.039X0.433+9.392X0.62+3.664 0.215 0.561
(B)' Y=4 040.193X0.433+7 754.244X0.62+12.405 0.419 0.313
(lg B)' Y=80.78X0.433+276.949X0.62+6.045 0.619 0.235
(1/lg B)' Y=96.957X0.433+37.491X0.68+3.886 0.779 0.203
(1/B)' Y=232.566X0.433+154.661X0.62+6.410 0.249 0.591
(B) Y=939.573X0.433-940.738X0.68+5.435 0.500 0.295
1/ lg B) Y=3563.708X0.433+8558.546X1.79+3.886 0.389 0.452
Tab.2  ASD土壤盐分估算模型
Fig.6  用实测盐分值验证ASD重采样光谱预测盐分值
Fig.7  ASD重采样模型与OLI模型预测盐分值的关系
Fig.8  校正后OLI影像光谱预测和ASD实测盐分值散点图
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