渭—库绿洲土壤电导率反演模型研究
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摘要
土壤盐渍化是干旱半干旱地区土地资源退化形式之一。经常发生在蒸发强度大,降水量少且地下水位高的地区。土壤盐渍化直接或间接地影响着人类生活和农业生产与发展。为了管理和准确评价盐渍化情况,定量描述盐渍化的程度与分布是非常重要的。但随着定量遥感研究的发展,定量提取盐渍化信息的要求也提高,像元尺度的数据不能满足需求。同时,国内外已有研究表明,盐渍化土壤遥感监测研究中,单一利用盐渍化土壤光谱特征是不能全面、准确地反映土壤盐渍化信息。因此有必要加以修改或发展图像识别算法,适应定量提取区域尺度盐渍化土壤信息的新要求。
本研究以混合像元提纯为突破口,减少了由于地物波普的复杂性,传感器空间分辨率的局限性所导致的混合像元普遍存在而引起的影像信息不确定性的问题。在此基础上,选择植被和土壤光谱特征作为盐渍化土壤的两个关键指标,利用光谱角制图模型(SAM)获取土壤盐渍化图并计算归一化差异植被指数(NDVI)。同时考虑土壤水分,利用土壤水分遥感监测模型(PDI)提取土壤水分信息。最后建立综合反应电导率,植被光谱响应指数与土壤水分之间的非线性回归模型。结果表明:SAM获取的土壤盐渍化图与野外实测的点尺度电导率数据扩展到区域尺度进行验证,相关性达到0.76,较好的表示地表土壤盐渍化程度。所建立的非线性回归模型中因变量与自变量的相关系数R=0.78,相应的显著值为0.001,表示结果在0.05的水平上是显著的。同时采用F检验法对回归方程进行显著性检验,F=15.384说明此模型有很好的拟合作用。
Soil salinization is one of the soil degradation form in arid and semi-arid areas and it always occurs in high evaporotion , low precipitation and high ground water table area. Soil salinization affect directly or indrectly human life, agricultural productivity and development. For management purposes, quentifying both the extent and distribution of salinization is extrimly important.But with development of quantitative Remote Sensing monitoring, requirement for the quantitative extraction of salinized information is getting stricted and pixel scale data did not satisfy our demand.Domestic and overseas study shows that, in soil salinization monitoring study, using single salinized soil spectrum can not reflect salinization informatiom correctly. Therefore, modifying or advancing the image identifying algorithm is new desire for the extraction of regional soil salinization.
This study starting with purification of mixed pixels to reduce the uncertainty of information as a result of mixed pixels widely contained in the Remote Sensing image because of the complexity of spectrum taken from the field and limit spatial resolution of sensor .Therefore, we used Spectral Angle Mapper (SAM) as a mapping method to generated soil electrical conductivity map and calculated Normalized Diffrence Vegetation Index(NDVI) togather with extracted the Soil Moisture Content (SM) through model of soil moisture monitoring by remote sensing (PDI).At last, we applied nonlinear regression model to identifying the relations among Electrical Conductivity(EC) ,Normalized Diffrence Vegetation Index(NDVI) and Soil Moisture Content(SM) .Result shows that, we used SAM to obtain soil salinization (EC) map and integrated with field data which consist of measurement of electrical conductivity (EC) are obtained by the combination of geophysical methods to validate the extended regional data. Accuracy is 0.7584, 0. 7388 respectivly and can preferably express the salinization degree.Correlation coefficient between dependent and indepentdent variables is 0.78, corresponding value is 0.001in nonlinear regression model that we used. Result refers that the function we gained is distinct on level0.05. At the same time ,we used F inspection method to inspect the distinctness of regression model, F=15.384 explains that this model has been collaborated very well.
本研究以混合像元提纯为突破口,减少了由于地物波普的复杂性,传感器空间分辨率的局限性所导致的混合像元普遍存在而引起的影像信息不确定性的问题。在此基础上,选择植被和土壤光谱特征作为盐渍化土壤的两个关键指标,利用光谱角制图模型(SAM)获取土壤盐渍化图并计算归一化差异植被指数(NDVI)。同时考虑土壤水分,利用土壤水分遥感监测模型(PDI)提取土壤水分信息。最后建立综合反应电导率,植被光谱响应指数与土壤水分之间的非线性回归模型。结果表明:SAM获取的土壤盐渍化图与野外实测的点尺度电导率数据扩展到区域尺度进行验证,相关性达到0.76,较好的表示地表土壤盐渍化程度。所建立的非线性回归模型中因变量与自变量的相关系数R=0.78,相应的显著值为0.001,表示结果在0.05的水平上是显著的。同时采用F检验法对回归方程进行显著性检验,F=15.384说明此模型有很好的拟合作用。
Soil salinization is one of the soil degradation form in arid and semi-arid areas and it always occurs in high evaporotion , low precipitation and high ground water table area. Soil salinization affect directly or indrectly human life, agricultural productivity and development. For management purposes, quentifying both the extent and distribution of salinization is extrimly important.But with development of quantitative Remote Sensing monitoring, requirement for the quantitative extraction of salinized information is getting stricted and pixel scale data did not satisfy our demand.Domestic and overseas study shows that, in soil salinization monitoring study, using single salinized soil spectrum can not reflect salinization informatiom correctly. Therefore, modifying or advancing the image identifying algorithm is new desire for the extraction of regional soil salinization.
This study starting with purification of mixed pixels to reduce the uncertainty of information as a result of mixed pixels widely contained in the Remote Sensing image because of the complexity of spectrum taken from the field and limit spatial resolution of sensor .Therefore, we used Spectral Angle Mapper (SAM) as a mapping method to generated soil electrical conductivity map and calculated Normalized Diffrence Vegetation Index(NDVI) togather with extracted the Soil Moisture Content (SM) through model of soil moisture monitoring by remote sensing (PDI).At last, we applied nonlinear regression model to identifying the relations among Electrical Conductivity(EC) ,Normalized Diffrence Vegetation Index(NDVI) and Soil Moisture Content(SM) .Result shows that, we used SAM to obtain soil salinization (EC) map and integrated with field data which consist of measurement of electrical conductivity (EC) are obtained by the combination of geophysical methods to validate the extended regional data. Accuracy is 0.7584, 0. 7388 respectivly and can preferably express the salinization degree.Correlation coefficient between dependent and indepentdent variables is 0.78, corresponding value is 0.001in nonlinear regression model that we used. Result refers that the function we gained is distinct on level0.05. At the same time ,we used F inspection method to inspect the distinctness of regression model, F=15.384 explains that this model has been collaborated very well.
引文
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