利用多源光谱信息反演宁夏银北地区干湿季土壤含盐量  

Inversion of soil salinity in dry and wet seasons based on multi-source spectral data in Yinbei area of Ningxia,China

作　　者：贾萍萍 尚天浩 张俊华 孙媛[1,2,3] Jia Pingping;Shang Tianhao;Zhang Junhua;Sun Yuan(College of Resources and Environmental Science,Ningxia University,Yinchuan 750021,China;Institute of Environmental Engineering,Ningxia University,Yinchuan 750021,China;Ningxia Key Laboratory of Resource Assessment and Environment Regulation in Arid Region,Yinchuan 750021,China)

机构地区：[1]宁夏大学资源环境学院,银川750021 [2]宁夏大学环境工程研究院,银川750021 [3]宁夏旱区资源评价与环境调控重点实验室,银川750021

出　　处：《农业工程学报》2020年第17期125-134,共10页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(41561078);宁夏自然科学基金项目(2020AAC03113)。

摘　　要：土壤盐渍化是导致全球荒漠化和土壤退化的主要诱因之一。为确定高光谱和多光谱遥感反演干湿季土壤含盐量的最优模型,该研究以宁夏银北平罗县为例,以干季(4月)和湿季(10月)实测高光谱和Landsat 8 OLI多光谱以及干湿两季实测土壤含盐量为基础数据源,利用相关系数法、灰度关联法和逐步回归法筛选敏感光谱数据,分别采用偏最小二乘、支持向量机、岭回归、BP神经网络和地理加权回归建立干湿两季土壤含盐量反演模型。结果表明:1)银北地区土壤盐渍化较为严重,干湿季含盐量均表现为强度变异,且干季变异程度大于湿季;2)在不同土壤含盐量条件下,重采样后的高光谱波段反射率和影像波段反射率具有显著相关性;3)对比相关性分析、灰度关联和逐步回归三组变量筛选方法下各模型R2和RMSE,逐步回归组模型整体效果较好;4)5种土壤含盐量反演模型中地理加权回归模型精度较高,支持向量机算法和BP神经网络算法在基于不同变量组的模型中表现较为接近,岭回归表现最差,偏最小二乘回归模型出现了较严重的“过拟合”现象。局部模型在土壤含盐量反演方面更具优越性。干季以实测灰度关联组-地理加权回归模型效果最佳,其验证决定系数Rp 2和相对分析误差RPD分别为0.94和4.49;湿季以影像相关系数组-地理加权回归模型反演效果最好,其验证决定系数Rp 2和相对分析误差RPD分别为0.96和4.83。研究结果可为当地及同类地区土壤盐渍化的识别、防治提供理论依据。Soil salinization is one of the main causes of global desertification and soil degradation.Information data about salinity and alkalinity is essential to the treatment of alkalized soil for preventing its further degradation and sustainable development of agriculture.Soil salinization is often characterized with significant spatiotemporal dynamics.Taking the saline soil in Pingluo County as the research object,which is predominant in the Ningxia Yinbei area of Northwestern China,this study aims to explore the salt content of soil in dry and wet seasons,and then compare the accuracy of local models and global models,further to determine the optimal model for retrieving soil salinity using the hyperspectral and multispectral remote sensing.The specific processing is following,based on hyperspectral and Landsat 8 OLI image data in the dry season(April)and wet season(October),First,the hyperspectral data was resampled to the image band range for matching the two,whereas,the 11 salt indices under the two spectral data were calculated separately.Second,different algorithms including pearson correlation coefficient(PCC),stepwise regression(SR)and gray relational analysis(GRA)were applied for the sensitive band and index screening of the measured and image spectral data in the dry and wet seasons.Finally,the quantitative analysis models for soil salinity were established using the partial least squares regression(PLSR),support vector machine(SVM),ridge regression(RR),BP neural networks(BPNN),and geographically weighted regression(GWR)method,respectively.All these regression models were verified to select the optimal model,after comparing the effects of different input variables and different regression methods on the model precision.The results showed that:1)The soil of the Yinbei region was strongly salt-affected,and the salt content in the wet and dry season was characterized by the intensity variation,where the variation degree of dry season was much higher than that of the wet season.2)The resampling bands showed a g

关 键 词：遥感 反演 模型 土壤含盐量 光谱指数 GWR模型 

分 类 号：S127[农业科学—农业基础科学] TP79[自动化与计算机技术—检测技术与自动化装置]