基于综合高光谱指数的区域土壤盐渍化监测研究

选取新疆塔里木盆地北缘渭干河-库车河三角洲绿洲盐渍化土壤、植被及其光谱反射率为研究对象,对实测土壤、植被高光谱进行包络线、倒数、对数、均方根、一阶微分等各种光谱变换,分析并确定反映盐渍化程度最敏感的波段,结果表明:实测高光谱土壤、植被一阶微分光谱变换对土壤盐渍化响应程度最敏感;基于实测综合光谱指数的盐渍化监测高光谱模型可以准确提取土壤盐渍化信息,明显优于传统遥感方法中单纯利用植被指数或者土壤盐分指数的模型,对土壤盐渍化的高精度遥感监测研究具有较好促进作用。

艾比湖湿地自然保护区土壤盐分多光谱遥感反演模型

土壤盐分是衡量土壤质量的要素,也是作物生长发育的基本条件。因此,迫切地需要一种可以快速了解土壤盐分含量(SSC)的方法。针对艾比湖湿地自然保护区,基于Landsat8 OLI多光谱遥感影像,以该研究区36个土壤表层样品的盐分含量为数据源,选择相关性较好的多光谱遥感指数分析研究区土壤盐分分布状况,并将其分别与实测SSC构建线性、对数、二次函数模型,进而优选精度最高的模型来反演该研究区SSC。结果表明:(1)在多光谱遥感指数中,与SSC相关性最高的是增强型植被指数(EVI),其相关性范围为(-0.70～-0.67);其次是传统型植被指数(TVI),其范围为(-0.58～-0.46);土壤盐分指数(SI)与SSC的相关性最低,其范围为(-0.45～0.16),其中SI3和SI4与SSC均没有相关性。(2)将实测土壤盐分值所反演的分布图与EVI对比分析,发现在西北、正南方向的艾比湖湖边周围和东北方向盐池桥的SSC均较高,其EVI的值较低,说明通过该研究区实测土壤盐分值所反演的盐分分布图与EVI的空间分布结果较为一致,表明EVI对该地区土壤盐分具有一定的敏感性,能较好地反演SSC的空间分布;(3)分别将三种EVI与实测SSC建模分析比较,发现SSC与增强型比值植被指数(ERVI)所构建的二次函数模型最好;其验证集的决定系数(R^2)为0.92,均方根误差(RMSE)为2.48,相对分析误差(RPD)为2.09,模型精度较高、稳定性较为可靠,相比之下,说明ERVI对该湿地自然保护区土壤盐分有更高的敏感性,可以用来预测该区域SSC,从而进行空间反演。在TVI中加入Landsat8多光谱遥感影像的b6和b7波段,得到EVI,以此来反演SSC是可行的,且比传统可见光和近红外波段所构建的植被指数反演效果更好。因此该研究不仅可以为遥感反演提供理论参考,而且对该地区SSC的定量估算和动态监测具有重要的意义,也可作为其他区域SSC预测反演的备选方案。

基于综合高光谱指数的区域土壤盐渍化建模——以平罗县为例

为建立综合高光谱土壤盐渍化模型,以干旱区典型的土壤盐渍化区域宁夏回族自治区平罗县为研究区,以植被和土壤的实测高光谱数据和土壤含盐量测量结果为基础数据,对高光谱数据进行平滑(S-G方法)和数学变换(倒数、对数和一阶微分等),并将其与土壤盐分数据进行相关分析,筛选对土壤含盐量响应最敏感的光谱波段,计算并选取最优高光谱植被指数和土壤盐分指数,采用多元线性回归方法建立高光谱土壤盐渍化模型.结果表明,以实测光谱反射率的对数一阶变换结果与土壤盐分指数和高光谱植被指数相结合作为自变量,土壤含盐量为因变量,所构建的多元线性回归模型效果最佳.该模型的预测值和实测值的相关性较好,R2=0.8279,通过99%的显著性检验;均方根误差为0.236 g/kg,相对分析误差为2.029,表明样本实测值与预测值之间的偏差较小,在一定程度上可以预测土壤含盐量.

A Spectral Index for Estimating Soil Salinity in the Yellow River Delta Region of China Using EO-1 Hyperion Data

Soil salinization is one of the most common land degradation processes. In this study, spectral measurements of saline soil samples collected from the Yellow River Delta region of China were conducted in laboratory and hyperspectral data were acquired from an EO-1 Hyperion sensor to quantitatively map soil salinity in the region. A soil salinity spectral index (SSI) was constructed from continuum-removed reflectance (CR-reflectance) at 2052 and 2203 nm, to analyze the spectral absorption features of the salt-affected soils. There existed a strong correlation (r = 0.91) between the SSI and soil salt content (SSC). Then, a model for estimation of SSC with SSI was established using univariate regression and validation of the model yielded a root mean square error (RMSE) of 0.986 and an R2 of 0.873. The model was applied to a Hyperion reflectance image on a pixel-by-pixel basis and the resulting quantitative salinity map was validated successfully with RMSE = 1.921 and R2 = 0.627. These suggested that the satellite hyperspectral data had the potential for predicting SSC in a large area.

基于EM38和遥感影像的土壤表观电导率建模研究

选取阿克苏地区渭干河-库车河绿洲外围的典型盐渍化区域作为研究对象,应用EM38大地电导率仪测量该地区样点表观电导率,选取四种不同的土壤盐分指数,结合同时期的遥感影像建立基于影像波段的土壤盐分指数回归模型,通过统计软件进行相关分析,发现表观电导率与土壤盐分指数S2即(b1-b3)/(b1+b3)的相关性最高,然后将不同层的土壤表观电导率同S2进行曲线拟合,对不同模型的拟合效果进行对比分析,结果表明:增长模型即复合比级数曲线模型的拟合度最高,通过精度验证,模型不适用于灌区,而荒漠区的各层的预测值与实测值相关分析结果都达到显著性水平,利用该模型可进行荒漠地区表观电导率的反演,因此基于电磁感应技术的土壤表观电导率建模研究可以为快速土壤盐渍化监测提供一种新方法。

Estimating Soil Salinity in the Yellow River Delta, Eastern China——An Integrated Approach Using Spectral and Terrain Indices with the Generalized Additive Model

Soil salinity is one of the most severe environmental problems worldwide. It is necessary to develop a soil-salinity-estimation model to project the spatial distribution of soil salinity. The aims of this study were to use remote sensed images and digital elevation model(DEM) to develop quantitative models for estimating soil salinity and to investigate the influence of vegetation on soil salinity estimation. Digital bands of Landsat Thematic Mapper(TM) images, vegetation indices, and terrain indices were selected as predictive variables for the estimation. The generalized additive model(GAM) was used to analyze the quantitative relationship between soil salt content, spectral properties, and terrain indices. Akaike's information criterion(AIC) was used to select relevant predictive variables for fitted GAMs. A correlation analysis and root mean square error between predicted and observed soil salt contents were used to validate the fitted GAMs. A high ratio of explained deviance suggests that an integrated approach using spectral and terrain indices with GAM was practical and efficient for estimating soil salinity. The performance of the fitted GAMs varied with changes in vegetation cover.Salinity in sparsely vegetated areas was estimated better than in densely vegetated areas. Visible red and near-infrared bands, and the second and third components of the tasseled cap transformation were the most important spectral variables for the estimation. Variable combinations in the fitted GAMs and their contribution varied with changes in vegetation cover. The contribution of terrain indices was smaller than that of spectral indices, possibly due to the low spatial resolution of DEM. This research may provide some beneficial references for regional soil salinity estimation.