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1.
Mid‐infrared spectroscopy (MIRS) is a well‐established analytical tool for qualitative and quantitative analysis of soil samples. However, effects of soil sample grinding procedures on the prediction accuracy of MIR models and on qualitative spectral information have not been well investigated and, in consequence, not standardized up to now. Further, the effects of soil sample selection on the accuracy of MIR prediction models has not been quantified yet. This study investigated these effects by using 180 well‐characterized soil samples that were ground for different times (0, 2 or 4 minutes) and then used for MIR measurements. To study the impact of sample preparation, soil spectra were subjected to principal component analyses (PCA), multiple regression and partial least square (PLS) analysis. The results indicate that the prediction accuracy of MIR models for soil organic carbon (SOC) and pH and the qualitative spectral information were better overall for lightly ground (2 minutes) soil samples compared with intensively (4 minutes) or unground soil samples. Whereas the grinding procedure did not show any effect on spectra of clay minerals, spectral information for quartz and for SOC was modified. Even though it is difficult to recommend a global standardized soil sample grinding procedure for MIR measurements because of different mill types available within laboratories, we highly recommend using an internally standardized grinding procedure. Moreover, we show that neither land use nor soil sampling depth influences the prediction of the SOC content. However, sand and clay content substantially affect the score vectors used by the PLS algorithm to predict the SOC content. Thus, we recommend using soil samples similar in texture for more precise SOC calibration models for MIR spectroscopy.  相似文献   

2.
基于EPO-PLS回归模型的盐渍化土壤含水率高光谱反演   总被引：5，自引：1，他引：4  
表层土壤含水率对于指导农业灌溉有重要的作用。研究表明,土壤光谱受到土壤水分和盐分的共同影响,但对于盐渍化地区的土壤含水率高光谱反演却很少涉及。该文通过对11组不同含盐量土壤室内蒸发过程连续监测,获取相关反射率光谱和水分、盐分的变化数据,利用外部参数正交化方法(external parameter orthogonalisation,EPO)预处理土壤光谱,滤除盐分(质量比0.1%~5.0%)的影响,建立经过EPO预处理后的偏最小二乘(partial least squares regression after EPO pre-processing,EPO-PLS)土壤水分预测模型。与偏最小二乘(partial least square model,PLS)模型相比,验证样本的决定系数R2和对分析误差RPD(residual predictive deviation)分别从0.722、1.976上升到0.898、3.145;均方根误差RMSE从5.087 g/(100 g)减少到3.237 g/(100 g)。通过EPO算法预处理后的模型性能提升显著,利用该方法能够有效的消除土壤盐分的影响,很好地实现盐渍化地区的水分含量估测。  相似文献   

3.
This study explored the potential of mid-infrared spectroscopy (MIR) with partial least-squares (PLS) analysis to predict sorption coefficients (Kd) of pesticides in soil. The MIR technique has the advantage of being sensitive to both the content and the chemistry of soil organic matter and mineralogy, the important factors in the sorption of nonionic pesticides. MIR spectra and batch Kd values of atrazine were determined on a set of 31 soil samples as reference data for PLS calibration. The samples, with high variability in soil organic carbon content (SOC), were chosen from 10 southern Australian soil profiles (A1, A2, B, and C in one case). PLS calibrations, developed for the prediction of Kd from the MIR spectra and reference Kd data, were compared with predictions from Koc-based indirect estimation using SOC content. The reference Kd data for the 31 samples ranged from 0.31 to 5.48 L/kg, whereas Koc ranged from 30 to 680 L/kg. Both coefficients generally increased with total SOC content but showed a relatively poor coefficient of determination (R2 = 0.53; P > 0.0001) and a high standard error of prediction (SEP =1.22) for the prediction of Kd from Koc. This poor prediction suggested that total SOC content alone could explain only half of the variation in Kd. In contrast, the regression plot of PLS predicted versus measured Kd resulted in an improved correlation, with R2 = 0.72 ( P > 0.0001) and standard error of cross-validation (SECV) = 0.63 for three PLS factors. With the advantages of MIR-PLS in mind, (i) more accurate prediction of Kd, (ii) an ability to reflect the nature and content of SOC as well as mineralogy, and (iii) high repeatability and throughput, it is proposed that MIR-PLS has the potential for an improved and rapid assessment of pesticide sorption in soils.  相似文献   

4.
去除水分影响提高土壤有机质含量高光谱估测精度   总被引：9，自引：5，他引：4  
土壤水分的影响是当前采用光谱分析法预测土壤养分含量的关键问题,该文旨在探索去除土壤水分影响、提高有机质高光谱定量估测精度的方法。首先采用地物光谱仪进行湿土和过筛干土的高光谱测试,并进行一阶导数变换;然后,采用奇异值分解(singular value decomposition,SVD)结合相关分析筛选土壤水分特征光谱,构建去除水分因素的修正系数,形成湿土光谱的校正光谱;最后基于校正前后湿土光谱,应用偏最小二乘(partial least squares,PLS)回归构建土壤有机质含量的估测模型,并对模型进行验证和比较,分析评价校正前后光谱的预测精度。结果显示:按土壤水分含量梯度划分的2组和全部棕壤及褐土土样共4组样本校正后建模决定系数和均方根误差分别为0.85、0.82、0.74、0.76和0.19%、0.20%、0.23%、0.19%,决定系数提高了0.02~0.09,均方根误差降低了0.01~0.03百分点,验证决定系数、均方根误差和相对分析误差分别为0.78、0.77、0.72、0.76,0.21%、0.15%、0.21%、0.15%和2.03、2.02、1.86、1.98,决定系数提高了0.06~0.15,均方根误差除褐土土样提高0.02百分点外,其他样本组降低了0.01~0.08百分点,相对分析误差提高了0.17~0.43,模型决定系数和相对分析误差得到显著提升;尤其对于土壤水分含量变异系数较小的3组土样,模型从待改进级别提高到性能良好级别,对土壤有机质含量具有较好的预测准确性。说明该方法用于去除土壤水分因素影响和提高有机质含量高光谱估测精度的有效性。  相似文献   

5.
Remote sensing allows for the rapid and inexpensive acquisition of soil reflectance data. Knowing what soil parameters have the greatest influence on bare soil imagery will facilitate better use of remote sensing for precision crop management. The objectives of this study were (i) to determine measured soil properties that are most influential on remotely sensed bare soil reflectance and (ii) to select which spectral band or combination of spectral bands is best for describing individual soil properties. This study was conducted on three study sites located in northeastern Colorado. All sites were in irrigated continuous corn (Zea mays L.) cropping systems. Remotely sensed imagery was acquired by aircraft prior to planting. Soil samples were collected and analyzed for bulk density, soil color (moist and dry), organic matter, organic carbon, soil texture, and cone index. Principal component analysis (PCA) was performed for the green, red, and near-infrared (NIR) bands of the imagery. Least-squares regression analysis was used for analyzing relationships between remote sensing data and soil data. Across study sites, the first principal components of the green, red, and NIR bands were found to have significant statistical relationships with organic carbon and sand, silt, and clay fractions. Individual spectral bands explained a significant portion of the variability in soil moisture, moist soil color, dry soil color, organic carbon, sand, silt, and clay. Results from this study support the use of remote sensing for assessment of soil variability.  相似文献   

6.
In this study, we examined the efficiency of a kaolinite clayey soil to mitigate water repellency of a sandy soil with olive trees. The treatment was applied to the soil zone below the tree canopy, which displayed the highest degree of water repellency [average water drop penetration time (WDPT) value = 820 s]. Both dry (incorporated onto the top soil) and wet clay applications (after dispersion in irrigation water) were examined in a replicated experiment, with control trees being used for comparison. The application rate of the clayey soil was maintained in both cases (wet and dry mode) equal to 1 kg m−2, while the effect of subsequent wetting and drying cycles on the treatment performance was evaluated. The results of the study verify that clay application was effective to mitigate soil water repellency. Dry supplementation displayed low efficiency (26% reduction of the air‐dry WDPT compared with the control soil) within the first week of application. The efficiency of the dry‐clay treatment increased to 76% after applying three subsequent wetting and drying cycles. In comparison with the dry mode, the wet clay was efficient immediately after application (74% reduction of the WDPT), indicating that the limiting step in the overall process was clay dispersion. Based on the findings of this study, it was proposed that wet clay application is of interest for controlling soil water repellency in agricultural land. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

7.
Visible near-infrared (vis-NIR) and portable X-ray fluorescence (pXRF) spectrometers have been increasingly utilized for predicting soil properties worldwide. However, only a few studies have focused on splitting the predictive models by horizons to evaluate prediction performance and systematically compare prediction performance for A, B, and combined A+B horizons. Therefore, we investigated the performance of pXRF and vis-NIR spectra, as individual or combined, for predicting the clay, silt, sand, total carbon (TC), and pH of soils developed in loess, and compared their prediction performance for A, B, and A+B horizons. Soil samples (176 in A horizon and 172 in B horizon) were taken from Mollisols and Alfisols in 136 pedons in Wisconsin, USA and analyzed for clay, silt, sand, pH, and TC. The pXRF and vis-NIR spectrometers were used to measure the pXRF and vis-NIR soil spectra. Data were separated into calibration (n=244, 70%) and validation (n=104, 30%) datasets. The Savitzky-Golay filter was applied to preprocess the pXRF and vis-NIR spectra, and the first 10 principal components (PCs) were selected through principal component analysis (PCA). Five types of predictor, i.e., PCs from vis-NIR spectra, pXRF of beams at 0-40 and 0-10 keV (XRF40 and XRF10, respectively) spectra, combined XRF40 and XRF10 (XRF40+XRF10) spectra, and combined XRF40, XRF10, and vis-NIR (XRF40+XRF10+vis-NIR) spectra, were compared for predicting soil properties using a machine learning algorithm (Cubist model). A multiple linear regression (MLR) model was applied to predict clay, silt, sand, pH, and TC using pXRF elements. The results suggested that pXRF spectra had better prediction performance for clay, silt, and sand, whereas vis-NIR spectra produced better TC and pH predictions. The best prediction performance for sand (R2=0.97), silt (R2=0.95), and clay (R2=0.84) was achieved using vis-NIR+XRF40+XRF10 spectra in B horizon, whereas the best prediction performance for TC (R2=0.93) and pH (R2=0.79) was achieved using vis-NIR+XRF40+XRF10 spectra in A+B horizon. For all soil properties, the best MLR model had a lower prediction accuracy than the Cubist model. It was concluded that pXRF and vis-NIR spectra can be successfully applied for predicting clay, silt, sand, pH, and TC with high accuracy for soils developed in loess, and that spectral models should be developed for different horizons to achieve high prediction accuracy.  相似文献   

8.
Recent advances in semiconductor technologies have given rise to the development of mid‐infrared (mid‐IR) spectrometers that are compact, relatively inexpensive, robust and suitable for in situ proximal soil sensing. The objectives of this research were to evaluate a prototype portable mid‐IR spectrometer for direct measurements of soil reflectance and to model the spectra to predict sand, clay and soil organic matter (SOM) contents under a range of field soil water conditions. Soil samples were collected from 23 locations at different depths in four agricultural fields to represent a range of soil textures, from sands to clay loams. The particle size distribution and SOM content of 48 soil samples were measured in the laboratory by conventional analytical methods. In addition to air‐dry soil, each sample was wetted with two different amounts of water before the spectroscopic measurements were made. The prototype spectrometer was used to measure reflectance (R) in the range between 1811 and 898 cm?1 (approximately 5522 to 11 136 nm). The spectroscopic measurements were recorded randomly and in triplicate, resulting in a total of 432 reflectance spectra (48 samples × three soil water contents × three replicates). The spectra were transformed to log10 (1/R) and mean centred for the multivariate statistical analyses. The 48 samples were split randomly into a calibration set (70%) and a validation set (30%). A partial least squares regression (PLSR) was used to develop spectroscopic calibrations to predict sand, clay and SOM contents. Results show that the portable spectrometer can be used with PLSR to predict clay and sand contents of either wet or dry soil samples with a root mean square error (RMSE) of around 10%. Predictions of SOM content resulted in RMSE values that ranged between 0.76 and 2.24%.  相似文献   

9.
基于相似光谱匹配预测土壤有机质和阳离子交换量   总被引：4，自引：1，他引：3  
土壤可见光-近红外波段光谱(350~2 500 nm)包含了大量的土壤属性信息,相同类型的土壤具有相似的光谱曲线特征,但相似光谱曲线是否具有相似的属性含量?探讨此问题可为土壤光谱库的应用提供依据,从而最终服务于快速获取土壤信息技术体系的构建。该研究以安徽宣城为研究区,根据母质、地形特征和土地利用等信息,采集91个典型土壤剖面,共含400个土壤发生层样品,测定了有机质(soil organic matter,SOM)和阳离子交换量(cation exchange capacity,CEC)含量,同时采用VARIAN公司的Cary 5000分光光度计测定了土壤光谱,并将光谱数据变换为反射率(R)、反射率一阶导数(FDR)和吸收度(Log(1/R))3种形式。该文采用光谱角(spectral angle mapper,SAM)、偏最小二乘回归(partial least square regression,PLSR)和SAM-PLSR(spectral angle mapper-partial least square regression,SAM-PLSR)3种方法预测土壤SOM和CEC。SAM方法是通过对测试集104个光谱曲线与参考集的296个光谱曲线进行相似性计算,并以此实现土壤SOM和CEC含量的预测。SAM-PLSR方法以SAM算法下的匹配结果作为建模样本建立PLSR模型和进行预测分析。结果表明,具有相似光谱曲线的土壤具有相似的SOM和CEC含量,SAM算法下相似光谱匹配可直接预测SOM(R2=0.78,RPD=2.17)和CEC(R2=0.82,RPD=2.41)。PLSR方法可很好地预测SOM(R2=0.87,RPD=2.77)和CEC(R2=0.87,RPD=2.59);相较之下,SAM-PLSR方法不仅可以更加准确预测SOM(R2=0.89,RPD=3.00)和CEC(R2=0.91,RPD=3.06),而且大大减少了建模样本的数量。该研究使可见光-近红外光谱可更加高效地用于土壤属性分析,并为土壤光谱数据库的建设及应用提供技术参考。  相似文献   

10.
Carbon 13 nuclear magnetic resonance spectroscopy (13C NMR) is a powerful technique for studying the structure and turnover of soil organic matter, but is time consuming and expensive. It is therefore worth seeking swifter and cheaper methods. Diffuse reflectance FT‐IR spectroscopy (DRIFT), along with partial least squares (PLS) algorithms, provides statistical models to quantify soil properties, such as contents of C, N and clay. I have applied DRIFT?PLS to quantify soil organic C species, as measured by solid state 13C NMR spectroscopy, for several bulk soils and physical soil fractions. Calibration and prediction models for organic C and for particular NMR regions, namely alkyl C, O?alkyl C and carboxyl C, attained R2 values of between 0.94 and 0.98 (calibration) and 0.70–0.93 (cross‐validation). The prediction of unknown soil samples, after pre‐selection by statistical indices, confirmed the applicability of DRIFT?PLS. The prediction of aromatic C failed, probably because of superimposition of aromatic bands by signals from minerals. Results from fractions of particulate organic matter suggest that the chemical homogeneity of the material hampers the quantification of its constituting C species by DRIFT?PLS. For alkyl C, prediction of carbon species by DRIFT?PLS was better than direct peak‐area quantification in the IR spectra, but advantageous in parts only compared with a linear model correlating C species with soil C contents. In conclusion, DRIFT?PLS calibrated with NMR data provides quantitative information on the composition of soil organic matter and can therefore complement structural studies by its application to large numbers of samples. However, it cannot replace the information provided by more specific methods. The actual potential of DRIFT?PLS lies in its capacity to predict unknown samples, which is helpful for classification and identification of environmental outliers or benchmarks.  相似文献   

11.
采用干、湿筛法研究了种植苎麻和花生对红壤旱地土壤团聚体及其特性的影响,并比较分析了土壤团聚体及土壤理化性质与地表径流和土壤侵蚀量的关系。结果表明:(1)与花生地相比,苎麻地有机质、田间持水量、总孔隙度、沙粒分别升高了28.44%,10.06%,5.65%和53.13%,土壤容重、粉粒和黏粒则分别降低了7.20%,14.85%和34.95%,均达显著性差异水平(p0.05)。(2)团聚体平均重量直径(MWD)、稳定性指数(ASI)显著升高(p0.01),苎麻地土壤团聚体稳定性优于花生地;(3)两处理均以0.25~1mm粒径团聚体保存几率最大,抗水蚀能力最强。(4)地表径流量和土壤侵蚀量与土壤有机质、沙粒含量、1mm的干团聚体、0.5mm的水稳性团聚体、MWD以及ASI呈极显著负相关关系(p0.01),而与粉粒、黏粒、0.25mm干团聚体、0.053mm的水稳性团聚体、呈极显著正相关关系(p0.01)。  相似文献   

12.
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13.
C. Chinn  U.P.P. Pillai   《Geoderma》2008,144(3-4):491-501
Vertisols have the inherent ability to self-repair because of high clay contents and clay type that govern volume change. A study was undertaken to correlate soil inherent properties with two indicators of structure improvement based on tensile strength and clod porosity of compacted soil cores before and after wet/dry cycles. In order to minimize inter-soil differences Vertisols under similar cropping regimes and from the same climatic region in Queensland, Australia were selected. A soil repair index (RT(1)) based on compressive strength of soil cores was related to soil inherent properties and shrinkage indices, COLESTD and LSMOD using multiple regression. Results showed that compressive strength of soil cores after a single wet/dry cycle after compaction was sufficient to rank Vertisols in terms of their capacity to improve structure after compaction. Clay content and clay activity (CEC/clay) on their own were poor indicators of soil repair. Fine sand was shown to be an important component in the repair process. LSMOD and COLESTD predicted RT(1) equally well and indicated that Vertisols with COLESTD values > 0.15 and LSMOD > 12% would be expected to have sharper reductions in tensile strength compared to those with lower values after just one wet/dry cycle. Clod porosity was poorly related to soil inherent properties.  相似文献   

14.
Reflectance spectroscopy provides an alternate method to classical physical and chemical laboratory soil analysis for estimation of a large range of key soil properties. Techniques including classical chemometrics approaches and specific absorption features studies have been developed for deriving estimates of soil characteristics from visible and near-infrared (VNIR, 400-1200 nm) and shortwave infrared (SWIR, 1200-2500 nm) reflectance measurements. This paper examines the performances of two distinct methods for clay and calcium carbonate (CaCO3) content estimation (two key soil properties for erosion prediction) by VNIR/SWIR spectroscopy: i) the Continuum Removal (CR) has been used to correlate spectral absorption bands centred at 2206 and 2341 nm with clay and CaCO3 concentrations and ii) the partial least-squares regression (PLSR) method with leave-one-out cross-validation, which is a classical chemometrics technique, has been used to predict clay and CaCO3 concentrations from VNIR/SWIR full spectra. We tried to respond to the question “should we use all bands in the 400-2500 nm range or should we focus our analysis on selected spectral absorption bands to determine soil properties from reflectance data?” In this paper, the CR and PLSR methods were applied to VNIR/SWIR laboratory and airborne HYMAP reflectance measurements collected over the La Peyne Valley area in southern France.This study shows that the performance of both techniques is dependent on the spectral feature for the soil property of interest and on the level data acquisition (lab or airborne) face to the instrument specifications. When airborne HYMAP reflectance measurements are used, the PLSR technique performs better than the CR approach. As well, when the soil property of interest has no well-identified spectral feature, which is the case of clay, the PLSR technique performs better than the CR approach. In this last situation, PLSR is able to find surrogate spectral features that retain satisfactory estimations of the studied soil properties. However, parts of these spectral features remain difficult to explain or relate to area-specific correlations between soil properties, which means that extrapolation to larger pedological contexts must be envisaged with care. In the near future, VNIR/SWIR airborne hyperspectral data processed by the PLSR technique will allow for accurate mapping of clay and CaCO3 contents, which will contribute significantly to the digital mapping of soil properties.  相似文献   

15.
Naturally occurring wetting‐and‐drying cycles often enhance aggregation and give rise to a stable soil structure. In comparatively dry regions, such as large areas of Australia, organic‐matter (OM) contents in topsoils of arable land are usually small. Therefore, the effects of wetting and drying are almost solely reliant on the clay content. To investigate the relations between wetting‐and‐drying cycles, aggregation, clay content, and OM in the Australian environment, an experiment was set up to determine the relative influence of both clay content (23%, 31%, 34%, and 38%) and OM amendments of barley straw (equivalent to 3.1 t ha–1, 6.2 t ha–1, and 12.4 t ha–1) on the development of water‐stable aggregates in agricultural soil. The aggregate stability of each of the sixteen composite soils was determined after one, three, and six wet/dry cycles and subsequent fast and slow prewetting and was then compared to the aggregate stabilities of all other composite soils. While a single wet/dry cycle initiated soil structural evolution in all composite soils, enhancing macroaggregation, the incorporation of barley straw was most effective for the development of water‐stable aggregates in those soils with 34% and 38% clay. Repeated wetting‐and‐drying events revealed that soil aggregation is primarily based on the clay content of the soil, but that large straw additions also tend to enhance soil aggregation. Relative to untreated soil, straw additions equivalent to 3.1 t ha–1 and 12.4 t ha–1 increased soil aggregation by about 100% and 250%, respectively, after three wet/dry cycles and fast prewetting, but were of less influence with subsequent wet/dry cycles. Straw additions were even more effective in aggregating soil when combined with slow prewetting; after three wet/dry cycles, the mean weight diameters of aggregates were increased by 70% and 140% with the same OM additions and by 160% and 290% after six wet/dry cycles, compared to samples without organic amendments. We suggest that in arable soils poor in OM and with a field texture grade of clay loam or finer, the addition of straw, which is often available from preceding crops, may be useful for improving aggregation. For a satisfactory degree of aggregate stability and an improved soil structural form, we found that straw additions of at least 6.2 t ha–1 were required. However, rapid wetting of straw‐amended soil will disrupt newly formed aggregates, and straw has only a limited ability to sustain structural improvement.  相似文献   

16.
以滨海盐土为研究对象,通过添加不同浓度的盐溶液并模拟蒸发过程,获取不同含水、含盐量的土壤样品,并测定土壤光谱和土壤含水量,分别运用光谱指数法和偏最小二乘回归法(PLSR)对土壤含水量进行预测。结果表明：由2027 nm和1878 nm构建的土壤水分差异化光谱指数(NDMI2027,1878)是预测土壤水分的最优指数,且适用于任何等级的盐渍化土壤,其建模集和验证集的预测结果均优于PLSR方法,验证集R2达0.99,RMSE仅为21.84 g/kg,可比较准确地预测盐渍化土壤的含水量。  相似文献   

17.
Methods to quantify organic carbon (OC) in soil fractions of different stabilities often involve time-consuming physical and chemical treatments. The aim of the present study was to test a more rapid alternative, which is based on the spectroscopic analysis of bulk soils in the mid-infrared region (4000-400 cm−1), combined with partial least-squares regression (PLS). One hundred eleven soil samples from arable and grassland sites across Switzerland were separated into fractions of dissolved OC, particulate organic matter (POM), sand and stable aggregates, silt and clay particles, and oxidation resistant OC. Measured contents of OC in each fraction were then correlated by PLS with infrared spectra to obtain prediction models. For every prediction model, 100 soil spectra were used in the PLS calibration and the residual 11 spectra for validation of the models. Correlation coefficients (r) between measured and PLS-predicted values ranged between 0.89 and 0.97 for OC in different fractions. By combining different fractions to one labile, one stabilized and one resistant fraction, predictions could even be improved (r=0.98, standard error of prediction=16%). Based on these statistical parameters, we conclude that mid-infrared spectroscopy in combination with PLS is an appropriate and very fast tool to quantify OC contents in different soil fractions.  相似文献   

18.
The calibration of soil organic C (SOC) and hot water‐extractable C (HWE‐C) from visible and near‐infrared soil reflectance spectra is hindered by the complex spectral interaction of soil chromophores that usually varies from one soil or soil type to another. The exploitation of spectral variables from spectroradiometer data is further affected by multicollinearity and noise. In this study, a set of soil samples (Fluvisols, Podzols, Cambisols and Chernozems; n = 48) representing a wide range of properties was analysed. Spectral readings with a fibre‐optics visible to near‐infrared instrument were used to estimate SOC and HWE‐C contents by partial least squares regression (PLS). In addition to full‐spectrum PLS, spectral feature selection techniques were applied with PLS (uninformative variable elimination, UVE‐PLS, and a genetic algorithm, GA‐PLS). On the basis of normalized spectra (mean centring + vector normalization), the order of prediction accuracy was GA‐PLS ? UVE‐PLS > PLS for SOC; for HWE‐C, it was GA‐PLS > UVE‐PLS, PLS. With GA‐PLS, acceptable cross‐validated (cv) prediction accuracies were obtained for the complete dataset (SOC, , RPDcv = 2.42; HWE‐Ccv, , RPDcv = 2.13). Splitting the soil data into two groups with different basic properties (Podzols compared with Fluvisols/Cambisols; n = 21 and n = 23, respectively) improved SOC predictions with GA‐PLS distinctly (Podzols, , RPDcv = 3.14; Fluvisols/Cambisols, , RPDcv = 3.64). This demonstrates the importance of using stratified models for successful quantitative approaches after an initial rough screening. GA selection frequencies suggest that the spectral region over 1900 nm, and in particular the hydroxyl band at 2200 nm are of great importance for the spectral prediction of both SOC and HWE‐C.  相似文献   

19.
利用多源光谱信息反演宁夏银北地区干湿季土壤含盐量   总被引：1，自引：3，他引：1  
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Rp2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Rp2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。  相似文献   

20.
北京典型耕作土壤养分的近红外光谱分析   总被引：7，自引：2，他引：5  
为研究土壤养分含量分布信息,以从北京郊区一块试验田采集的72个土壤样品为试验材料,应用傅里叶变换近红外光谱技术分析了土样的全氮、全钾、有机质养分含量和pH值。采用偏最小二乘法(PLS)对光谱数据与土壤养分实测值进行回归分析,建立预测模型,以模型决定系数(R2)、校正标准差(RMSECV)、预测标准差(RMSEP)和相对分析误差(RPD)作为模型精度的评价指标。结果表明,利用该模型与光谱数据对土壤全氮、全钾、有机质养分含量和pH值进行预测,结果与实测数据具有较好的一致性,最高决定系数R2达到0.9544。偏最小二乘回归方法建立的养分预测模型能准确地对北京地区褐土土质全氮、有机质、全钾和pH值4种养分进行预测。  相似文献   

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