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1.
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.  相似文献   

2.
Based on legacy soil data from a soil survey conducted recently in the traditional manner in Hong Kong of China, a digital soil mapping method was applied to produce soil order information for mountain areas of Hong Kong. Two modeling methods (decision tree analysis and linear discriminant analysis) were used, and their applications were compared. Much more eflort was put on selecting soil covariates for modeling. First, analysis of variance (ANOVA) was used to test the variance of terrain attributes between soil orders. Then, a stepwise procedure was used to select soil covariates for linear discriminant analysis, and a backward removing procedure was developed to select soil covariates for tree modeling. At the same time, ANOVA results, as well as our knowledge and experience on soil mapping, were also taken into account for selecting soil covariates for tree modeling. Two linear discriminant models and four tree models were established finally, and their prediction performances were validated using a multiple jackknifing approach. Results showed that the discriminant model built on ANOVA results performed best, followed by the discriminant model built by stepwise, the tree model built by the backward removing procedure, the tree model built according to knowledge and experience on soil mapping, and the tree model built automatically. The results highlighted the importance of selecting soil covariates in modeling for soil mapping, and suggested the usefulness of methods used in this study for selecting soil covariates. The best discriminant model was finally selected to map soil orders for this area, and validation results showed that thus produced soil order map had a high accuracy.  相似文献   

3.
Soil salinity and hydrologic datasets were assembled to analyze the spatio-temporal variability of salinization in Fengqiu County, Henan Province, China, in the alluvial plain of the lower reaches of the Yellow River. The saline soil and groundwater depth data of the county in 1981 were obtained to serve as a historical reference. Electrical conductivity (EC) of 293 surface soil samples taken from 2 km × 2 km grids in 2007 and 40 soil profiles acquired in 2008 was analyzed and used for comparative mapping. Ordinary kriging was applied to predict EC at unobserved locations to derive the horizontal and vertical distribution patterns and variation of soil salinity. Groundwater table data from 22 observation wells in 2008 were collected and used as input for regression kriging to predict the maximum groundwater depth of the county in 2008. Changes in the groundwater level of Fengqiu County in 27 years from 1981 to 2008 was calculated. Two quantitative criteria, the mean error or bias (ME) and the mean squared error (MSE), were computed to assess the estimation accuracy of the kriging predictions. The results demonstrated that the soil salinity in the upper soil layers decreased dramatically and the taxonomically defined saline soils were present only in a few micro-landscapes after 27 years. Presently, the soils with relatively elevated salt content were mainly distributed in depressions along the Yellow River bed. The reduction in surface soil salinity corresponded to the locations with deepened maximum groundwater depth. It could be concluded that groundwater table recession allowed water to move deeper into the soil profile, transporting salts with it, and thus played an important role in reducing soil salinity in this region. Accumulation of salts in the soil profiles at various depths below the surface indicated that secondary soil salinization would occur when the groundwater was not controlled at a safe depth.  相似文献   

4.
基于土壤剖面测定数据计算中国土壤有机碳贮量   总被引：10，自引：0，他引：10  
Soil organic carbon (SOC) storage under different types of vegetations in China were estimated using measured data of 2 440 soil profiles to compare SOC density distribution between different estimates, to map the soil organic carbon stocks under different types of vegetation in China, and to analyze the relationships between soil organic carbon stocks and environmental variables using stepwise regression analyses. Soil organic carbon storage in China was estimated at 69.38 Gt (1015 g). There was a big difference in SOC densities for various vegetation types, with SOC distribution closely related to climatic patterns in general. Stepwise regression analyses of SOC against environmental variables showed that SOC generally increased with increasing precipitation and elevation, while it decreased with increasing temperature. Furthermore, the important factor controlling SOC accumulation for forests was elevation, while for temperate steppes mean annual temperature dominated. The more specific the vegetation type used in the regression analysis, the greater was the effect of environmental variables on SOC. However, compared to native vegetation, cultivation activities in the croplands reduced the influence of environmental variables on SOC.  相似文献   

5.
土壤水分特征曲线的分形模拟   总被引：17，自引：0，他引：17  
Many empirical models have been developed to describe the soil water retention curve （SWRC）. In this study, a fractal model for SWRC was derived with a specially constructed Menger sponge to describe the fractal scaling behavior of soil; relationships were established among the fractal dimension of SWRC, the fractal dimension of soil mass, and soil texture; and the model was used to estimate SWRC with the estimated results being compared to experimental data for verification. The derived fractal model was in a power-law form, similar to the Brooks-Corey and Campbell empirical functions. Experimental data of particle size distribution （PSD）, texture, and soil water retention for 10 soils collected at different places in China were used to estimate the fractal dimension of SWRC and the mass fractal dimension. The fractal dimension of SWRC and the mass fractal dimension were linearly related. Also, both of the fractal dimensions were dependent on soil texture, i.e., clay and sand contents. Expressions were proposed to quantify the relationships. Based on the relationships, four methods were used to determine the fractal dimension of SWRC and the model was applied to estimate soil water content at a wide range of tension values. The estimated results compared well with the measured data having relative errors less than 10% for over 60% of the measurements. Thus, this model, estimating the fractal dimension using soil textural data, offered an alternative for predicting SWRC.  相似文献   

6.
Several methods,including stepwise regression,ordinary kriging,cokriging,kriging with external drift,kriging with varying local means,regression-kriging,ordinary artificial neural networks,and kriging combined with artificial neural networks,were compared to predict spatial variation of saturated hydraulic conductivity from environmental covariates.All methods except ordinary kriging allow for inclusion of secondary variables.The secondary spatial information used was terrain attributes including elevation,slope gradient,slope aspect,profile curvature and contour curvature.A multiple jackknifing procedure was used as a validation method.Root mean square error (RMSE) and mean absolute error (MAE) were used as the validation indices,with the mean RMSE and mean MAE used to judge the prediction quality.Prediction performance by ordinary kriging was poor,indicating that prediction of saturated hydraulic conductivity can be improved by incorporating ancillary data such as terrain variables.Kriging combined with artificial neural networks performed best.These prediction models made better use of ancillary information in predicting saturated hydraulic conductivity compared with the competing models.The combination of geostatistical predictors with neural computing techniques offers more capability for incorporating ancillary information in predictive soil mapping.There is great potential for further research and development of hybrid methods for digital soil mapping.  相似文献   

7.
A number of optical sensing tools are now available and can potentially be used for refining need-based fertilizer nitrogen (N) topdressing decisions.Algorithms for estimating field-specific fertilizer N needs are based on predictions of yield made while the crops are still growing in the field.The present study was conducted to establish and validate yield prediction models using spectral indices measured with proximal sensing using GreenSeeker canopy reflectance sensor,soil and plant analyzer ...  相似文献   

8.
基于不同地表曲面模型预测土壤有机碳含量   总被引：1，自引：0，他引：1  
Local terrain attributes,which are derived directly from the digital elevation model,have been widely applied in digital soil mapping.This study aimed to evaluate the mapping accuracy of soil organic carbon (SOC) concentration in 2 zones of the Heihe River in China,by combining prediction methods with local terrain attributes derived from different polynomial models.The prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice,rather than how morphometric variables and their geomorphologic interpretations are understood and calculated.In this study,2 neighborhood types (square and circular) and 6 representative algorithms (Evans-Young,Horn,Zevenbergen-Thorne,Shary,Shi,and Florinsky algorithms) were applied.In general,35 combinations of first-and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods (i.e.,kriging with an external drift and geographically weighted regression).The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the topography.Among the different combinations of first-and second-order derivatives used,there was a best combination with a more accurate estimate.For different prediction methods,the relative improvement in the two zones varied between 0.30％ and 9.68％.The SOC maps resulting from the higher-order algorithms (Zevenbergen-Thorne and Florinsky) yielded less interpolation errors.Therefore,it was concluded that the performance of predictive methods,which incorporated auxiliary variables,could be improved by attempting different terrain analysis algorithms.  相似文献   

9.
中国禹城土壤盐渍化的时空变异及其预测   总被引：5，自引：0，他引：5  
This research used both geostatistics and GIS approach to compare temporal change of soil salt between 1980 and 2003, to analyze the spatial distribution of surface soil salt, to developed methods for predicting soil salinization potential based on recent improvements to the Dempster-Shafer theory, and to develop probability maps of potential salinization in Yucheng City, China. A semivariogram model of soil salt content was developed from the spherical model, and then employing kriging interpolation the spatial distribution of salt content in 2003 was obtained utilizing data from 100 soil sampling points. Potential salinization distribution was mapped using an approach that integrated soil data of the second general survey in 1980 in Yucheng City, which included groundwater salinity, groundwater depth, soil texture, soil organic matter content, and geomorphic maps. With the support of Dempster-Shafer theory and fuzzy set technique the factors that affected potential soil salinization were characterized and integrated;and then soil salinization was predicted. Finally a prognosis map of potential salinization distribution in the research area was obtained, with higher probability values indicating higher hazards to salinity processes. The distribution of the potential soil salinization probability was a successive surface.  相似文献   

10.
伊朗一些石灰性土壤中锌解吸动态研究   总被引：1，自引：0，他引：1  
Desorption of zinc (Zn) from soil is an important factor governing Zn concentration in the soil solution and Zn availability to plants. Batch experiments were performed to study the kinetics of Zn desorption by diethylenetriaminepentaacetic acid (DTPA) from 15 calcareous soil samples taken from Golestan Province in northern Iran. Soils were equilibrated with 0.005 mol L-1 DTPA solutions for 0.25 to 192 h. The results showed that the extraction process consisted of rapid extraction in the first 2 h followed by much slower extraction for the remainder of the experiment. Desorption kinetic data was fitted to pseudo-first-order kinetic model. The experimental data were found to deviate from the straight line of the pseudo-first-order plots after 2 h. The model of two first-order reactions was fitted to the kinetic data and allowed to distinguish two pools for Zn: a labile fraction (Q1 ), quickly extracted with a rate constant k1 , and a slowly labile fraction (Q2 ), more slowly extracted with a rate constant k2 . The applicability of pseudo-second-order model in describing the kinetic data of Zn desorption was also evaluated.  相似文献   

11.
基于盲源分离的稀疏植被区土壤含盐量反演   总被引：1，自引：0，他引：1  
植被对土壤光谱的干扰是目前土壤盐渍化遥感监测的重要限制因素之一,探索消除稀疏植被覆盖区植被对光谱影响的方法,对提高土壤含盐量遥感反演精度具有重要意义。本文通过对189组不同植被覆盖度且不同盐渍化程度种植微区野外实测地表可见-近红外反射光谱进行分析,比较并评价了基于原始光谱和盲源分离(blind source separation,BSS)后光谱预测土壤含盐量的结果。结果表明:地表植被覆盖严重影响基于可见-近红外反射光谱的土壤含盐量反演精度。盲源分离方法,尤其是基于方程z=tanh(y)的独立分量分析(independent components analysis,ICA)算法,可有效分解植被和土壤的混合光谱,并提高植被覆盖下基于可见-近红外反射光谱的土壤含盐量反演精度。该方法为植被覆盖区大尺度土壤盐渍化遥感监测提供了方法指导。  相似文献   

12.
基于光谱指数优选的土壤盐分定量光谱估测   总被引：4，自引：1，他引：3  
[目的]探索基于光谱指数的盐渍土盐分估测的最佳技术路线,为研究区土壤盐分定量、快速遥感监测提供理论基础和技术参考。[方法]以山东省垦利县为研究区,野外采样,获取盐分及其主要离子(Cl-,Na+,Ca2+)含量及高光谱数据;然后采用2种思路:(1)先选取敏感波段,进而构建常见的5种光谱指数;(2)先任意两波段组合构建光谱指数,进而筛选敏感光谱指数。最后皆采用随机森林方法(random forest,RF)构建土壤盐分及其主要离子的光谱模型。[结果]基于筛选的敏感亮度指数(1 750,1 620nm)的RF模型精度最高,作为研究区土壤盐分的最佳估测模型,亮度指数作为最佳光谱指数;思路(2)明确的特征光谱范围涵盖思路(1)筛选的敏感波段,更有利于光谱特征分析;思路(2)建模的结果明显优于思路(1);确定最佳技术路线为:任意波段两两组合构建光谱指数后,利用相关分析筛选土壤盐分及其主要离子的敏感光谱指数,进而构建其RF模型。[结论]该技术路线适用于黄河三角洲地区土壤盐渍化信息的有效提取。  相似文献   

13.
土壤盐渍化是导致土壤退化和生态系统恶化的主要原因之一,对干旱区的可持续发展构成主要威胁。为了尽可能精确地监测土壤盐渍化的空间变异性,该研究收集新疆艾比湖湿地78个典型样点,其中选取54个样本作为训练集,24个样本作为独立验证集。基于Sientinel-2 多光谱传感器（Multi-Spectral Instrument,MSI）、数字高程模型（Digital Elevation Model,DEM）数据提取3类指数（红边光谱指数、植被指数和地形指数）,经过极端梯度提升（Extreme Gradient Boosting,XGBoost）算法筛选有效特征变量,构建了关于土壤电导率（Electrical Conductivity,EC）的随机森林（Random Forest,RF）、极限学习机（Extra Learning Machine,ELM）和偏最小二乘回归（Partial Least Squares Regression,PLSR）预测模型,并选择最优模型绘制了艾比湖湿地盐渍化分布图。结果表明：优选的红边光谱指数基本能够预测EC的空间变化;红边光谱指数与植被指数组合建模效果总体上优于其与地形指数的组合,3类指数组合的建模取得了较为理想的预测精度,其中RF模型表现最优（验证集R2=0.83,RMSE=4.81 dS/m,RPD=3.11）;在整个研究区内,中部和东部地区土壤盐渍化程度尤为严重。因此,XGBoost所筛选出的环境因子结合机器学习算法可以实现干旱区土壤盐渍化的监测。  相似文献   

14.
快速、无损地估算盐生植物叶片盐离子含量在植物生长监测、耐盐植物筛选和土壤盐渍化监测等方面有实用价值。该研究以新疆艾比湖保护区内盐生植物为研究对象,通过分析植物叶片盐离子(K~+、Na~+、Ca~(2+)、Mg~(2+))含量与冠层高光谱数据的光谱变换和二维植被指数(比值型植被指数(ratiovegetationindex,RVI)、差值型植被指数(difference vegetation index,DVI)、归一化型植被指数(normalized difference vegetation index,NDVI))的相关性选取特征波段,构建基于地理加权回归模型(geographically weighted regression,GWR)的叶片盐离子含量估算模型,并与BP神经网络模型(back propagation neural network)进行对比,研究基于GWR模型估算干旱区盐生植物叶片盐离子的可行性。结果表明,选取特征波段集中表现在红及短波红外波段:K~+含量在反射率倒数的对数选取的红光区域内波段使用GWR估算效果最佳;Na~+的特征波段在光谱变换下集中于短波红外区域,二维植被指数集中在近红外、短波近红外及黄、橙、红区域,各种波段选取下GWR对Na~+的含量估算均有较好效果,但反射率对数的一阶估算效果最好;Ca~(2+)含量在反射率平方根的一阶微分下选取的短波红外波段通过GWR模型估算效果最好;Mg~(2+)含量在DVI选取的位于红光区域特征波段估算效果最佳,但使用GWR模型对Mg~(2+)的估算精度不及BP模型。分析基于GWR盐离子模型估算模型发现,含量较高的离子估算效果更好,K~+、Na~+的模型精度优于Ca~(2+)、Mg~(2+)。在使用GWR模型估算植物叶片盐离子含量时,特征波段均指向红及短波红外波段,符合植被光谱机理的响应。  相似文献   

15.
以博斯腾湖湖滨绿洲为研究区,对土壤高光谱反射率R进行数学光谱变换,并计算其差值型、比值型、归一化型3种盐分指数,通过显著性检验优选特征波段,结合土壤表层盐分实验数据,构建基于地理加权回归模型的土壤表层盐分含量估算模型。研究结果表明:1)土壤表层盐分含量平均值为7.535 g·kg-1,其光谱变换建模选取的特征波段集中在466~482、1669~1728、1979~2371 nm,其中对数倒数的一阶微分(1/lg R)′相关性较好,相关系数绝对值为0.672;2)构建3种盐分指数优选的特征波段集中在1700~1728、1992~2014、2375~2405 nm,建立的模型决定系数均大于0.870,光谱反射率R的决定系数仅为0.621;3)差值型盐分指数优选特征波段建立的地理加权回归模型为最优模型,建模集与检验集的决定系数R2分别为0.934和0.915,RMSE分别为1.186和0.917。  相似文献   

16.
基于环境变量的渭干河-库车河绿洲土壤盐分空间分布   总被引：5，自引：4，他引：1  
土壤属性的数字制图对精准农业生产和环境保护治理至关重要。为了在大尺度上尽可能精确的监测土壤盐分空间变异性,该文使用普通克里格(ordinary kriging,OK)、地理加权回归(geographically weighted regression,GWR)和随机森林(random forest,RF)方法,结合地形、土壤理化性质和遥感影像数据等16个环境辅助变量,绘制渭干河-库车河绿洲表层土壤盐分分布图。基于决定系数(R^2)、均方根误差(RMSE)和平均绝对误差(MAE)验证模型精度。结果表明:不同方法预测的盐分分布趋势没有显著差异,大体上从研究区的西北向东南部方向增加;结合辅助变量的不同预测方法中,RF方法预测精度最高,R^2为0.74,RMSE和MAE分别为9.07和7.90 mS/cm,说明该模型可以有效地对区域尺度的土壤盐分进行定量估算;RF方法对电导率(electric conductivity,EC)低于2 mS/cm时预测精度最高,RMSE为3.96 mS/cm,很好的削弱了植被覆盖对电导率EC的影响。  相似文献   

17.
土壤含水量（soil water content, SWC）和土壤含盐量（soil salt content, SSC）是影响作物生长和农业生产力的重要因素。光学卫星图像已成为SWC和SSC估计的主要数据源。然而,在SWC或SSC变化较大地区,土壤水分和盐分会影响对方对光谱反射率的响应,使得SSC和SWC的反演精度较差。对此,该研究提出了一个半解析性的反射率模型—RVS模型,来模拟植被光谱反射率（Rv）对作物根区土壤含水量和含盐量的响应;并通过构建的RVS模型,对植被覆盖区域的土壤含水量和土壤含盐量进行同步监测。研究表明：RVS模型在反演研究区土壤含盐量和含水量时,精度较为可靠(水分：决定系数R2为0.63～0.74,均方根误差为0.017～0.028;盐分：决定系数R2为0.68～0.75,均方根误差为0.0525～0.0617)。在作物生长过程中,植被光谱反射率对深层土壤的含水量和含盐量的响应比对浅层土壤的含水量和含盐量的响应更加明显,而且随着作物的生长,影响光谱反射率的主导因素从土壤水分慢慢转向土壤盐分和水盐相互作用。该研究在一定程度上揭示了土壤水分、盐分、水盐交互作用对作物光谱反射率的干扰过程,实现土壤水分和盐分的同步监测,对实现区域尺度上土壤含盐量和含水量的精准监测具有一定的意义。  相似文献   

18.
覆膜对无人机多光谱遥感反演土壤含盐量精度的影响   总被引：2，自引：2，他引：0  
快速、准确地获取农田土壤盐分含量对指导合理灌溉及盐渍土的治理有重要意义。该文以内蒙古河套灌区沙壕渠灌域内的覆膜耕地为研究对象,利用无人机多光谱相机获取研究区内5月和6月的多光谱遥感数据,并同步采集区域内表层土壤含盐量数据,研究覆膜对无人机多光谱遥感图像反演农田土壤盐分含量精度的影响。利用支持向量机(support vector machine,SVM)、反向传播神经网络(back propagation neural network,BPNN)和极限学习机(extreme learning machine,ELM)3种机器学习方法,分别构建去膜前后基于原始光谱反射率和优选光谱指数的土壤含盐量估算模型。结果表明,去膜前后的各模型均可有效估测土壤盐分含量,但基于去膜处理后的数据构建的盐分含量估算模型精度较不去膜处理的有所提升,同时,基于光谱指数构建的盐分含量估算模型精度比基于光谱反射率构建的模型精度高;利用ELM构建的盐分含量估算模型在6月份预测效果最佳,其中基于光谱反射率和光谱指数的建模R2和RMSE分别为0.695、0.663和0.182、0.191,验证R2和RMSE分别为0.717、0.716和0.171、0.169。研究结果可为无人机多光谱遥感估算覆膜状态下的农田土壤盐分含量提供参考。  相似文献   

19.
环境敏感变量优选及机器学习算法预测绿洲土壤盐分   总被引：10，自引：5，他引：5  
基于机器学习预测干旱区(如新疆)土壤盐分的研究目前较少涉及且敏感变量的筛选还需深入探讨。该研究比较5种机器学习算法(套索算法,The Least Absolute Shrinkage and Selection Operator-LASSO;多元自适应回归样条函数,Multiple Adaptive Regression Splines-MARS;分类与回归树,Classification and Regression Trees-CART;随机森林,Random Forest-RF;随机梯度增进算法,Stochastic Gradient Treeboost-SGT)在3个不同地理区域(奇台绿洲,渭-库绿洲和于田绿洲)的性能表现;参与的变量被分为6组:波段,植被相关变量集,土壤相关变量集,数字高程模型(digital elevation model,DEM)衍生变量集,全变量组,优选变量组(全变量组经过算法筛选后的变量集合)。通过算法筛选,以示不同研究区的盐度敏感变量。同时借助以上述6组结果评判算法的性能。结果表明:综合分析6个变量组的R2和RMSE,预测精度排名如下:优选变量组植被指数变量组土壤相关变量组波段DEM衍生变量组。由于结果不稳定,全变量组未参与排名。在所有变量中,植被指数(EEVI,ENDVI,EVI2,CSRI,GDVI)和土壤盐度指数(SIT,SI2和SAIO)与土壤盐度相关性高于其他变量。综合评价以上5种算法,Lasso和MARS的预测结果出现极端异常值,但其预测结果能基本呈现土壤盐分空间分布格局。CART的结果能清晰分辨灌区和非灌区土壤盐分的分布态势,但二者内部并无太多变化且稳定性较差。RF和SGT的结果显示,二者在3个绿洲的土壤盐分值域范围和土壤盐分空间分布格局相似,纹理信息相对其他3个算法更为丰富。更为重要的是,算法在各个地区的结果都较为稳定。二者相比,SGT验证精度相对最高,其次为RF。  相似文献   

20.
红外光谱指数反演大田冬小麦覆盖度及敏感性分析   总被引：5，自引：2，他引：5  
植被的覆盖度能反映植被对光的截获、指示植物的生物产量等。常用的红光/近红外构成的植被指数能指示作物覆盖度，但它们易受到不确定因素的影响，估测结果往往偏差较大。该文以冬小麦为例，研究了利用近红外和短波红外光谱指数估测覆盖度的可行性，并评价了这些指数对品种、肥水处理和叶色的敏感性。试验中对冬小麦用数码相机垂直成像获取照片，利用分类算法自动提取覆盖度。根据同步获取的冬小麦光谱特征，构造了56个红外比值和28个红外归一化光谱指数，并选取了8个基于红光近红外的植被指数，利用通用线性模型(GLM)评价它们对覆盖度的预测能力及敏感性分析。结果表明，短波红外光谱指数R1690/R1450，R1450/R1690及(R1450-R1690)/(R1450+R1690)等不易受品种，肥水管理及叶色的影响，能很好地预测大田冬小麦覆盖度。  相似文献   

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