用于估算伊朗盐土和盐碱土土壤水分特征曲线的土壤传递函数(PTFs)研究

Soil water retention data are essential for irrigation scheduling and determination of irrigation frequency.However,direct measurement of this characteristic is time consuming and expensive and furthermore its spatial and temporal variabilities in field scales increase the number of measurements.Different pedotransfer functions,such as Saxton et al.,Campbell,Vereecken et al.,Rawls and Brakensiek,Wo¨sten et al.,Rajkai et al.,Ghorbani Dashtaki and Homaee,Zacharias and Wessolek,and Rosetta,were evaluated to estimate soil water retention of saline and saline-alkali soils collected from south of Tehran,Iran.The saturation-extract conductivity of all the 68 samples and exchangeable sodium percentage of more than half of them were measured to be greater than 4 dS m-1 and 15%,respectively.The calculated Akaike’s information criterion values showed that Saxton et al.and Campbell models were the best in estimation of soil water retention curve and total available water,respectively.     

用激光衍射法评价有机物和和碳酸盐对土壤团聚的作用

>Aggregation in many soils in semi-arid land is affected by their high carbonate contents.The presence of lithogenic and/or primary carbonates can also inffuence the role of soil organic matter(SOM) in aggregation.The role of carbonates and SOM in aggregation was evaluated by comparing the grain-size distribution in two carbonate-rich soils(15% and 30% carbonates) under conventional tillage after different disaggregating treatments.We also compared the effect of no-tillage and conventional tillage on the role of these two aggregating agents in the soil with 30% of carbonates.Soil samples were treated as four different ways:shaking with water(control),adding hydrochloric acid(HCl) to remove carbonates,adding hydrogen peroxide(H2O2) to remove organic matter,and consecutive removal of carbonates and organic matter(HCl + H2O2),and then analyzed by laser diffraction grain-sizing.The results showed that different contributions of carbonates and SOM to aggregate formation and stability depended not only on their natural proportion,but also on the soil type,as expressed by the major role of carbonates in aggregation in the 15% carbonate-rich soil,with a greater SOC-to-SIC(soil organic C to soil inorganic C) ratio than the 30% carbonate-rich soil.The increased organic matter stocks under no-tillage could moderate the role of carbonates in aggregation in a given soil,which meant that no-tillage could affect the organic and the inorganic C cycles in the soil.In conclusion,the relative role of carbonates and SOM in aggregation could alter the aggregates hierarchy in carbonate-rich soils.     

多尺度土壤入渗特性的变异特征和传递函数构建

土壤入渗特性的变异特征具有明显空间依赖性和尺度效应,其多尺度上的参数估值是农田灌溉设计和管理的重要基础。该研究以在关中平原进行的52组双环入渗试验为基础,通过比较不同方法计算的标定因子对Kostiakov公式的标定效果,结合小波分析和通径分析方法识别并量化分析标定因子和土壤特性参数（土壤机械组成、容重、初始含水率和有机质含量）在多尺度的相关性,在此基础上分别利用多元线性回归（Multiple Linear Regression, MLR）、BP神经网络（BP Artificial Neural Network, BP-ANN）和支持向量机（Support Vector Machine, SVM）3种方法构建估算标定因子的土壤传递函数。结果表明,采用最小二乘法计算标定因子对Kostiakov公式的标定效果最优,所有测点标定后累积入渗量与实测值的均方根误差（Root Mean Square Error, RMSE）、平均偏差（Mean Bias Error, MBE）、相对误差绝对值均值（Mean Absolute Value of Relative Error, MARE）分别为1.83 cm、0.24 cm、21.2%;多尺度条件下,土壤容重、砂粒、黏粒和有机质含量组合是引起研究区域标定因子空间变化的主要变异源,其中标定因子与砂粒和有机质含量呈显著正相关关系（P<0.05）,总通径系数分别为0.78和0.65,与黏粒和土壤容重呈显著负相关关系（P<0.05）,总通径系数分别为−0.74和−0.68;采用SVM法构建估算标定因子的土壤传递函数精度最高,其验证集所得入渗量估算值与实测值具有较高的一致性,两者间的RMSE、MBE和MARE分别为1.92 cm、0.05 cm和27.6%,说明SVM法可用于构建估算标定因子的土壤传递函数。研究结果有助于揭示多尺度上土壤入渗特性的变异特征和解决入渗参数难以快速获取的问题。     

基于多光谱数据的荒漠矿区土壤有机质估算模型

目前运用高光谱数据估算土壤有机质的模型精度已经可以达到精准农业的要求,但其数据的整理和运算过程较为复杂且观测尺度较小.为节省资源,提高效率并为多光谱遥感估算土壤有机质积累经验,该文将Landsat8_OLI多光谱遥感影像各波段的反射率数据与地面土壤有机质SOM(soil organic matter)实测数据相结合,利用SPSS软件及多元线性回归分析方法建立基于反射率R、反射率倒数1/R、反射率倒数对数LN(1/R)、反射率一阶导数FDR(first derivative reflectance)的土壤有机质定量估算模型,精度检验后择取最优模型通过多光谱遥感波段运算的方式推广至整个研究区.结果表明:FDR模型的精度更高,RMSE为0.215,F检验结果为4.072,预测值与实际值之间的决定系数R2为0.963.基于该模型估算研究区空间范围的土壤有机质含量,得出土壤有机质含量在0～5 g/kg之间的面积占总研究区的84.065％,＞10 g/kg的面积仅仅为0.001 5％.在4种土地类型中工矿用地SOM平均含量为最高的7.35 g/kg,受开采的煤炭中有机质影响较大.裸地面积2 674.44 km2,占研究区面积的63％,SOM平均含量6.12 g/kg;盐渍地和荒漠林地SOM含量偏低.总之,运用多光谱遥感数据估算干旱区土壤有机质的方法可行,也为遥感估算其他地表参数提供参考.     

番茄形态结构模型参数的多目标拟合估算方法研究

对番茄生长模型参数进行准确估算是模型得以验证和面向应用的前提条件。基于番茄拓扑结构,采用非线性最小二乘优化方法,通过对植株形态数据的多目标拟合,实现模型参数的最优估计和校准。非线性最小二乘法的权值根据目标数据自动调整,使得不同量纲和不同数值范围的观测数据可以在同一水平上进行,提高了计算的精度和稳定性;另一方面用差商近似代替导数加快了算法的收敛速度。通过应用拟合优度检验,得出模型输出对观测值的拟合程度较好,说明模型合理,参数可靠。实践表明该估算方法是对模型进行验证的一个行之有效的方法。     

基于GIS和多目标评价方法的果树适宜性评价

漳州地区是福建省乃至全国有名的水果之乡,地貌复杂,自然资源与生态环境差异显著。为了科学合理地利用自然资源,对该地区进行果树适宜性综合评价,分析其种植现状与利用潜力,提供科学决策依据。首先建立研究区域内土壤、气候与地形等数据库,并利用地形对气候分布状况进行校正,同时通过LandsatTM遥感影像的分析解译得到研究区域内的土地利用现状分布图和主要果树种植分布图,在此基础上采用GIS和多目标评价(MCE)方法对漳州地区三种主要果树(香蕉、荔枝和龙眼)进行适宜性评价,最后综合分析这些果树适宜分布现状与利用潜力。研究结果表明,漳州地区大部分区域都非常适宜种植这三种水果,发展水果生产潜力较大。     

基于CWSI和土壤水分修正系数的冬小麦田土壤含水量估算

2000年10月～2001年7月在西北农林科技大学灌溉实验站冬小麦田内,进行了利用作物冠层温度定量估算冬小麦田土壤含水量的试验研究。根据水分亏缺条件下作物蒸发蒸腾量计算公式及作物水分胁迫指标(CWSI)的定义,得到了基于CWSI和土壤水分修正系数Ks的不同生育阶段冬小麦田土壤含水量估算公式,其中Ks采用了康绍忠的幂函数形式(KM)及Dooreboos的线性公式(DM)。用该公式对冬小麦田土壤含水量在4个生育阶段进行了估算,并对估算值和实测值进行对比和误差分析。分析结果表明:在出苗越冬期和越冬返青期KM和DM模型对土壤水分的估算偏高,其原因是由于麦田土地裸露导致CWSI观测中出现了较大误差;在返青抽穗开花期和灌浆成熟期KM模型估算冬小麦根层土壤含水量较适宜,误差在15%以内,DM模型误差较大达到30%。     

基于机器视觉的旱田多目标直线检测方法

在实际应用中,由于摄像头安装的高度不同或者车辆在地面高低不平的农田内行驶时产生的晃动,都会产生摄像头内出现多作物行的现象。因此根据农田图像的特点,提出了基于已知线的方法判断农作物列数,避免了传统算法只有先确定农作物列数才能提取导航线的弊端。针对农田图像中多列目标检测问题,采用了基于水平线扫描的归类算法,并利用改进的Hough变化快速检测多条定位线。试验结果表明,处理一幅720×480像素彩色图像平均消耗时间为258 ms,98%的图像中所有目标直线都可以检测出来。该算法能够准确提取各种天气环境下农田图像中的列信息,确定多条定位线的方位。     

地(市)级土壤图的编制技术程序和方法

地(市)级土壤图是在县级土壤普查成果图的基础上,经过若干制图技术处理缩编而成的中比例尺土壤图(1:10万至1:25万),是反映地(市)级土壤普查成果的重要内容。     

基于改进水云模型和Radarsat-2数据的农田土壤含水量估算

为了直接将雷达遥感中"水云模型"进行反演应用,该研究将"水云模型"中植被参数改为雷达植被指数,利用全极化数据直接支持遥感反演土壤含水量,无需遥感反演植被参数输入。改进模型为利用雷达遥感结合"水云模型"进行土壤含水量监测提供了一种高效便捷方法。基于Radarsat-2全极化数据对冬小麦覆盖的农田土壤含水量进行估算,利用2014年在陕西杨凌区获取的4个生育期内Radarsat-2卫星数据及同步田间测量108组冬小麦农田土壤含水量地面测量数据进行模型参数校正和精度验证。验证结果精度为:改进的雷达植被指数模型原叶面积指数模型(实测叶面积指数验证)原叶面积指数模型(光学遥感反演叶面积指数验证),且改进的雷达植被指数模型可以在多个生育期内对农田土壤含水量进行监测。     

Improvement in estimation of soil water retention using fractal parameters and multiobjective group method of data handling

Soil water retention characteristic is required for modeling of water and substance movement in unsaturated soils and need to be estimated using indirect methods. Point pedotransfer functions (PTFs) for prediction of soil water content at matric suctions of 1, 5, 25, 50, and 1500 kPa were developed and validated using a data-set of 148 soil samples from Hamedan and Guilan provinces, Iran, by multiobjective group method of data handling (mGMDH). In addition to textural and structural properties, fractal parameters of the power-law fractal models for both particles and aggregates distributions were also included as predictors. Their inclusion significantly improved the PTFs’ accuracy and reliability. The aggregate size distribution fractal parameters ranked next to the particle size distribution (PSD) in terms of prediction accuracy. The mGMDH-derived PTFs were significantly more reliable than those by artificial neural networks but their accuracies were practically the same. Similarity between the fractal behavior of particle and void size distributions may contribute to the improvement of the derived PTFs using PSD fractal parameters. It means that both distributions of the pore and particle size represent the fractal behavior and can be described by fractal models.     

基于土壤传递函数的土壤持水特性间接估计及其空间变异性研究

A total of 107 soil samples were taken from the city of Qingdao,Shandong Province,China.Soil water retention data at 2.5,6,10,33,100,300,and 1 500 kPa matric potentials were measured using a pressure membrane apparatus.Multiple linear regression (MLR) was used to develop pedotransfer functions (PTFs) for single point estimation and van Genuchten parameter estimation based on readily measurable soil properties,i.e.,MLR-based point (MLRP) PTF and MLR-based parametric (MLRV) PTF.The double cross-validation method was used to evaluate the accuracy of PTF estimates and the stability of the PTFs developed in this study.The performance of MLRP and MLRV PTFs in estimating water contents at matric potentials of 10,33,and 1 500 kPa was compared with that of two existing PTFs,the Rawls PTF and the Vereecken PTF.In addition,geostatistical analyses were conducted to assess the capabilities of these PTFs in describing the spatial variability of soil water retention characteristics.Results showed that among all PTFs only the Vereecken PTF failed to accurately estimate water retention characteristics.Although the MLRP PTF can be used to predict retention characteristics through traditional statistical analyses,it failed to describe the spatial variability of soil water retention characteristics.Although the MLRV and Rawls PTFs failed to describe the spatial variability of water contents at a matric potential of 10 kPa,they can be used to quantify the spatial variability of water contents at matric potentials of 33 and 1 500 kPa.     

Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity

Modeling water flow and solute transport in vadose zone requires knowledge of soil hydraulic properties, which are water retention and hydraulic conductivity curves. As an alternative to direct measurement, indirect determination of these functions from basic soil properties using pedotransfer functions (PTFs) has attracted the attention of researchers in a variety of fields such as soil scientists, hydrologists, and agricultural and environmental engineers. In this study, PTFs for point and parametric (van Genuchten's parameters) estimation of soil hydraulic parameters from basic soil properties such as particle-size distribution, bulk density, and three different pore sizes were developed and validated using artificial neural network (ANN) and multiple-linear regression methods and the predictive capabilities of the two methods was compared using some evaluation criteria. Total of 195 soil samples was divided into two groups as 130 for the development and 65 for the validation of PTFs. Although the differences between the two methods were not statistically significant (p > 0.05), regression predicted point and parametric variables of soil hydraulic parameters better than ANN. Both methods had lower accuracy in parametric predictions than in point predictions. Accuracy of the predictions was evaluated by the coefficient of determination (R²) and the root mean square error (RMSE) between the measured and predicted parameter values. The R² and RMSE varied from 0.637 to 0.979 and from 0.013 to 0.938 for regression, and varied from 0.444 to 0.952 and from 0.020 to 3.511 for ANN, respectively. Even though regression performs insignificantly better than ANN in this case, ANN produces promising results and its advantages can be utilized by developing or using new algorithms in future studies.     

根据土壤粒径分形估计紫色土水分特征曲线

选择不同质地紫色土,分别根据土壤颗粒的数量和重量分布计算土壤粒径分形维数,并与Tyler和Wheatcraft土壤水分特征曲线模型的拟合分形维数进行比较分析。结果表明,三种途径获得的分形维数与土壤质地密切相关,均表现出土壤黏粒含量越高,质地越细,分形维数越高。土壤粒径数量分布分形维数(2.98～3.26)和土壤粒径重量分布分形维数(2.73～2.81)存在较大的差异,但二者间却存在显著的线性关系。土壤粒径分形维数与土壤水分特征曲线模型拟合分形维数(2.72～2.84)均存在显著的线性关系,尤其是土壤粒径重量分布分形维数与土壤水分特征曲线模型拟合分形维数数值十分接近。通过建立的土壤水分特征曲线模型分形维数与土壤粒径分布分形维数关系式,结合Tyler和Wheatcraft模型进行土壤水分特征曲线预测,预测值与实测值具有良好的一致性,因而根据土壤粒径分形预测紫色土水分特征曲线是可行的。     

鲁中南山地典型植被土壤颗粒与土壤水分特征曲线的分形学特征

土壤粒径分布与水分特征曲线是土壤的重要物理性质,对土壤侵蚀状况和土壤肥力有显著影响。以鲁中南山地典型植被下土壤为研究对象,运用分形学理论研究5种典型植被土壤颗粒与水分特征曲线的分形学特征。结果表明:1)不同植被类型的土壤颗粒单重分形维数、多重分形参数和土壤水分特征曲线分形维数具有显著差异,均表现为麻栎+刺槐混交林>黑松+黄连木混交林>黑松林>核桃林>荒草地;2)土壤分形维数有林地大于荒草地,混交林大于纯林;3)土壤分形维数与黏粒体积分数、粉粒体积分数呈显著正相关,与砂粒体积分数呈显著负相关;4)土壤颗粒单重分形维数与土壤水分特征曲线分形维数呈显著的正相关关系。说明土壤颗粒分布与土壤水分特征曲线的分形参数可以作为反映土壤结构性状变化的定量指标,可利用土壤颗粒分形与土壤水分特征曲线分形维数的相关关系来描述对应的土壤水分特征曲线。研究成果可为鲁中南山地退耕还林与生态造林工程建设及其生态效益评价提供理论参考。