典型龟裂碱土土壤水分光谱特征及预测

以不同含水量的宁夏典型龟裂碱土为研究对象,系统分析了土壤光谱与土壤含水量的相关性,并建立了含水量预测模型.结果表明:随着含水量的增加,土壤光谱反射率逐渐降低,当土壤含水量高于田间持水量时,土壤光谱反射率随着含水量的增加呈增加趋势.土壤光谱反射率原始数据(r)、平滑后的反射率(R)和反射率对数(lgR)与龟裂碱土水分含量呈极显著负相关关系,整个波段R与土壤水分含量的相关系数平均比r和lgR分别高0.0013和0.0397;反射率倒数(1/R)和反射率倒数的对数[lg(1/R)]2种变换形式与龟裂碱土水分含量呈正相关关系,在950~1000 nm的相关系数平均比400~950 nm高0.2350;3种一阶微分变换形式与土壤水分的相关性不稳定.基于r、lg(1/R)、反射率的一阶微分R’和反射率对数的一阶微分(lgR)’采用不同回归模式建立的龟裂碱土含水量预测模型平均决定系数分别为0.7610、0.8184、0.8524和0.8255,其中R’的幂函数模式决定系数高达0.9447,该模型预测的土壤含水量与室内实测值拟合度为0.8279,说明该模型预测精度最高,采用r建立的模型预测精度最低.研究结果可为龟裂碱土含水量预测和当地农田灌溉提供科学依据.     

基于分数阶微分优化光谱指数的土壤电导率高光谱估算

土壤电导率与含盐量具有高度相关性,精准的土壤电导率监测有助于了解区域土壤的盐渍化程度,对区域盐渍化防治与调控,农业可持续发展以及生态文明建设具有重要意义。为寻求预测土壤电导率的最佳高光谱参数,实现土壤盐分信息的高效监测,本研究对土壤样品进行室内高光谱和电导率测定,利用两波段优化算法对简化光谱指数(nitrogen planar domain index, NPDI)进行波段优化,筛选不同高光谱数据(原始高光谱反射率及其对应的5种数学变换)运算下的最敏感高光谱参数,从而建立土壤电导率高光谱估算模型。结果表明:1)NPDIs与土壤电导率之间的相关性显著,在原数据及其平方根、倒数、对数倒数、1.6阶微分变换形式下,优化光谱指数对土壤电导率的敏感程度更强,相关系数绝对值均超过0.80,且基于1.6阶微分变换的(R_(2020nm)+R_(1893 nm))/R_(1893 nm)波段组合相关系数绝对值最高,达到0.888。2)基于1.6阶微分波段优化的预测模型效果最佳,预测精度为R■=0.84,RMSE_(Pre)=2.07mS/cm,RPD=2.94,AIC=158.11。因此,对高光谱数据的适当数学变换有利于优化光谱指数更好地估算土壤电导率,进一步实现土壤盐渍化高精度动态监测。     

基于分数阶微分技术的土壤水盐信息高光谱反演

准确高效获取土壤水盐信息是盐碱地改良和可持续利用的前提。本研究以地面野外高光谱反射率和实测土壤水盐含量为数据源,利用分数阶微分(FOD)技术对原始光谱反射率进行步长为0.25的处理,从光谱数据与土壤水盐信息相关性层面筛选FOD阶数,构建二维光谱指数,采用支持向量机回归(SVR)和地理加权回归(GWR)建立土壤水盐含量反演模型并进行验证。结果表明：FOD技术可以在一定程度上减弱高光谱噪声并挖掘潜在光谱信息,提高高光谱反射率与土壤含水量(SMC)、pH值和含盐量的相关性,相关系数最高分别提升0.98、1.35和0.33。与一维光谱相比,FOD结合二维光谱指数筛选的特征波段组合对SMC、pH值和含盐量的响应更敏感,分别以1.5、1.0和0.75阶为最优,其中,SMC最大相关系数绝对值的最佳组合波段为570、1000、1010、1020、1330和2140 nm; pH值为550、1000、1380和2180 nm;含盐量为600、990、1600和1710 nm。相较于原始光谱反射率,SMC、pH值和含盐量最优阶次估算模型验证决定系数(R_p²)最高...     

湿地小叶章叶绿素含量的高光谱遥感估算模型

通过实测不同覆盖度和水深状况下小叶章(Calamagroestis angustifolia)的冠层高光谱反射率与叶绿素a(Chl-a)浓度,采用高光谱可见光-近红外波段及其微分光谱波段(350～1050 nm)逐波段构建FNDVI、FRVI、FDVI、FDNDVI、FDRVI和FDDVI植被指数,分别找出与Chl-a具有最佳相关性波段组合的植被指数,建立小叶章Chl-a含量的最佳估算模型,并对比分析了最佳模型与线性模型的预测精度.结果显示:微分光谱植被指数与Chl-a的最佳预测模型(FDNDVI、FDRVI和FDDVI)比反射率植被指数最佳模型(FNDVI、FRVI和FDVI)的预测精度分别提高了6.86%、4.82%和10.10%;植被指数(FNDVI、FD-VI、FDNDVI和FDRVI)与Chl-a含量具有较好的线性关系,而最佳模型比线性模型预测精度仅仅提高了0.60%、1.40%、1.02%和0.93%,可以用简单的线性模型反演湿地小叶章的Chl-a含量.     

不同质地盐渍化土壤水盐含量的高光谱反演

为了方便快捷地同步监测盐渍化土壤的水、盐含量,本文以新疆典型盐渍化灌区为研究对象,基于高光谱技术、运用便携式光谱仪获取不同质地的土壤水盐含量光谱曲线,采用一阶微分、二阶微分、连续统去除的数据处理方法对土壤原始光谱进行变换.结果表明: 对原始光谱数据的变换有利于土壤属性指纹波段的提取,不同质地水盐含量的变换方法并不相同,在壤土中质量含水量为0%和10%时的水盐光谱曲线使用连续统去除方法、15%含水量使用一阶微分、19%含水量使用二阶微分,砂土中0%含水量使用连续统去除方法、10%、15%和19%含水量水盐光谱曲线使用二阶微分处理后,有利于特征波段的提取;对筛选出的变换数据采用偏最小二乘回归方法构建水盐反演模型,壤土盐度小于6.38 mS·cm^-1、砂土小于5.94 mS·cm^-1时,模型建立的建模数据集决定系数(R_cal²)、内部交叉验证(R_cv²)和外部检验数据集决定系数(R_val²)均大于0.65(P<0.05);壤土水分含量小于16%、砂土小于12%时模型反演精度较高.研究结果可为盐渍化土壤水盐含量同步监测提供阈值参考.     

宁夏中宁枸杞林地粉壤土阳离子交换量空间分布预测方法对比

土壤阳离子交换量(CEC)是土壤施肥、改良的主要依据和土壤质量的评价指标,研究土壤CEC的空间分布及模型预测可为土壤养分监测、管理及精准农业实施提供科学依据。本研究以中宁枸杞林地粉壤土为对象,在自相关、交互相关等分析基础上,采用协同克里格(CoKriging)、普通最小二乘法(OLS)、地理加权回归(GWR)和随机森林(RF)模型对土壤CEC进行回归分析,比较了制图效果及模型预测精度。结果表明:中宁枸杞林地粉壤土CEC平均值为13.12 cmol·kg~(-1),属中等肥力;土壤CEC的空间分布具有自相关性,并与土壤pH、有机质、黏粒和电导率在不同滞后距离上存在不同的空间相互关系; RF模型预测图避免了CoKriging、OLS和GWR模型预测图中土壤CEC图斑边界两侧破碎程度大、突变明显的缺陷,使土壤CEC在空间变化上表现为自然、平缓的过渡; RF模型RMSE值分别比CoKriging、OLS和GWR模型减少33.82%、20.55%和19.81%,R~2分别提高8.84%、51.92%和7.69%。RF模型考虑了样点空间位置,明显提高了插值精度且制图效果更加平缓。     

基于高光谱指数的塔里木河上游胡杨和灰叶胡杨叶片氮素含量估测

采用高光谱技术,以胡杨、灰叶胡杨为实验材料,利用野外地物光谱辐射计获得了塔里木河上游胡杨、灰叶胡杨叶片的光谱反射率数据,同时进行叶片的采集并分析其氮素含量。结果表明:(1)以原始光谱反射率与叶片氮素含量的最大相关波段处的反射率为自变量,氮素含量为因变量,拟合的线性模型灰叶胡杨的决定系数大于胡杨,胡杨在657 nm处和灰叶胡杨689 nm处所构建的模型在叶片氮素预测模型中较为理想;(2)以光谱特征变量、植被指数及红边位置为自变量,叶片氮素含量为因变量所构建的线性模型拟合效果较好,其中叶片氮素与植被指数构建的模型其决定系数值较高,说明其具有较高的预测能力;(3)基于光谱面积的预测变量在监测叶片营养状况中也有较大的潜力。研究认为,利用高光谱反射率数据及其光谱变换参数来估测胡杨、灰叶胡杨的氮素含量是可行的。     

闽江口湿地土壤全氮含量的高光谱遥感估算

氮是湿地生态系统重要生源要素,基于高光谱(350~2500 nm)遥感数据对其进行估算以实现湿地土壤全氮(TN)含量无损、快速和准确定量化具有重要意义。选取闽江河口湿地为研究区,于2013年5月,沿潮滩(高潮滩到中潮滩)采集16个土壤剖面80个样本,室内测定其光谱反射率和TN含量,并基于原始反射率(R)和光谱指数(比值指数RI、归一化指数NDI和差值指数DI)建立土壤TN含量高光谱估算模型,并进一步分析反射光谱与铵态氮(NH_4~+-N)、硝态氮(NO_3~--N)、有机质(SOM)和电导率(EC)之间的关系,以期揭示河口湿地土壤TN含量估算的机理。结果表明:土壤光谱反射率在350~600 nm,表现为高潮滩中潮滩,而在600~2500 nm,表现为高潮滩中潮滩;闽江河口湿地土壤TN含量与R在500 nm附近相关关系较好,并在490 nm有最大相关系数(-0.508);RI、NDI和DI大大提高了反射光谱与土壤TN含量的相关关系,其相关系数较高区域集中在600~1000 nm的波段组合,以RI(590,640)、RI(610,940)、NDI(940,590)、NDI(940,610)、DI(640,920)和DI(640,940)相关关系表现较好,能较好地实现研究区湿地土壤TN含量反演,其估算与检验模型r~2均大于0.610,RMSE均小于0.208,其中以RI(610,940)估算精度最好,估算与检验模型r~2分别为0.832和0.631,RMSE分别为0.178和0.202;闽江口湿地土壤TN含量与SOM含量密切相关是土壤TN含量估算的重要机理,而NH_4~+-N、NO_3~--N和盐分含量对其估算精度影响不大。     

基于DWT-GA-PLS的土壤碱解氮含量高光谱估测方法

以山东齐河县为研究区,实地采集土壤样本,在土样高光谱测试并进行一阶导数变换的基础上,先运用离散小波变换(DWT)对土壤光谱去噪降维,然后采用遗传算法(GA)筛选土壤碱解氮定量估测模型的参与变量,最后应用偏最小二乘(PLS)回归构建土壤碱解氮含量的估测模型.结果表明: 离散小波变换结合遗传算法和偏最小二乘法(DWT-GA-PLS)用于土壤碱解氮含量定量估测,不仅可压缩光谱变量、减少模型参与变量,而且可改善模型估测准确度;较之于采用土壤全谱,小波离散分解1～2层低频系数构建的模型在参与变量大幅减少的情况下,取得更准确或与之相当的预测结果,其中,基于第2层小波低频系数采用GA筛选变量构建的PLS模型的预测效果表现最好,预测R²达到0.85,RMSE为8.11 mg·kg^-1,RPD为2.53.说明DWT-GA-PLS用于土壤碱解氮含量高光谱定量估测的有效性.     

一种估测土壤有机质含量的近红外光谱参数

通过系统分析我国中、东部地区5种不同类型土壤风干样本的有机质含量与近红外(1000～2500 nm)光谱反射率和一阶导数两波段构成的比值、差值及归一化指数之间的关系,构建了适合土壤有机质含量估测的光谱参数及定量反演模型.结果表明：用多元散射校正及Savitzky-Golay平滑方法对原始光谱反射率进行预处理后,其两波段组成的光谱参数与土壤有机质含量的相关性明显优于原始光谱反射率组成的光谱参数,而由上述预处理后的反射率一阶导数的两波段构成的光谱参数介于二者之间;不同类型光谱参数构成形式中,以差值指数的预测性最好,其次为比值和归一化指数;与土壤有机质含量相关程度最高的光谱参数,是以近红外合频区1883和2065 nm 2个波段的反射率经多元散射校正和Savitzky-Golay平滑后构建而成的差值指数DI(CR₁₈₈₃, CR₂₀₆₅),两者呈极显著的直线相关.经不同类型土壤的观测资料检验,模型的决定系数为0.837,均方根误差为4.06;与偏最小二乘法的全谱建模结果相比,尽管DI(CR₁₈₈₃, CR₂₀₆₅)的预测精度略低于后者,但该指数只使用了2个波段的反射率,且所建模型比较简单,能为便携式监测仪的研制提供更有效的信息,可作为一种良好的土壤有机质估测光谱参数.     

基于地理加权回归模型探究环境异质性对秦岭大熊猫空间利用的影响

环境异质性对野生动物分布的影响具有明显的空间不均匀性。传统分析中多采用经典线性回归模型来量化野生动物分布与环境变量之间的关系,难以准确反映物种-环境关系的空间异质特征。地理加权回归(GWR)是近年来提出的一种新的空间分析方法,通过将空间结构嵌入线性回归模型中,以此来探测空间关系的非均匀性。以秦岭大熊猫为例,应用GWR模型分析大熊猫空间分布与环境异质性特征之间的潜在关系,并同经典的全局最小二乘回归法(OLS)进行比较。结果表明,GWR模型的AIC、R~2和校正R~2均显著优于OLS模型,GWR模型的局部回归系数估计能够更加深刻地揭示大熊猫空间分布与环境变量间的复杂空间关系,且GWR模型能够为物种的科学保护提供更加有效的理论支撑。因此,GWR模型可为探究物种-环境关系的空间异质特征提供一种新的方法,在物种栖息地选择与利用研究中具有一定的应用前景。     

Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems

Aim The objective of this paper is to obtain a net primary production (NPP) regression model based on the geographically weighted regression (GWR) method, which includes spatial non‐stationarity in the parameters estimated for forest ecosystems in China. Location We used data across China. Methods We examine the relationships between NPP of Chinese forest ecosystems and environmental variables, specifically altitude, temperature, precipitation and time‐integrated normalized difference vegetation index (TINDVI) based on the ordinary least squares (OLS) regression, the spatial lag model and GWR methods. Results The GWR method made significantly better predictions of NPP in simulations than did OLS, as indicated both by corrected Akaike Information Criterion (AIC_c) and R². GWR provided a value of 4891 for AIC_c and 0.66 for R², compared with 5036 and 0.58, respectively, by OLS. GWR has the potential to reveal local patterns in the spatial distribution of a parameter, which would be ignored by the OLS approach. Furthermore, OLS may provide a false general relationship between spatially non‐stationary variables. Spatial autocorrelation violates a basic assumption of the OLS method. The spatial lag model with the consideration of spatial autocorrelation had improved performance in the NPP simulation as compared with OLS (5001 for AIC_c and 0.60 for R²), but it was still not as good as that via the GWR method. Moreover, statistically significant positive spatial autocorrelation remained in the NPP residuals with the spatial lag model at small spatial scales, while no positive spatial autocorrelation across spatial scales can be found in the GWR residuals. Conclusions We conclude that the regression analysis for Chinese forest NPP with respect to environmental factors and based alternatively on OLS, the spatial lag model, and GWR methods indicated that there was a significant improvement in model performance of GWR over OLS and the spatial lag model.     

宁夏中宁枸杞林地粉壤土阳离子交换量空间分布预测方法对比

土壤阳离子交换量(CEC)是土壤施肥、改良的主要依据和土壤质量的评价指标,研究土壤CEC的空间分布及模型预测可为土壤养分监测、管理及精准农业实施提供科学依据。本研究以中宁枸杞林地粉壤土为对象,在自相关、交互相关等分析基础上,采用协同克里格(CoKriging)、普通最小二乘法(OLS)、地理加权回归(GWR)和随机森林(RF)模型对土壤CEC进行回归分析,比较了制图效果及模型预测精度。结果表明:中宁枸杞林地粉壤土CEC平均值为13.12 cmol·kg^-1,属中等肥力;土壤CEC的空间分布具有自相关性,并与土壤pH、有机质、黏粒和电导率在不同滞后距离上存在不同的空间相互关系;RF模型预测图避免了CoKriging、OLS和GWR模型预测图中土壤CEC图斑边界两侧破碎程度大、突变明显的缺陷,使土壤CEC在空间变化上表现为自然、平缓的过渡;RF模型RMSE值分别比CoKriging、OLS和GWR模型减少33.82%、20.55%和19.81%,R^2分别提高8.84%、51.92%和7.69%。RF模型考虑了样点空间位置,明显提高了插值精度且制图效果更加平缓。     

基于不同空间模型的马尾松林生态系统碳密度研究

以江西省马尾松林生态系统为研究对象,基于样地调查及样品碳含量测定结果计算其碳密度,并选取立地、植被及气象等方面的15个因子,采用多元线性逐步回归方法筛选出对生态系统碳密度影响显著的因子,然后分别利用最小二乘模型(OLS)、空间误差模型(SEM)、空间滞后模型(SLM)和地理加权回归模型(GWR)构建生态系统碳密度与其影响因子之间的关系模型,筛选出最优的拟合模型。结果表明:对马尾松林生态系统碳密度影响显著的因子分别为海拔、坡度、土层厚度、胸径、年均温度和年均降水量。4种模型拟合结果均显示碳密度与坡度呈负相关,与海拔、土层厚度、胸径呈正相关。模型的决定系数(R~2)由大到小分别为GWR(0.8043)SEM(0.6371)SLM(0.6364)OLS(0.6321),模型均方误差(MSE)与赤池信息准则(AIC)最大的均为OLS模型,最小的均为GWR模型;残差检验表明GWR模型能有效降低模型残差的空间自相关性。综合分析得出GWR模型的拟合效果最优,更适用于江西省马尾松林生态系统碳密度的估测。     

Spatial pattern of non-stationarity and scale-dependent relationships between NDVI and climatic factors—A case study in Qinghai-Tibet Plateau,China

Spatial non-stationarity and scale-dependence are important characteristics of the relationship between NDVI and climatic factors. To improve the reliability of model prediction, it is necessary to find the scales and spatial heterogeneity in which a stationary relationship is reached. In this paper, a geographically weighted regression (GWR) model was developed to define spatial non-stationarity and scale-dependent relationships between NDVI and climatic factors. The results indicate that the spatial scale of the stationary relationship for NDVI and both temperature and precipitation is 156 km over the whole Qinghai-Tibet Plateau. Both modeling performance and the spatial pattern of the GWR model are significantly better than global regression models such as OLS. Significant spatial heterogeneity of regression relationships between NDVI and climatic factors is revealed within the Qinghai-Tibet Plateau. We conclude that the dominant climatic factor influencing NDVI is not the same for all ecoregions within the study area. There are also different key scales of interaction between NDVI and the dominant climatic factor in these various ecoregions. Finally, model performance is different in the each eco-region. Therefore, this finding can provide a scientific basis for choosing a suitable scale and reliable models to solve scale-dependent problems in geography and ecology.     

黄土丘陵沟壑区土壤养分含量的高光谱预测模型

基于高光谱数据快速准确估算土壤养分含量,可为土壤养分监测及土壤理化参数反演提供优化方法.本研究在陕北黄土丘陵沟壑区选取典型样地,分析土壤养分含量与光谱反射率的定量关系,采用连续投影算法提取其光谱特征波长,利用偏最小二乘法、多元线性回归法、支持向量机法分别对土壤有机质、全氮、全磷、全钾含量进行预测并对比分析,构建该区域土壤养分含量的最优高光谱预测模型.结果表明: 黄土丘陵沟壑区土壤养分含量与光谱反射率在可见光区(400～760 nm)和近红外区(760～1100 nm)相关性较高,相关系数最大值均位于这两个光谱区间.4种土壤养分含量的SPA-SVM模型的普适性好且反演精度高,建模过程简单高效,适用于小数据量试验.本研究结果可为采用机器学习算法构建黄土丘陵沟壑区土壤养分含量高光谱预测模型提供参考.     

基于带宽优选地理加权回归模型的深圳市植被碳储量反演

植被碳储量估测是自然资源监测的重要内容,遥感技术结合地面样地进行反演可以获得区域范围内植被碳储量的空间连续分布,弥补了传统人工抽样调查估测的不足。然而,现有的参数和非参数遥感估测模型大多忽略了样地数据的变异与空间自相关关系。研究以Landsat 8 OLI影像为数据源提取遥感变量,结合植被碳储量实测调查数据,利用最小信息准则(AICc)、最大空间自相关距离(MSAD)和交叉验证(CV)分别确定最优带宽,组合Gaussian、Bi-square和Exponential核函数构建地理加权回归(GWR)模型估算深圳市植被碳储量,并与多元线性回归(MLR)进行比较,选择最优模型绘制深圳市植被碳储量空间分布图。研究结果表明,GWR模型整体精度优于MLR模型,GWR模型的决定系数(R~2)均高于MLR模型,且均方根误差(RMSE)和平均绝对误差(MAE)显著降低。带宽和核函数的选择对GWR模型估测结果产生了显著影响。以CV确定带宽、Exponential为核函数组合构建的GWR模型效果最佳,其R~2为0.697,RMSE为10.437 Mg C/hm~2,相比其它模型精度上升了13.87%—32....     

Comparison of the use of Landsat 8, Sentinel-2, and Gaofen-2 images for mapping soil pH in Dehui,northeastern China

Mapping the distribution and quantity of soil properties is important for black soil protection, management, and restoration in northeastern China. The objective of this study was to evaluate the effect of the spatial resolution on soil pH mapping using satellite images of the black soil region in northeastern China. A high spatial resolution Gaofen (GF)-2 high-definition image and multispectral images acquired by the Landsat 8 operational land imager and Sentinel-2 multi-spectral instrument were used to compare their performance in soil pH prediction. The spectral variables, including the original bands of the three satellite images and a variety of spectral indices derived from the original bands, were employed. Then, a machine learning model (quantile regression forest) was used to determine the relationships between the spectral variables and the measured soil pH, and prediction models were established to estimate the soil pH and to characterize the spatial pattern of the soil pH. The results revealed that the soil pH prediction model based on the GF-2 image had a slightly higher prediction accuracy than the models constructed using the Landsat 8 and Sentinel-2 images. The prediction models for Landsat 8, Sentinel-2, and GF-2 had root mean square errors of 0.34, 0.39, and 0.31, respectively. The use of remote sensing images with a high spatial resolution may not substantially increase the prediction accuracy of soil pH mapping compared with the results derived from medium-resolution images.     

Extending geographically and temporally weighted regression to account for both spatiotemporal heterogeneity and seasonal variations in coastal seas

Space-time modelling has been successfully applied in numerous research projects and has been studied extensively in the field of geographical information science. However, the cyclical or seasonal variations in the temporal dimension of most spatiotemporal processes are rarely considered along with spatiotemporal nonstationarity. Seasonal variations are widespread and typical in marine environmental processes, and addressing both spatiotemporal heterogeneity and seasonal variations is particularly difficult in the turbid and optically complex coastal seas. By incorporating seasonal periodic effects into a geographically and temporally weighted regression (GTWR) model, we proposed a geographically and cycle-temporally weighted regression (GcTWR) model. To test its performance, modelling of chlorophyll-a, known as an important indicator of the coastal environment, is performed using the in situ data collected from 2012 to 2016 in the coastal sea of Zhejiang Province, China. GcTWR is compared with global ordinary least squares (OLS), geographically weighted regression (GWR), cycle-temporally weighted regression (cTWR), and GTWR models. In the results, the GcTWR model decreases absolute errors by 89.74%, 79.77%, 76.60% and 29.83% relative to the OLS, GWR, cTWR, and GTWR models, and presents a higher R² (0.9274) than the GWR (0.5911), cTWR (0.6465), and GTWR (0.8721) models. The estimation results further confirm that the seasonal influences in coastal areas are much more significant than the interannual effects, which accordingly demonstrates that extending the GTWR model to handle both spatiotemporal heterogeneity and seasonal variations are meaningful. In addition, a novel 3D visualization method is proposed to explore the spatiotemporal heterogeneity of the estimation results.     

基于GWR模型的于田绿洲土壤表层盐分空间分异及其影响因子

以于田绿洲为研究靶区,利用24个采样点的土壤表层盐分数据,选取9个与土壤表层盐分密切相关的影响因子,结合空间自相关、传统回归分析和地理加权回归模型,分析表土盐分的空间分布特征及其影响因子的空间分异.结果表明:于田绿洲表土盐分在空间上并非随机分布,而是存在较强的空间依赖关系,空间自相关指数为0.479.地下水矿化度、地下水埋深、高程和温度是影响干旱区平原绿洲表土积盐的主要因子,这些因子具有空间异质性,选取的9个环境变量中除土壤pH值外,其他变量对表土盐分的影响强度均存在显著的空间分异.GWR模型对存在空间非平稳性数据的解释能力和估计精度都优于OLS模型,而且在模型估计参数的可视化上具有明显优势.