于田绿洲土壤盐渍化遥感监测研究

首先对含有7个多光谱波段的TM图像分别做了主成分、缨帽变换.然后在对遥感数据及其派生数据分析的基础上,得出可见光、近红外波段之一和第三主成分及绿度特征.做RGB彩色合成能够较好地揭示干旱区盐渍化土壤信息,此方法处理的效果受表层土壤含水量的影响.最后对合成图像分类,得出研究区盐渍地的数量和空间分布状况.     

基于高光谱与电磁感应技术的干旱区绿洲土壤含水量反演研究

为实现干旱区绿洲土壤含水量的快速、准确监测,利用采集自渭干河-库车河绿洲的84个表层(0~10cm)土壤样本,通过利用电磁感应仪(EM38)将所测解译后数据代替实测土壤含水量数据,将高光谱反射率重采样为Landsat8卫星遥感波段反射率,在选取光谱特征参数、提取敏感波段的基础上,利用偏最小二乘回归(PLSR)方法建立土壤含水量模型,将最优估算模型应用于遥感影像,实现研究区土壤含水量遥感反演。研究结果表明:(1)利用EM38所测水平模式土壤表观电导率与土壤含水量拟合效果最优,能够代替实测土壤含水量进行后续建模分析。(2)相比3种单一的光谱特征指数,利用多种光谱特征指数所建土壤含水量估算模型的建模效果更优,其干、湿各季建模集决定系数R~2大于0.7,均方根误差(RMSE)均小于0.5%,RPD均大于2,能够作为有效手段估算干旱区绿洲土壤含水量。(3)不同季节土壤含水量遥感反演值与实测值决定系数R~2均大于0.6,均方根误差(RMSE)均小于0.6%,显示了较高的预测精度,证明利用电磁感应技术与高光谱相结合能够实现对干旱区绿洲土壤含水量的精准、高效监测。     

土壤盐渍化雷达反演模拟研究

以河套平原巴彦淖尔市杭锦后旗陕坝镇灌区土壤盐分雷达监测数据为依据,研究基于RADARSAT-2数据的盐渍化信息提取技术与方法。利用Radarsat-2四极化数据,配合DEM数据计算得到研究区域的后向散射系数(Sigma Nought)值。通过分析得出Sigma Nought值与土壤含盐量之间的反演关系,并以此为依据对整幅影像进行决策树分类,以确定其盐渍化程度及分布。经实测数据检验该方法能够在一定程度上满足盐渍化监测的需要,优于传统盐渍土分类方法,可丰富盐渍化监测的理论与方法,促进微波遥感在土壤盐渍化监测中的开拓应用。     

玛纳斯河流域绿洲农田土壤盐分反演及空间分布特征

土壤盐分的准确监测是干旱、半干旱地区农业可持续发展的前提,气候变化、不合理灌溉等因素使土壤盐渍化问题逐渐显现.文中以新疆玛纳斯河流域为例,采用多光谱遥感影像和野外实测土壤盐分数据相结合的方法,选取对土壤盐渍化影响较大的归一化差异水体指数及其它具有代表性的多光谱遥感指数与土壤盐分构建反演模型,探讨研究区内土壤盐渍化的空间...     

基于多源数据的干旱区盐渍化农田精准管理分区研究

根据土壤盐渍化空间异质性对南疆干旱区绿洲农田进行精准管理分区,对农业种植结构调整和精细化管理具有重要意义.以典型干旱区绿洲农田为研究对象,以电磁感应数据、地形数据和卫星遥感数据为数据源,通过地统计学方法分析研究区土壤盐渍化的空间异质性,利用相关分析筛选出不同时期的植被指数和盐分指数.以农田表层表观电导率(ECh0.37...     

青土湖区植被与土壤盐渍化响应研究

以甘肃民勤县青土湖为研究区,用Landsat OLI数据计算植被覆盖度(FVC)和改进盐渍化指数(MSI),在分级分析和探究两者相关性的基础上,定量分析了不同植被覆盖度和发展变化特征对不同程度盐渍土的响应。结果表明:2015—2017年中高和高覆盖度植被占比较低,分别为4.3%和2.0%,极低覆盖度占比最大为50.0%,低覆盖度占比为44.3%。2015—2017年非盐渍土面积为25.9%,重度盐渍土占研究区总面积的45.4%。植被覆盖度和土壤盐渍化呈显著负相关,相关系数为-0.691。高覆盖度和中高覆盖度植被分布于非盐渍土区域达90.0%以上,极低覆盖度植被分布于盐渍土区域面积为93.4%。青土湖浅水区植被覆盖度与改进盐渍化指数呈显著负相关关系(r=-0.532,P0.05)的面积为88.95 km~2,占研究区总面积的62.84%。且植被覆盖度发展和土壤盐渍化逆转区域在空间分布上相一致。因此,土壤盐渍化是影响青土湖区植被覆盖度的重要因素之一。     

干旱地区盐渍土系统的熵污染及其调控对策

本文从系统土壤学的角度,探讨了河西干旱地区耕地次生盐渍化的主要成因,根据非平衡态热力学理论,研究了盐渍土系统的熵变过程,提出了熵污染机制。 作为盐渍土熵污染的定量分析,主要是研究土壤盐分对作物的危害作用。文中运用主导因素权重分析法、逐步回归分析法以及土壤盐类集与作物指标集之间的典型相关分析法,从不同层次上进行计算与分析,并根据定性与定量分析结果,提出了干旱区盐渍土整治和优化利用对策。     

基于随机森林算法的土壤含盐量预测

快速监测区域土壤盐渍化信息，对于盐渍化治理与生态环境保护具有重要意义。本文以Sentinel-2A和Landsat8 OLI遥感影像为数据源，以银川平原为研究区，利用谷歌地球引擎（Google Earth Engine,GEE）平台，基于随机森林算法，通过建立光谱指数特征与地面实测土壤含盐量之间的关系，进行土壤含盐量估算。结果表明：GEE能够为土壤含盐量预测提供可靠的数据支撑；以Sentinel-2A为数据源建立的随机森林模型具有更好的预测精度（R2=0.789,RMSE=1.487），优于Landsat8 OLI，可用于土壤含盐量高分辨率遥感估算，能够为大尺度土壤含盐量监测工作提供理论支撑。     

开都河流域下游绿洲盐渍化土壤高光谱特征

土壤光谱反射特性是土壤遥感的物理基础.通过野外调查采样、土壤盐分实验分析与土壤高光谱数据采集,对土壤高光谱数据一阶和二阶导数微分变换处理,分析土壤样品的光谱特征,建立土壤光谱和土壤盐分含量间的相关关系,对研究区盐渍化土壤含盐量进行定量反演.研究结果表明:1)从土壤光谱反射率的形态特征来看,土壤的光谱反射率曲线总体上变化较为平缓,光谱特征形态较为相似,且基本平行.2)研究区土壤光谱反射率曲线的形状大致可由300 ～ 600nm、600 ～ 800nm、800～1000nm、1000 ～ 1400nm、1400 ～1900nm、1900 ～ 2100nm、2100～ 2500nm七个折线段和560nm、900nm、1400nm、1900nm、2200nm五个特征吸收点来控制.3)利用光谱反射率一阶导数微分的盐渍化土壤含盐量多元线性回归预测模型的预测效果均优于利用反射率原型和反射率二阶导数微分,其中氯化物-硫酸盐型RMSE=0.33,硫酸盐型RMSE=0.31,硫酸盐-氯化物型RMSE=0.22.     

土壤盐分组成的计算

盐渍土是新疆重要的土壤资源,开发利用潜力很大。新疆以灌溉农业为主,普遍存在着土壤次生盐渍化的威胁。目前,新疆农业生产中盐渍土是低产土壤,约有三分之一耕地有不同程度的盐渍化,成为建设高产稳产农田的障碍。因此,摸清盐渍土发生与演变规律,有针对性地进行利用和改良,是当前发展农业生产所必需的。     

基于地表高光谱与OLI影像的土壤含盐量和pH值估测

针对宁夏银北地区大面积土壤盐碱化监测的需要,利用实测植被冠层光谱与Landsat 8 OLI影像相结合进行土壤含盐量和pH值估测研究.对实测植被冠层高光谱与影像多光谱反射率进行倒数、对数、三角函数及其一阶微分等一系列变换,确定最佳光谱变换形式,筛选敏感植被指数和敏感波段,分别建立基于实测植被光谱与Landsat 8 O...     

准噶尔盆地人工林地土壤全盐的高光谱反演

随着高光谱遥感技术的快速发展,通过其定量估测土壤化学成分具有很好的可行性。使用ASD Pro FieldSpec3便携式光谱仪,测量准噶尔盆地人工林地风干土壤样品的可见光-近红外光谱,利用土壤反射光谱值预测全盐的含量。首先,通过皮尔森相关系数分析方法,计算土壤全盐与土壤反射光谱之间的相关性,其中土壤光谱值的二阶导数与土壤全盐的相关系数最高为0.806,均方根误差最小为1.508。其次,在基于光谱反射率的基础上,通过多元统计回归分析,表明土壤光谱在1 130 nm、1 430 nm和1 930 nm波段的全盐反演模型预测的效果较好,可以利用这3个波段建立回归方程,对土壤全盐进行反演估算。     

基于HJ1A-HSI反演松嫩平原土壤盐分含量

以HSI高光谱遥感影像为数据源,利用地理信息系统和偏最小二乘回归（PLSR）分析方法,结合实地采样的盐分离子、EC值、pH的化验数据,分析盐分离子在HSI数据中的光谱特征,建立土壤盐分与高光谱数据的偏最小二乘回归模型,实现对松嫩平原土壤中主要盐分参数的反演实验。结果表明：偏最小二乘回归分析方法在保证信息量最大的前提下,降低了光谱数据维数,提高了分析的效率。利用偏最小二乘回归建立的预测模型,对全盐、[WTBX]EC[WTBZ]值、Na⁺+K⁺、Cl^-、HCO^-₃有较好的反演精度,模型的判定系数分别为0.799、0.879、0.772、0.791和0.694。在土壤含盐量的定量反演方面,探索了使用HSI影像作为新的数据源,为松嫩平原土壤盐分含量的精确、定量、快速获取及盐碱化防治等提供参考。     

光谱数据与冬小麦叶绿素密度的相关性研究

分析2006年栾城试验站不同氮素水平下冬小麦的多时相的群体光谱测量数据和相应叶片叶绿素密度的测量数据，发现：冬小麦的群体光谱的导数光谱数据、红边光谱数据，归一化植被指数NDVI和比值植被指数RVI与叶绿素密度具有很好的相关关系，并且选取样本建立了相应的回归方程。以回归方程作为叶绿素高光谱估算模型，并利用检验样本对估算模型进行检验，结果表明，以745nm处一阶导数光谱值、733nm处二阶导数光谱值和红边振幅为变量的模型可以较好的估算叶绿素密度。     

Derivation of salt content in salinized soil from hyperspectral reflectance data: A case study at Minqin Oasis,Northwest China

Soil salinization is a serious ecological and environmental problem because it adversely affects sustainable development worldwide, especially in arid and semi-arid regions. It is crucial and urgent that advanced technologies are used to efficiently and accurately assess the status of salinization processes. Case studies to determine the relations between particular types of salinization and their spectral reflectances are essential because of the distinctive characteristics of the reflectance spectra of particular salts. During April 2015 we collected surface soil samples(0–10 cm depth) at 64 field sites in the downstream area of Minqin Oasis in Northwest China, an area that is undergoing serious salinization. We developed a linear model for determination of salt content in soil from hyperspectral data as follows. First, we undertook chemical analysis of the soil samples to determine their soluble salt contents. We then measured the reflectance spectra of the soil samples, which we post-processed using a continuum-removed reflectance algorithm to enhance the absorption features and better discriminate subtle differences in spectral features. We applied a normalized difference salinity index to the continuum-removed hyperspectral data to obtain all possible waveband pairs. Correlation of the indices obtained for all of the waveband pairs with the wavebands corresponding to measured soil salinities showed that two wavebands centred at wavelengths of 1358 and 2382 nm had the highest sensitivity to salinity. We then applied the linear regression modelling to the data from half of the soil samples to develop a soil salinity index for the relationships between wavebands and laboratory measured soluble salt content. We used the hyperspectral data from the remaining samples to validate the model. The salt content in soil from Minqin Oasis were well produced by the model. Our results indicate that wavelengths at 1358 and 2382 nm are the optimal wavebands for monitoring the concentrations of chlorine and sulphate compounds, the predominant salts at Minqin Oasis. Our modelling provides a reference for future case studies on the use of hyperspectral data for predictive quantitative estimation of salt content in soils in arid regions. Further research is warranted on the application of this method to remotely sensed hyperspectral data to investigate its potential use for large-scale mapping of the extent and severity of soil salinity.     

Plant classification for weed detection using hyperspectral imaging with wavelet analysis

The goal of this study is to develop a new weed detection method that can be applied for automatic mechanical weed control. For successful weed detection, plants must be classified into crops and weeds according to their species. In this study, we employed a portable hyperspectral imaging system. The hyperspectral camera can capture landscape images that include crops, weeds, and the soil surface, and can provide more extensive information than conventional red, green, and blue (RGB) images. Although RGB images consist of red, green, and blue wavebands, the obtained hyperspectral images consist of 240 wavebands of spectral information. Hyperspectral imaging is expected to provide powerful technology for agricultural sensing. In the initial step of this study, the image pixels of the plants (crop or weeds) were segmented from the background soil surface using Euclidean distance as the discriminant function. In the next step, the image pixels of the crop (sugarbeet) and weeds (four species) were classified using the difference in the spectral characteristics of the plant species. In this process, classification variables were generated using wavelet transformation for data compression, noise reduction, and feature extraction, and then stepwise linear discriminant analysis was applied. The validation results indicate that the developed classification method has potential for practical use.     

Spectral discrimination of Ridolfia segetum and sunflower as affected by phenological stage

Ridolfia segetum is an umbelliferous weed frequent and abundant in sunflower crops in the Mediterranean basin. Field research was conducted to evaluate the potential of hyperspectral and multispectral reflectance and five vegetation indices in the visible to near infrared spectral range, for discriminating bare soil, sunflower and R. segetum at different phenological stages. This was a preliminary step for mapping R. segetum patches in sunflower using remote sensing for herbicide application decisions. Reflectance data were collected at three sampling dates (mid‐May, mid‐June and mid‐July, corresponding to vegetative‐early reproductive, flowering and senescent phenological stages respectively) using a handheld field spectroradiometer. Differences observed in hyperspectral reflectance curves were statistically significant within and between crop and weed phenological stages depending on sampling date, which facilitates their discrimination. Statistically significant differences in the multispectral and vegetation indices analysis showed that it is also possible to distinguish any of the classes studied. Our study provides some information for constructing the spectral libraries of sunflower and R. segetum in which the different phenological stages co‐existing in the field were considered. Hyperspectral and multispectral results suggest that mapping R. segetum patches in sunflower is feasible using airborne hyperspectral sensors, and high‐resolution satellite imagery or aerial photography, respectively, taking into account specific timeframes.     

Detecting soil salinity with arid fraction integrated index and salinity index in feature space using Landsat TM imagery

Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context(vegetation cover,moisture,surface roughness,and organic matter)and the weak spectral features of salinized soil.Therefore,an index such as the salinity index(SI)that only uses soil spectra may not detect soil salinity effectively and quantitatively.The use of vegetation reflectance as an indirect indicator can avoid limitations associated with the direct use of soil reflectance.The normalized difference vegetation index(NDVI),as the most common vegetation index,was found to be responsive to salinity but may not be available for retrieving sparse vegetation due to its sensitivity to background soil in arid areas.Therefore,the arid fraction integrated index(AFII)was created as supported by the spectral mixture analysis(SMA),which is more appropriate for analyzing variations in vegetation cover(particularly halophytes)than NDVI in the study area.Using soil and vegetation separately for detecting salinity perhaps is not feasible.Then,we developed a new and operational model,the soil salinity detecting model(SDM)that combines AFII and SI to quantitatively estimate the salt content in the surface soil.SDMs,including SDM1 and SDM2,were constructed through analyzing the spatial characteristics of soils with different salinization degree by integrating AFII and SI using a scatterplot.The SDMs were then compared to the combined spectral response index(COSRI)from field measurements with respect to the soil salt content.The results indicate that the SDM values are highly correlated with soil salinity,in contrast to the performance of COSRI.Strong exponential relationships were observed between soil salinity and SDMs(R2>0.86,RMSE<6.86)compared to COSRI(R2=0.71,RMSE=16.21).These results suggest that the feature space related to biophysical properties combined with AFII and SI can effectively provide information on soil salinity.