| 基于热红外发射率光谱的土壤盐分预测模型的建立与验证 |
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| 引用本文： | 阿尔达克·克里木,塔西甫拉提·特依拜,张 飞,雷 磊,张 东.基于热红外发射率光谱的土壤盐分预测模型的建立与验证[J].农业工程学报,2015,31(17):115-120. |
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| 作者姓名： | 阿尔达克·克里木 塔西甫拉提·特依拜 张 飞 雷 磊 张 东 |
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| 作者单位： | 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 830046,1. 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 830046,1. 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 830046,1. 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 830046,1. 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 |
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| 基金项目： | 国家自然科学基金重点项目（41130531）；博士创新项目(XJUBSCX-2012027)；开放课题（XJDX0201-2012-08） |
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| 摘 要： | 该研究尝试性地分析盐渍化土壤热红外发射率光谱特征，并建立土壤盐分高光谱预测模型，旨在为遥感传感器识别土壤盐分信息奠定基础。首先，采用FTIR（Fourier transform infrared spectrometer）温度与发射率分离处理软件进行土壤温度和发射率的分离。运用高斯滤波平滑法对研究区野外测的土壤样品热红外发射率光谱数据进行滤波去噪处理。其次，对不同土壤含盐量热红外发射率光谱数据进行特征分析；然后在原始热红外发射率光谱数据的基础上进行4种形式的数学变换，分析热红外发射率光谱数据的变换处理形式与土壤含盐量之间的定量相关性。最后，使用多元回归方法建立预测模型并进行精度评价。结果表明：经过数据变换处理后，发射率光谱差异性有所提高；以平方根变换后的热红外光谱数据建立的预测模型效果较好，R2达到0.82。该研究将热红外遥感独特的发射率光谱特性应用于土壤盐渍化的实际科学问题中，为定量地分析盐渍土热红外发射率光谱信息提供参考。
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| 关 键 词： | 土壤 盐分 光谱分析 遥感 盐渍化 热红外 发射率光谱 逐步多元回归 |
| 收稿时间： | 4/8/2015 12:00:00 AM |
| 修稿时间： | 8/7/2015 12:00:00 AM |
Prediction model of saline of soil and its validation based on thermal infrared emissivity spectrum |
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| Institution: | 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,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,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,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 and 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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| Abstract: | Abstract: In arid and semi -arid inland areas, due to high evapotranspiration , the minerals in the soil water accumulation in the soil surface, soil salinization becomes a serious threat to the local agricultural production, ecological stability and economic development. At present, most research on remote sensing technology to monitor the saline soil is focused on quantifying the relationship between the saline soil salt content and its associated, environmental factors or human factors and visible-near infrared, thermal infrared, or microwave remote sensing data. In this paper, we took the Ebinur Lake in the northeast of Junggar Basin Xinjiang as the study area. Thermal infrared emissivity spectra unique characteristics were used to determine the degree of soil salinization. First, we used the platform of FTIR (Fourier Transform Infrared Spectrometer) temperature and emissivity separation processing software to separate temperature and emissivity in order to obtain the original soil emissivity spectral data. Then, the spectrum smoothing iterative method was used to separate the soil emissivity and thermometers for elimination of environmental and human-caused errors when collecting spectral emissivity data in order to get the real soil emissivity spectral information. After that, we used Gaussian filter smoothing method to filter noise spectral data. In addition, we mixed the pure soil with salt to achieve five different soil salt contents: 0.1, 2.3 ,9.7 , 26.22, 49.8 g/kg soil and a pure soil with no salt addition, and analyzed the thermal infrared emissivity spectral characteristics of them. The raw spectral data from them were de-noised by square root transformation, logarithmic transformation, the first derivative, and the second derivative. The four transformations were compared in their normalized ratio, and we determined the relationship between spectral data and soil salinity. We also used stepwise multiple regression equation to establish six different forms of forecasting models. By comparing the models of analysis, the establishment of the square root transformation had the highest prediction accuracy and R2 was greater than 0.82. By use of stepwise multiple regression model for each data set, the modeling results very stable, and test sample coefficient of determination R2=0.82, the root mean square error(RMS) is 0.92. Prediction model had performed very well, between the Ebinur basin of thermal emissivity spectra of soil salinizaiton in square root transformation and salt content has exist a function form. In this study, we discussed the hyperspectral remote sensing monitoring technology that can be used to predict the soil salt in Ebinur Lake basin, it provided the technical methods for the large-scale, low-cost and real-time monitoring the soil salinity. Such variation of emissivity method would promote the development and application of hyperspectral remote sensing technology monitoring on the space-time dynamic of arid land saline soil for future regional ecological restoration. |
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| Keywords: | soils salts spectrum analysis remote sensing salinization soil salinity thermal infrared emissivity spectra stepwise multiple regression |
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