粤西地区红土光谱特性研究

土壤中水分、有机质和氧化铁的含量对其光谱特征有很大影响。本文对粤西红土的有机质光谱指标、水分、氧化铁含量与 野外光谱特征进行分析。研究表明，在短波红外波段，水分含量与野外红土反射光谱关系最密切； 在近红外波段，氧化铁含量与 野外红土反射光谱关系最密切； 在可见光波段，有机质对野外红土光谱影响最大。     

土壤有机质光谱特征研究

对在宜兴市和横山县采集的174个土样400nm～2500nm波段的光谱曲线进行了研究。为了有效去除背景噪声对目标光谱的影响，并将非线性关系线性化，首先对土壤光谱进行了14种变换，然后运用光谱微分技术、逐步回归分析等方法研究了土壤光谱反射特性与土壤有机质之间的关系。结果表明，反射率对数的一阶微分这一变换形式对土壤有机质含量最为敏感。建立了相应的回归预测模型，模型方程判定系数达到0．885，较好地利用土壤光谱反射特性预测了土壤有机质的含量。     

松嫩平原典型土壤高光谱定量遥感研究

为实现松嫩平原典型土壤理化参数时空信息的快速获取,为定量遥感、精准农业等相关研究服务,以松嫩平原典型土壤的高光谱反射率为研究对象,分析土壤反射光谱特征及其与土壤理化参数的关系,建立基于反射光谱指数的土壤理化参数遥感估算模型;提取黑土光谱特征点,建立黑土反射光谱曲线模拟函数.结果表明:松嫩平原不同土壤光谱特征差异主要在450-600,600-800 nm两个吸收谷部分,土壤有机质是黑土反射光谱特征的决定因素;不同于南方土壤,铁对松嫩平原典型土壤反射光谱特征的影响较小;随着含水量的增加,土壤水分对土壤光谱反射率的作用过程可以用三次方程定量描述;基于土壤反射率及反射光谱特征的土壤理化参数光谱预测模型可以用于土壤相关理化参数的快速测定;基于光谱特征点的黑土反射光谱曲线模拟函数可以准确描述黑土的反射光谱特征,这一方法可以用于高光谱数据压缩和基于多光谱数据的高光谱反射率重建.     

像元二分模型的土壤重金属Cr的遥感反演

针对遥感影像上土壤重金属光谱敏感性不显著、土壤重金属含量定量反演拟合模型精度低的缺陷,该文采用像元二分模型线性解混提取土壤反射光谱,运用倒数对数变换后的土壤反射光谱为光谱参量建立土壤重金属Cr含量的三次多项式估算模型,反演耕地土壤中Cr的含量。结果表明,经过像元二分模型处理后的土壤反射率与重金属Cr的敏感性从不显著提升为显著相关,反演模型拟合优度显著提高。可见,基于像元二分模型得到土壤反射光谱,提升了其与重金属(Cr)之间的敏感性,提高了土壤重金属含量估测模型精度。     

土壤钾含量高光谱定量反演研究

为了更快捷准确地进行土壤钾(K)含量的预测,基于土壤高光谱数据和化学元素分析数据,研究土壤光谱与土壤钾含量之间的定量关系.在对土壤原始光谱进行处理分析基础上,提取反射率(R)、反射率倒数的对数(log(1/R))、反射率一阶微分(R')和波段深度(BD)4种光谱指标,运用偏最小二乘回归方法建立相应的预测模型,并对模型进行检验.结果表明,波段深度是估算土壤钾含量最好的光谱指标,其建模精度超过0.85,均方根误差不超过0.1;全波段高光谱分辨率反射光谱具有快速有效估算土壤钾含量的潜力.     

不同含水量土壤偏振光谱特征定量分析

水分含量是影响土壤偏振光谱特征的重要参数之一,研究土壤不同水分含量的偏振光谱特征在土壤偏振遥感波段的选择和图像解释上具有重要意义,为应用偏振遥感信息进行土壤调查及理化性质的分析提供依据。本文通过在350-2500 nm波段范围内对不同水分含量的土壤进行偏振光谱测试与分析,研究土壤偏振光谱数据与水分含量之间的关系,建模并检验其精度,对影响土壤偏振光谱特征的各个因素设计科学的正交实验,研究含水量、偏振角度、探测角和方位角等各个因素及其交互作用对土壤偏振光谱特征的影响。结果表明,含水量与偏振角度的交互作用和含水量本身对土壤偏振光谱的影响最大,显著性最强,其次是探测角与含水量的交互作用的影响,而偏振角对土壤偏振光谱有一定的影响,其他因素对土壤光谱偏振反射的影响不大。     

中国陆地背景和土壤光谱反射特性的地理分区的初步研究

通过对土壤光谱反射特性与土壤理化性状关系的研究,并根据我国土壤的分布规律,提出了以植被覆盖度和土壤光谱反射特性为基础的我国陆地背景地理分区的初步方案。该方案把我国划分为6个区和23个亚区。     

基于反射光谱预测土壤重金属元素含量的研究

本文利用实验室实测的土壤反射光谱以及铅、镉、汞等重金属元素数据,进行土壤重金属元素含量快速预测的可行性研究。本文利用偏最小二乘回归方法,研究了反射率(R)、一阶微分(FDR)、反射率倒数的对数(lg(1/R))和波段深度(BD)等对预测精度的影响,对这几种光谱指标预测土壤重金属含量的能力进行了分析和评价,同时分析了多光谱数据估算土壤重金属元素含量的可行性。结果表明,反射率倒数的对数lg(1/R)是估算土壤重金属元素含量最好的光谱指标,尤其是Cd和Pb,检验精度R超过0.82。有机质、铁锰氧化物和黏土矿物对土壤重金属元素的吸附是可见光—近红外—短波红外光谱估算其含量的机理。多光谱数据同样具有估算土壤重金属元素含量的能力,但实际数据则要考虑多种因素的影响。     

基于地面实测光谱的土壤有机质含量预测

以德兴铜矿尾矿坝附近的土壤为研究对象,在实验室内利用ASD便携式光谱仪对研究区内68组土壤样本进行了测定,并通过研究土壤反射光谱特征,选择了反射率的对数微分变换作为土壤有机质(soil organic matter,SOM)预测模型的因变量;通过对土壤有机质含量与土壤光谱特征的相关分析,将402 nm与2 312 nm波段反射率的对数微分变换结果参与模型建立;最终,从多元回归分析和模糊数学2个角度建立了有机质含量的预测模型。结果表明:基于模糊数学的研究方法优于多元线性回归方法,相关系数达到89.3%,平均相对误差较小。因此,地面实测光谱可以用于预测土壤的有机质含量。该方法具有周期短、成本低等特点。     

微量元素在植物光谱中的响应机理研究

微量元素与植物光谱特征的研究是光学遥感定量化研究的重要内容之一。本研究利用高光谱地面光谱仪（GER）在湖南黔阳地区不同地质地球化学背景下的同一垂直剖面上分别测量了岩石、土壤植物（林灌草）的反射光谱曲线和植物叶片的8种微量元素含量。通过研究微量元素Fe,Mn,Cu,Zn,Co,Cr,Mo,B与植物（包括林灌草）反射光谱间的相关性研究发现Co的含量与植物光谱绿区（0．56μm附近）反射率存在强的负相关，Mn,B,Mo和Zn分别在可见光、近红外、短波红外存在较好的相关性和光谱响应。     

水分含量与红土野外光谱的定量分析

土壤水分含量与土壤光谱关系密切,本文利用实测粤西红土野外光谱数据及其水分含量来分析两者之间的关系。得出以下结论:在0.46-2.5μm波段水分含量与红土光谱反射率呈显著负相关关系,并且随波长的增加其相关性有所增强,在2.22-2.29μm波段两者相关性最强;利用野外红土光谱反射率的倒数与水分含量进行回归分析,得到建模样本修正后的确定系数R2为0.781。这将为利用遥感技术监测土壤水分提供依据。     

土壤有机质光谱特征研究(英文)

The study on soil spectral reflectance features is the physical basis for soil remote sensing. Soil organic matter content influences the soil spectral reflectance dramatically. This paper studied the spectral curves between 400 nm∼2500 nm of 174 soil samples which were collected in Hengshan county and Yixing county. Fourteen types of transformations were applied to the soil reflectance R to remove the noise and to linearize the correlation between reflectance (independent variable) and soil organic matter (SOM) content (dependent variable). Then, the methods such as derivative spectrum technology and stepwise regression analysis, were applied to study the relationship between these soil spectral features and soil organic matter content. It shows that order 1 derivative of the logarithm of reflectance (O1DLA) is the most sensitive to SOM among the various transform types of reflectance in consideration. The regression model whose coefficient of determination reaches 0.885 is built. It predicted the soil organic matter content with higher effect. Supported by the National Natural Science Foundation of China (No. 40271007).     

Estimating soil zinc concentrations using reflectance spectroscopy

Soil contamination by heavy metals has been an increasingly severe threat to nature environment and human health. Efficiently investigation of contamination status is essential to soil protection and remediation. Visible and near-infrared reflectance spectroscopy (VNIRS) has been regarded as an alternative for monitoring soil contamination by heavy metals. Generally, the entire VNIR spectral bands are employed to estimate heavy metal concentration, which lacks interpretability and requires much calculation. In this study, 74 soil samples were collected from Hunan Province, China and their reflectance spectra were used to estimate zinc (Zn) concentration in soil. Organic matter and clay minerals have strong adsorption for Zn in soil. Spectral bands associated with organic matter and clay minerals were used for estimation with genetic algorithm based partial least square regression (GA-PLSR). The entire VNIR spectral bands, the bands associated with organic matter and the bands associated with clay minerals were incorporated as comparisons. Root mean square error of prediction, residual prediction deviation, and coefficient of determination (R²) for the model developed using combined bands of organic matter and clay minerals were 329.65 mg kg⁻¹, 1.96 and 0.73, which is better than 341.88 mg kg⁻¹, 1.89 and 0.71 for the entire VNIR spectral bands, 492.65 mg kg⁻¹, 1.31 and 0.40 for the organic matter, and 430.26 mg kg⁻¹, 1.50 and 0.54 for the clay minerals. Additionally, in consideration of atmospheric water vapor absorption in field spectra measurement, combined bands of organic matter and absorption around 2200 nm were used for estimation and achieved high prediction accuracy with R² reached 0.640. The results indicate huge potential of soil reflectance spectroscopy in estimating Zn concentrations in soil.     

粗糙地表土壤含水量遥感模型研究

土壤粗糙度对地表二向反射率特征有重要影响而且极不稳定 ,是影响土壤含水量遥感精度的一个重要因素。该文分析土壤含水量影响反射率的物理过程 ,论证土壤含水量与土壤光谱的关系服从比尔定律 ;分析土块级土壤粗糙度影响土壤反射率的根本原因是微坡面的倾斜使二次和多次散射的比例增加。在此基础上建立粗糙地表土壤含水量遥感模型 ;实验证明模型具有较高的精度     

Studies on Textural and Compositional Characteristics of Sand and Clay Mixtures Using Hyperspectral Radiometry

This paper examines the hyperspectral signatures (in the Visible Near Infrared (VNIR)-Shortwave Infrared (SWIR) regions) of soil samples with varying colour and minerals. 36 samples of sands (from river and beach) with differing clay contents were examined using a hyperspectral radiometer operating in the 350–2,500 nm range, and the spectral curves were obtained. Analysis of the spectra indicates that there is an overall increase in the reflectance in the VNIR-SWIR region with an increase in the content of kaolinite clay in the sand samples. As regards the red and black clays and sand mixtures, the overall reflectance increases with decreasing clay content. Several spectral parameters such as depth of absorption at 1,400 nm and 1,900 nm regions, radius of curvature of the absorption troughs, slope at a particular wavelength region and the peak reflectance values were derived. There exists a correlation between certain of these spectral parameters (depth, slope, position, peak reflectance, area under the curve and radius of the curve) and the compositional and textural parameters of the soils. Based on these well-defined relations, it is inferred that hyperspectral radiometry in the VNIR and SWIR regions can be used to identify the type of clay and estimate the clay content in a given soil and thus define its geotechnical category.     

Retrieval of soil physicochemical properties towards assessing salt-affected soils using Hyperspectral Data

The relationship between soil salinity parameters and their influence on soil spectral characteristics were analyzed using both satellite data (Hyperion) and reflectance data of soil samples collected from parts of Ahmedabad district of Gujarat, India. The soil spectral reflectance curves were assessed using absorption feature parameters by DISPEC software to identify suitable spectral band for salinity characterization. The Hyperion data of the study area were processed and classified into different classes by spectral angle mapper algorithm using spectral library generated from soil spectra. The results showed that among all the observed soil parameters Electrical Conductivity, Exchangeable Sodium Percentage, Cation Exchange Capacity and Mg⁺⁺ predictions can be made accurately based on partial least square regression models developed from selected wavelengths. Out of the total study area moderately saline-sodic, severely saline-sodic, severely saline and slightly saline soils occupy 23.5, 12.6, 10.9 and 0.04%, respectively.     

Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra

Soil salinization is a worldwide environmental problem with severe economic and social consequences. In this paper, estimating the soil salinity of Pingluo County, China by a partial least squares regression (PLSR) predictive model was carried out using QuickBird data and soil reflectance spectra. At first, a relationship between the sensitive bands of soil salinity acquired from measured reflectance spectra and the spectral coverage of seven commonly used optical sensors was analyzed. Secondly, the potentiality of QuickBird data in estimating soil salinity by analyzing the correlations between the measured reflectance spectra and reflectance spectra derived from QuickBird data and analyzing the contributions of each band of QuickBird data to soil salinity estimation Finally, a PLSR predictive model of soil salinity was developed using reflectance spectra from QuickBird data and eight spectral indices derived from QuickBird data. The results indicated that the sensitive bands covered several bands of each optical sensor and these sensors can be used for soil salinity estimation. The result of estimation model showed that an accurate prediction of soil salinity can be made based on the PLSR method (R² = 0.992, RMSE = 0.195). The PLSR model's performance was better than that of the stepwise multiple regression (SMR) method. The results also indicated that using spectral indices such as intensity within spectral bands (Int1, Int2), soil salinity indices (SI1, SI2, SI3), the brightness index (BI), the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) as independent model variables can help to increase the accuracy of soil salinity mapping. The NDVI and RVI can help to reduce the influences of vegetation cover and soil moisture on prediction accuracy. The method developed in this paper can be applied in other arid and semi-arid areas, such as western China.     

Analysis and classification of hyperspectral data for mapping land degradation: An application in southern Spain

Desertification is a severe stage of land degradation, manifested by “desert-like” conditions in dryland areas. Climatic conditions together with geomorphologic processes help to mould desert-like soil surface features in arid zones. The identification of these soil features serves as a useful input for understanding the desertification process and land degradation as a whole. In the present study, imaging spectrometer data were used to detect and map desert-like surface features. Absorption feature parameters in the spectral region between 0.4 and 2.5 μm wavelengths were analysed and correlated with soil properties, such as soil colour, soil salinity, gypsum content, etc. Soil groupings were made based on their similarities and their spectral reflectance curves were studied. Distinct differences in the reflectance curves throughout the spectrum were exhibited between groups. Although the samples belonging to the same group shared common properties, the curves still showed differences within the same group.Characteristic reflectance curves of soil surface features were derived from spectral measurements both in the field and in the laboratory, and mean reflectance values derived from image pixels representing known features. Linear unmixing and spectral angle matching techniques were applied to assess their suitability in mapping surface features for land degradation studies. The study showed that linear unmixing provided more realistic results for mapping “desert-like” surface features than the spectral angle matching technique.