基于高光谱指数的土壤盐渍化遥感监测研究——以平罗县为例
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摘要
为建立土壤盐渍化遥感监测模型,以宁夏平罗县为例,通过在野外测定高光谱数据,结合室内土壤样品化学分析结果,分析不同类型盐渍化土壤光谱特征,并对实测土壤光谱数据进行倒数、对数及其一阶微分等变换,确定响应土壤盐分质量分数和pH值的最优波段,最后通过回归分析构建土壤盐渍化监测模型。结果表明:不同类型盐渍化土壤光谱曲线在形态上基本趋于一致,光谱反射率在可见光范围内随波长增长而增大,在近红外波段,增长速度减缓;通过相关分析,确定对数一阶微分变换对应的385.7 nm和原始一阶微分变换对应的1 708.4 nm分别为土壤光谱反射率与土壤盐分质量分数和pH值的最佳特征波段;以高光谱盐分指数(SI2)为自变量,土壤盐分质量分数为因变量,利用二次多项式回归模型建立的预测模型为最优模型,该模型实测值和预测值间拟合系数(R2)为0.673,通过0.01显著性水平检验。
A remote sensing monitoring model for soil salinization is established in this paper, taking Pingluo county of Ningxia as the research area. Based on the hyperspectral data measured in the field and the chemical analysis of indoor soil samples, the spectral characteristics of different types of salinized soils are analyzed, and the reciprocal, logarithmic and first-order differential transformations of measured soil spectral data are carried out. The optimum wave band responding to soil salt content and pH value is determined. Finally, the monitoring model of soil salinization is constructed by regression analysis. The results show that the spectral curves of different types of salinized soils tend to be consistent in morphology,and the spectral reflectance increases with the increase of wavelength in the visible range. In the near infrared band, the growth rate slows down. By correlation analysis, it is determined that the 385.7 nm corresponding to logarithmic first differential transformation is the best characteristic band for soil spectral reflectance and for soil salt content, the 1 708.4 nm corresponding to the original first derivative is the best characteristic band for soil spectral reflectance and soil pH. With hyperspectral salinity index(SI2) as an independent variable and soil salt content as a dependent variable, the prediction model is the best one based on quadratic polynomial regression. The fitting coefficient(R2) between measured and predicted values of the model is 0.673, which is tested by the significant level of 0.01.
A remote sensing monitoring model for soil salinization is established in this paper, taking Pingluo county of Ningxia as the research area. Based on the hyperspectral data measured in the field and the chemical analysis of indoor soil samples, the spectral characteristics of different types of salinized soils are analyzed, and the reciprocal, logarithmic and first-order differential transformations of measured soil spectral data are carried out. The optimum wave band responding to soil salt content and pH value is determined. Finally, the monitoring model of soil salinization is constructed by regression analysis. The results show that the spectral curves of different types of salinized soils tend to be consistent in morphology,and the spectral reflectance increases with the increase of wavelength in the visible range. In the near infrared band, the growth rate slows down. By correlation analysis, it is determined that the 385.7 nm corresponding to logarithmic first differential transformation is the best characteristic band for soil spectral reflectance and for soil salt content, the 1 708.4 nm corresponding to the original first derivative is the best characteristic band for soil spectral reflectance and soil pH. With hyperspectral salinity index(SI2) as an independent variable and soil salt content as a dependent variable, the prediction model is the best one based on quadratic polynomial regression. The fitting coefficient(R2) between measured and predicted values of the model is 0.673, which is tested by the significant level of 0.01.
引文
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[3] CLARK R N,ROUSH T L.Reflectance spectroscopy:quantitative analysis techniques for remote sensing applications[J].Journal of Geophysical Research Solid Earth, 1984,89(B7):6329-6340.
[4] BEN-DOR E,PATKIN K,BANIN A,et al.Mapping of several soil properties using DAIS-7915 hyperspectral scanner data-a case study over clayey soils in Israel[J].International Journal of Remote Sensing,2002,23(6):1043-1062.
[5]关元秀,刘高焕,刘庆生,等.黄河三角洲盐碱地遥感调查研究[J].遥感学报,2001,5(1):46-52.
[6]屈永华,段小亮,高鸿永,等.内蒙古河套灌区土壤盐分光谱定量分析研究[J].光谱学与光谱分析,2009,29(5):1362-1366.
[7]王向辉.西北地区环境变迁与农业可持续发展研究[D].杨凌:西北农林科技大学,2012.
[8]温利强.我国盐渍土的成因及分布特征[D].合肥:合肥工业大学,2010.
[9]朱庭芸.灌区土壤盐渍化防治[M].北京:农业出版社,1992:32-38.
[10]董新光,周金龙,陈跃滨.干旱内陆区水盐监测与模型研究及其应用[M].北京:科学出版社,2007:1-27.
[11]李茂廷,孙海健,黄青锁,等.平罗县农用地现状、存在问题及对策[J].宁夏农林科技,2005(3):44-45.
[12]李聪敏,王彦兵.宁夏引黄灌区耕地土壤盐渍化现状及影响因素调查研究[J].地下水,2007(3):41-44.
[13]王遵亲,祝寿泉,俞仁培,等.中国盐渍土[M].北京:科学出版社,1993.
[14] SAVITZKY A,GOLAY M J E.Smoothing and differentiation of date by simplified least squares procedures[J].Analytical Chemistry,1964,36(8):1627-1639.
[15]杨凯,沈渭寿,刘波,等.那曲典型草地植被光谱特征分析[J].遥感技术与应用,2014,29(1):40-45.
[16]姚远,丁建丽,阿尔达克·克里木,等.基于实测高光谱和电磁感应数据的区域土壤盐渍化遥感监测研究[J].光谱学与光谱分析,2013,33(7):1917-1921.
[17] KHAN N M,RASTOSKUEV V V,SATO Y,et al.Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators[J].Agricultural Water Management,2005,77(1):96-109.
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