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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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量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。
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.
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.
引文
[1]Bayaer W,Shen Yanjun,Audengaowa A.Using remote sensing to evaluate land salinization in typicalareas of Inner Mongolia,China[C].2005 IEEE International,2005:2888-2890.
[2]Farifteh J,Farshad A,George R J.Assessing salt-affected soils using remote sensing,solute modelling,and geophysics[J].Geoderma,2006,130(34):191-206.
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[4]Wang Yongling,Gong P.Spectral index for estimating soil salinity in the Yellow River delta region of China using EO-1hyperion data[J].Pedosphere,2010,20(3):378-388.
[5]程杰,柳钦火,李小文,等.基于相关性的中热红外温度与发射率分离算法[J].中国科学,2008,38(2):261-272.Cheng Jie,Liu Qinhuo,Li Xiaowen,et al.The corrrlation based mid-infrared temperature and emissivity separation algorithm[J].Science in China,2008,38(2):261-272.(in Chinese with English abstract)
[6]肖青,柳钦火,李小文,等.热红外发射率光谱光谱的野外测量方法与土壤热红外发射率光谱特性研究[J].红外与毫米波学报,2003,22(5):373-378.Xiao Qing,Liu Qinhuo,Li Xiaowen,et al.A field measurement method of spectral emissivity and research on the feature of soil thermal infrared emissivity[J].Journal of Infrared and Millimeter Waves,2003,22(5):373-378.(in Chinese with English abstract)
[7]夏军,塔西甫拉提·特依拜,买买提·沙吾提,等.热红外发射率光谱光谱在盐渍化土壤含盐量估算中的应用研究[J].光谱学与光谱分析,2012,32(12):2956-2961.Xia Jun,Tashpolat·Tiyip,Mamat·Saut,et al.Application study of the thermal infrared emissivity spectra in the estimation of salt content of saline soil[J].Spectroscopy and Spectral Analysis,2012,32(12):2956-2961.(in Chinese with English abstract)
[8]雷磊,塔西甫拉提·特依拜,丁建丽,等.基于HJ-1A高光谱影像的盐渍化土壤信息提取——以渭干河-库车河绿洲为例[J].中国沙漠,2013,33(4):1104-1109.Lei Lei,Tashpolat·Tiyip,Ding Jianli,et al.Soil salinization information extraction by using hyperspectral data of HJ-1A HIS:A case study in the oasis of Ugan&Kuqa,Xinjiang,China[J].Journal of Desert Research,2013,33(4):1104-1109.
[9]吉力力·阿不都外力,米热班·阿布里米提,刘东伟,等.艾比湖干涸湖底不同景观类型下富盐沉积物盐分积聚特征[J].中国沙漠,2013,33(5):1426-1432.Abuduwaili Jilili,Abulimiti Mireban,Liu Dongwei,et al.Characteristics of salt accumulation in salt-rich sediments under different landscapes in the Ebinur lake playa,Xinjiang,China[J].Journal of Desert Research,2013,3(5):1426-1432.(in Chinese with English abstract)
[10]何学敏,吕光辉,秦璐,等.艾比湖荒漠-湿地生态系统非生长季碳通量数据特征[J].生态学报,2014,34(22):6655-6665.He Xueming,LüGuanghui,Qin Lu,et al.Research on data characteristics during non-growing season of desert-wetland ecosystem in Ebinur Lake[J].Acta Ecologica Sinica,2014,34(22):6655-6665.(in Chinese with English abstract)
[11]Christoph C B.Iterative retrieval of surface emissivity and temperature for a hyperspectral sonsor[C]//First JPL Workshop on Remote Sensing of Land Surface Emissivity,JPL,1997:6-8.
[12]Hook S J,Kahle A B.The micro Fourier transform interferometer(μFTIR):A new field spectrometer for acquisition of infrared data of natural surfaces[J].Remote Sensing of Environment,1996,56(3):172-181.
[13]Ingram P M,Muse A H.Sensitivity of iterative spectrally smooth temperature-emissivity separation to algorithmic assumptions and measurement noise[J].IEEE Transactions on GEO Science and Remote Sensing,2001,39(10):2158-2167.
[14]夏军.准东煤田土壤重金属污染高光谱遥感监测研究[D].乌鲁木齐:新疆大学,2014.Xia Jun.Study on the Monitoring of Soil Heavy Metal Pollution with Hyperspectral Remote Sensing in the Eastern Junggar Coalfield[D].Urumqi:Xinjiang University,2014.(in Chinese with English abstract)
[15]张严俊.干旱区盐渍化土壤热红外发射率光谱特性研究[D].乌鲁木齐:新疆大学,2014.Zhang Yanjun.Study of the Thermal Infrared Emissivity Spectra in the Soil Salinization in Arid Land[D].Urumqi:Xinjiang University,2014.(in Chinese with English abstract)
[16]French A N,Schmugge T J,Kustas W P.Estimating surface fluxes over the SGP site with remotely sensed data[J].Physics and Chemistry of the Earth,2000,25(2):167–172.
[17]Song Xiaoning,Ma Jianwei,Li Zhaoliang,et al.Estimation of vegetation canopy water content using Hyperion hyperspectral data[J].Pub Med Journal,2014,33(10):2833-2837.
[18]Mamat Sawut,Abduwasit Ghulham,Tashpolat Yiyip,et al.Estimating soil sand content using thermal infrared spectra in arid lands[J].International Journal of Applied Earth Observation and Geoinformation,2014,33:203-210.
[19]侯艳军,塔西甫拉提·特依拜,张飞,等.荒漠土壤全磷含量热红外发射率光谱估算研究[J].光谱学与光谱分析,2015,35(2):350-354.Hou Yanjun,Tashpolat·Tiyip,Zhang Fei,Study on estimation of deserts soil total phosphorus content from thermal-infrared emissivity[J].Spectroscopy and Spectral Analysis 2015,35(2):350-354.(in Chinese with English abstract)
[20]王珲,盘毅,李华,等.基于快速傅里叶红外光谱仪的自然地物光谱发射率测量[J].红外技术,2009,31(4):210-214.Wang Hui,Pan Yi,Li Hua,et al.Measuring spectral emissivity of natural objects with FTIR[J].Infrared Technology,2009,31(4):210-214.(in Chinese with English abstract)
[2]Farifteh J,Farshad A,George R J.Assessing salt-affected soils using remote sensing,solute modelling,and geophysics[J].Geoderma,2006,130(34):191-206.
[3]Leone A P,Menenti M,Buondonno A,et al.A field experiment on spectrometry of crop response to soil salinity[J].Agricultural Water Management,2007,89(12):39-48.
[4]Wang Yongling,Gong P.Spectral index for estimating soil salinity in the Yellow River delta region of China using EO-1hyperion data[J].Pedosphere,2010,20(3):378-388.
[5]程杰,柳钦火,李小文,等.基于相关性的中热红外温度与发射率分离算法[J].中国科学,2008,38(2):261-272.Cheng Jie,Liu Qinhuo,Li Xiaowen,et al.The corrrlation based mid-infrared temperature and emissivity separation algorithm[J].Science in China,2008,38(2):261-272.(in Chinese with English abstract)
[6]肖青,柳钦火,李小文,等.热红外发射率光谱光谱的野外测量方法与土壤热红外发射率光谱特性研究[J].红外与毫米波学报,2003,22(5):373-378.Xiao Qing,Liu Qinhuo,Li Xiaowen,et al.A field measurement method of spectral emissivity and research on the feature of soil thermal infrared emissivity[J].Journal of Infrared and Millimeter Waves,2003,22(5):373-378.(in Chinese with English abstract)
[7]夏军,塔西甫拉提·特依拜,买买提·沙吾提,等.热红外发射率光谱光谱在盐渍化土壤含盐量估算中的应用研究[J].光谱学与光谱分析,2012,32(12):2956-2961.Xia Jun,Tashpolat·Tiyip,Mamat·Saut,et al.Application study of the thermal infrared emissivity spectra in the estimation of salt content of saline soil[J].Spectroscopy and Spectral Analysis,2012,32(12):2956-2961.(in Chinese with English abstract)
[8]雷磊,塔西甫拉提·特依拜,丁建丽,等.基于HJ-1A高光谱影像的盐渍化土壤信息提取——以渭干河-库车河绿洲为例[J].中国沙漠,2013,33(4):1104-1109.Lei Lei,Tashpolat·Tiyip,Ding Jianli,et al.Soil salinization information extraction by using hyperspectral data of HJ-1A HIS:A case study in the oasis of Ugan&Kuqa,Xinjiang,China[J].Journal of Desert Research,2013,33(4):1104-1109.
[9]吉力力·阿不都外力,米热班·阿布里米提,刘东伟,等.艾比湖干涸湖底不同景观类型下富盐沉积物盐分积聚特征[J].中国沙漠,2013,33(5):1426-1432.Abuduwaili Jilili,Abulimiti Mireban,Liu Dongwei,et al.Characteristics of salt accumulation in salt-rich sediments under different landscapes in the Ebinur lake playa,Xinjiang,China[J].Journal of Desert Research,2013,3(5):1426-1432.(in Chinese with English abstract)
[10]何学敏,吕光辉,秦璐,等.艾比湖荒漠-湿地生态系统非生长季碳通量数据特征[J].生态学报,2014,34(22):6655-6665.He Xueming,LüGuanghui,Qin Lu,et al.Research on data characteristics during non-growing season of desert-wetland ecosystem in Ebinur Lake[J].Acta Ecologica Sinica,2014,34(22):6655-6665.(in Chinese with English abstract)
[11]Christoph C B.Iterative retrieval of surface emissivity and temperature for a hyperspectral sonsor[C]//First JPL Workshop on Remote Sensing of Land Surface Emissivity,JPL,1997:6-8.
[12]Hook S J,Kahle A B.The micro Fourier transform interferometer(μFTIR):A new field spectrometer for acquisition of infrared data of natural surfaces[J].Remote Sensing of Environment,1996,56(3):172-181.
[13]Ingram P M,Muse A H.Sensitivity of iterative spectrally smooth temperature-emissivity separation to algorithmic assumptions and measurement noise[J].IEEE Transactions on GEO Science and Remote Sensing,2001,39(10):2158-2167.
[14]夏军.准东煤田土壤重金属污染高光谱遥感监测研究[D].乌鲁木齐:新疆大学,2014.Xia Jun.Study on the Monitoring of Soil Heavy Metal Pollution with Hyperspectral Remote Sensing in the Eastern Junggar Coalfield[D].Urumqi:Xinjiang University,2014.(in Chinese with English abstract)
[15]张严俊.干旱区盐渍化土壤热红外发射率光谱特性研究[D].乌鲁木齐:新疆大学,2014.Zhang Yanjun.Study of the Thermal Infrared Emissivity Spectra in the Soil Salinization in Arid Land[D].Urumqi:Xinjiang University,2014.(in Chinese with English abstract)
[16]French A N,Schmugge T J,Kustas W P.Estimating surface fluxes over the SGP site with remotely sensed data[J].Physics and Chemistry of the Earth,2000,25(2):167–172.
[17]Song Xiaoning,Ma Jianwei,Li Zhaoliang,et al.Estimation of vegetation canopy water content using Hyperion hyperspectral data[J].Pub Med Journal,2014,33(10):2833-2837.
[18]Mamat Sawut,Abduwasit Ghulham,Tashpolat Yiyip,et al.Estimating soil sand content using thermal infrared spectra in arid lands[J].International Journal of Applied Earth Observation and Geoinformation,2014,33:203-210.
[19]侯艳军,塔西甫拉提·特依拜,张飞,等.荒漠土壤全磷含量热红外发射率光谱估算研究[J].光谱学与光谱分析,2015,35(2):350-354.Hou Yanjun,Tashpolat·Tiyip,Zhang Fei,Study on estimation of deserts soil total phosphorus content from thermal-infrared emissivity[J].Spectroscopy and Spectral Analysis 2015,35(2):350-354.(in Chinese with English abstract)
[20]王珲,盘毅,李华,等.基于快速傅里叶红外光谱仪的自然地物光谱发射率测量[J].红外技术,2009,31(4):210-214.Wang Hui,Pan Yi,Li Hua,et al.Measuring spectral emissivity of natural objects with FTIR[J].Infrared Technology,2009,31(4):210-214.(in Chinese with English abstract)