县域尺度下样点距离和土壤分类对农田土壤细菌多样性的影响

样点空间距离和土壤分类粒度是衡量土壤样点代表性和调查精度的重要指标.在江苏省常熟市稻麦轮作区考虑不同土壤分类粒度,按村、镇、县域3种空间尺度选择8个农田样区,每个农田样区选择4个相邻田块作为采样区,每个采样区按梅花状采集表层土壤混合样品,合计32个.利用高通量测序分析技术,研究土壤细菌多样性在不同样点空间距离和土壤分类...     

Comparing mapping approaches at subcatchment scale in northern Thailand with emphasis on the Maximum Likelihood approach

This study compares different soil mapping approaches in three different petrographic areas in order to test their suitability for regional mapping in northern Thailand. Sampling was based on transects or grid-based randomization. Maps were created based on expert knowledge (eye fitting) or using Classification Tree (CART algorithm) or the Maximum Likelihood approach. In addition, local knowledge-based-soil maps were created. Validation was performed using soil reference maps and independent sampling points. The mapping approaches based on transects and grid-based randomization showed a very high correspondence with the respective reference soil map and a very high degree of matching with independent sampling points. Both methods are best suited for sub-watershed scale. Mapping larger areas is difficult due to the inaccessibility of the mountainous regions. The soil maps based on Maximum Likelihood showed a high correlation with the respective reference soil maps and the individual sampling points. Maximum Likelihood maps and Classification Tree maps showed similar levels of accuracy. The Maximum Likelihood approach is applicable to upscaling procedures; therefore, a calibration area is required which represents the target area. Local knowledge-based-soil mapping is very cheap and fast, but is restricted to village areas where classification often varies even within a village. Despite this, local knowledge is very useful for soil reconnaissance surveys, as well as to acquire an overview of the major distribution of soils and their properties. Upscaling of local knowledge due to its inherent inconsistency is not realistic.     

Small-scale spatial variability of selected soil biological properties

A strategy for sampling soil from intact monolith lysimeters was established based on measurements of spatial heterogeneity within the lysimeter area. This was part of an ongoing study to determine relationships between soil microbial diversity and nutrient loss by leaching. The sampling protocol had to allow collection of soil on a regular basis (as opposed to destructive sampling) and ensure high spatial independence of subsamples. On each of the two sites (one developed under organic crop management and the other under conventional crop management), ten 15 cm soil cores (sampling points) were taken from three areas (replicates) of 50 cm diameter (lysimeter surface area) and separately analysed for biotic (microbial biomass carbon and nitrogen; arginine deaminase activity) and abiotic (total carbon and nitrogen) soil properties. The data were tested for variability, expressed as coefficient of variance (biotic and abiotic), and spatial heterogeneity using geostatistics (biotic properties). The biotic soil properties showed significant differences among sampling points, whereas the abiotic parameters were useful in differentiating on a larger scale, i.e. between sites. For all soil properties tested, the differences among the replicates were smaller than those between the sites or among points indicating that, in the main experiment, all treatments can be sampled following the same pattern. Geostatistical analysis and fitting of an exponential model showed that a spatial structure exists in the biotic soil properties and that the samples are independent beyond separation distances of 25-30 cm. A revised sampling pattern consisting of 11 samples per lysimeter is described.     

Soil salinity characteristics using moderate resolution imaging spectroradiometer (MODIS) images and statistical analysis

Remote sensing is currently a tremendous asset in controlling and monitoring soil salinity. Moderate resolution imaging spectroradiometer (MODIS) images can be obtained daily, are free, offer more opportunities to acquire cloud-free images and may be preferred over high-resolution spatial data. The main objective of this study was to evaluate the capability of MODIS imagery to assess soil properties when coupled with field soil sampling. The study area was ～95,000 ha, located in the south-east of Fars Province, Iran. In total, 240 soil samples were selected from 60 georeferenced soil pits, following a stratified random sampling approach. Sixteen spectral indices were calculated from a nadir-viewed MODIS scene to establish statistical correlation models between measured soil properties and MODIS band values. A precise map of the soil properties was produced using geostatistical techniques. A paired-sample t-test indicates that there are no significant differences between values estimated using MODIS data statistical modeling and laboratory-measured soil properties of samples collected through fieldwork. The results also indicate that image transformation (salinity index (SI) to radiance) reduces estimation errors and increases both model efficiency and the R ² of the models. The results also indicate that MODIS imagery provides useful information on soil properties.     

基于实测数据及遥感图片的土壤采样方法

如何结合土壤特性和先进手段,制定具有代表性,同时又经济的土壤采样方案一直是土壤分析的难题。该文根据陕西省卤泊滩盐碱地改良区土壤含盐量的实测资料和相应的遥感图片数据,并结合土壤属性空间分布特性,提出一种新的土壤水盐含量采集方案。结果表明,用33个已知点的实测数据可以估算出101个未测点的含量并最终构成插值343个点的空间分布图,且水分与盐分含量预测结果相关的确定系数分别为0.869和0.817。在此基础上进而对工程改良措施下的卤泊滩盐渍土表层水盐空间变异性进行研究。分析结果表明,研究区土壤水盐含量具有中等较强的空间自相关性和较弱的变异性。通过对该地区水盐空间变异性的研究可以及时了解盐渍地试验区的改良效果及水资源管理情况。     

基于空间模拟退火算法的最优土壤采样尺度选择研究

以研究区0.5 km×0.5 km(尺度a)网格的7050个样点为基础,分别得到1 km×1 km网格的1757个样点(尺度b),2 km×2 km网格的444个样点(尺度c),4 km×4 km网格的110个样点(尺度d),以土壤有机质(SOM)为目标属性,运用模拟退火算法对4种采样尺度的土壤样点进行优化选择,确定区...     

黄土丘陵区退耕坡面草地恢复与土壤物理性质空间分异特征

为了解地形对草地恢复与土壤物理性质的影响以及草地恢复与土壤物理性质之间的影响机制,因地制宜为当地生态恢复提供科学依据。选择吴起县枣庄沟小流域为研究区,对退耕坡面不同坡位草地恢复与土壤物理性质分异特征进行研究。从峁顶到沟底设置8个采样位置,在4面坡共布设128个采样点进行植被调查和土样采集,将0—40 cm土壤分0—10,10—20,20—40 cm 3层进行土壤物理性质测定,并分析其与植被分布的相关关系。结果表明:不同坡位植被分布与土壤物理性质均表现出沟缘线以上的下坡位较优,沟缘线以下的陡沟坡较差的趋势;植被恢复对土壤团聚体有明显改善作用,但对其与土壤物理性质的改善主要集中在0—20 cm表层土壤,对20—40 cm土壤影响较弱;坡度对植被盖度与土壤容重、孔隙度和饱和持水量影响显著且具有极显著相关性,但对团聚体与颗粒组成无显著影响;植被恢复对土壤颗粒组成改善效果缓慢,若土壤颗粒组成被破坏,短期内很难恢复。     

基于GPS和GIS的田间土壤特性空间变异性的研究

以一块面积约为13.3 hm²的冬小麦田为研究区，利用GPS接收机定位，按50 m×50 m设置网格，共取63个采样点，测定土壤表层(20 cm)内的土壤有机质、全氮、碱解氮、速效磷、速效钾、容重、田间土壤含水率和电导率，研究麦田土壤特性的空间变异规律。采用传统统计学和地统计学相结合的方法对所取的数据进行了分析，利用Arcview3.2软件的空间分析功能，绘制了表达这些土壤特性随机性和结构性的半方差图和空间分布图。研究结果表明：所有土壤特性均服从正态分布；土壤容重具有弱变异强度，其它土壤特性具有中等变异强度；土壤有机质、全氮、碱解氮、速效钾和电导率具有很强的相关性，土壤容重、速效磷和含水率具有中等强度的空间相关性，土壤特性的相关距变化范围为246.8～426.8 m。该成果可为农田的定位施肥、灌溉以及其它的农田精细管理提供依据。     

基于环境相关法和地统计学的土壤属性空间分布预测

土壤属性是土壤质量的重要决定因素,并强烈影响土地利用和生态过程。正确理解并充分考虑土壤空间变异,对于在景观尺度上建立生态、环境过程模型是必不可少的。在黄土高原横山县采集了254个样点,应用数字地形与遥感影像分析技术,获取相关地形因子与遥感指数,分析土壤属性（土壤容重、有机质和全磷）与环境因子相互关系,并利用环境变量进行空间预测。结果表明,土壤容重、有机质与地形因子和遥感指数之间存在较好相关性,而全磷与地形因子相关性不大;多元线性逐步回归模型对于土壤容重和有机质拟合较好,而对于全磷,预测结果较差;回归－克里格预测有效地减小了残差,消除了平滑效应,与实测值较为接近。     

应用土壤质地预测干旱区葡萄园土壤饱和导水率空间分布

田间表层土壤饱和导水率的空间变异性是影响灌溉水分入渗和土壤水分再分布的主要因素之一,研究土壤饱和导水率的空间变化规律,有助于定量估计土壤水分的空间分布和设计农田的精准灌溉管理制度。为了探究应用其他土壤性质如质地、容重、有机质预测土壤饱和导水率空间分布的可行性,试验在7.6 hm2的葡萄园内,采用均匀网格25 m×25 m与随机取样相结合的方式,测定了表层(0～10 cm)土壤饱和导水率、粘粒、粉粒、砂粒、容重和有机质含量,借助经典统计学和地统计学,分析了表层土壤饱和导水率的空间分布规律、与土壤属性的空间相关性,并对普通克里格法、回归法和回归克里格法预测土壤饱和导水率空间分布的结果进行了对比。结果表明:1)土壤饱和导水率具有较强的变异性,平均值为1.64 cm/d,变异系数为1.17;2)表层土壤饱和导水率60%的空间变化是由随机性或小于取样尺度的空间变异造成;3)土壤饱和导水率与粘粒、粉粒、砂粒和有机质含量具有一定空间相关性,而与土壤容重几乎没有空间相关性;4)在中值区以土壤属性辅助的回归克里格法对土壤饱和导水率的预测精度较好,在低值和高值区其与普通克里格法表现类似。研究结果将为更好地描述土壤饱和导水率空间变异结构及更准确地预测其空间分布提供参考。     

不同取样方式下土壤质地空间插值的精度分析

为研究土壤质地的合理取样方式,进而研究其空间变异情况,为田间施肥及灌溉提供依据,本试验利用地统计学方法和GIS技术,在重庆市彭水县重庆烟草试验站,利用289个表层土样,研究了16 m间距的栅格取样法(对照,253个土样,扣除36个验证样点)、34 m间距的栅格取样法(115个土样)和随机取样法(115个土样)3种取样方式下土壤质地的空间插值精度。3种土壤颗粒指标中粉粒占68.43%,砂粒含量最少,占12.68%,黏粒含量略高于砂粒。砂粒和黏粒具有中等强度的变异性,粉粒具弱变异性,且数据符合正态分布。地统计分析显示,在分析该区域土壤质地时,采用栅格取样方法应适当增大取样间距,而采用随机取样方法可适当缩小取样间距。交叉检验显示,土壤质地成分在3种取样方式下的插值精度均以对照最大,栅格取样次之,随机取样最小。综合考虑插值误差、样品采集和分析成本及时效性等因素,本研究建议在该区域进行土壤质地空间变异规律分析为生产服务时应采用随机取样。     

单种复合类型单元土壤全量元素空间变异的幅度效应

土壤属性变异性的幅度效应研究对土壤数字制图、土壤调查及农业生产均具有重要意义。针对江西东乡县研究区域水田—中潴灰鳝泥田(土种)一种土地利用与土壤的复合类型单元,设定了8个土壤采样幅度研究区,并利用多层次嵌套布点方法在每个采样幅度区分别布设60个采样点,共采集337个样点的土壤表层(0~20cm)样品;运用多种函数拟合分析手段,揭示单种复合类型单元(水田—中潴灰鳝泥田)土壤全氮、全磷、全钾含量的空间变异性随采样幅度的变化特征。结果表明,土壤全量元素的空间变异性均具有明显的幅度效应特征,全氮、全磷、全钾含量的变异系数均随采样幅度的拓展而逐渐增加,而增加趋势则逐渐变缓。不同土壤全量元素空间变异性体现出不同的幅度效应特征,土壤全氮、全磷变异系数(%)的幅度效应刻画函数为CV=b×Da(R20.87,p0.001),土壤全钾为CV=e(a/D+b)(R20.93,p0.001),D为幅度表征指标(km);不同土壤全量元素变异系数随采样幅度快速与缓慢变化的幅度分界点位置存在明显差异,表明基于复合类型单元的土壤多属性调查采样布点与分析策略需要统筹考虑。研究结果对于红壤丘陵区县域土壤调查样点合理布设具有重要的启示和参考价值。     

Potential Use of Near Infrared Reflectance Monitoring in Precision Agriculture

Abstract

The concept of Precision farming is not new, and interest in the potential benefits gained momentum in the late eighties. The high cost of soil sampling and chemical and physical analysis by conventional laboratories has restricted the full implementation of this technique at the field level. Near infrared reflectance (NIR) could be a cost‐effective solution. Soil properties that have been calibrated include gravimetric soil water, clay content, buffer capacity, pH, electrical conductivity, titratable acidity, organic matter, mineralizable nitrogen, potential ammonia volatilization from urea, potential nitrification rate, and urease activity. A whole paradigm shift in philosophy is needed in soil testing to move away from the traditional approach of taking a perceived‐representative sample, in which all the spatial variation is lost, to using a combination of grid soil sampling at a sample intensity of 4 to 10 cores per ha and analysed separately using rapid but less accurate methods such as NIR.     

NOAA卫星可见光和红外资料在干旱监测中的应用

利用NOAA卫星AVHRR通道1、2计算NDVI和通道4的亮温T4,建立模型方程W=ae^NDVI＋bT4＋c监测辽西地区土壤干旱情况。结果表明,土壤深度20cm左右是该模型适用的最佳层次,且模型的监测结果与实测值相对误差较小。     

Assessing Molybdenum Adsorption onto an Industrial Soil and Iron Minerals

Spatial accuracy of hydrologic modeling inputs influences the output from hydrologic models. A pertinent question is to know the optimal level of soil sampling or how many soil samples are needed for model input, in order to improve model predictions. In this study, measured soil properties were clustered into five different configurations as inputs to the Soil and Water Assessment Tool (SWAT) simulation of the Castor River watershed (11-km² area) in southern Quebec, Canada. SWAT is a process-based model that predicts the impacts of climate and land use management on water yield, sediment, and nutrient fluxes. SWAT requires geographical information system inputs such as the digital elevation model as well as soil and land use maps. Mean values of soil properties are used in soil polygons (soil series); thus, the spatial variability of these properties is neglected. The primary objective of this study was to quantify the impacts of spatial variability of soil properties on the prediction of runoff, sediment, and total phosphorus using SWAT. The spatial clustering of the measured soil properties was undertaken using the regionalized with dynamically constrained agglomerative clustering and partitioning method. Measured soil data were clustered into 5, 10, 15, 20, and 24 heterogeneous regions. Soil data from the Castor watershed which have been used in previous studies was also set up and termed “Reference”. Overall, there was no significant difference in runoff simulation across the five configurations including the reference. This may be attributable to SWAT's use of the soil conservation service curve number method in flow simulation. Therefore having high spatial resolution inputs for soil data may not necessarily improve predictions when they are used in hydrologic modeling.     

秸秆带状覆盖下培土对马铃薯产量与水分利用效率的影响

气候变化效应评估、土壤固碳潜力和肥力管理等,迫切需要详尽的土壤有机质（soil organic matter, SOM）空间分布信息。该文以江苏省第二次土壤普查的1 519个典型土壤剖面的表层（0～20 cm）SOM含量为例,选择1 217个样本为建模集,302个为验证集,选取年均温度、年均降雨、物理性黏粒和土壤pH值等因子进行SOM的地理加权回归（geographically weighted regression, GWR）建模。从建模集中分别随机抽取100%（1 217个）、80%（973个）、60%（730个）、40%（486个）,20%（243个）的样点,对比不同样点数量下GWR和传统全局回归模型的精度差异,并选择最优模型进行SOM空间预测制图。结果表明：1）江苏省SOM含量在不同空间尺度上存在极显著的空间自相关性。不同样点数量的建模集的全局自相关性和局部空间自相关聚类图结果相似。全局Moran''s I值介于0.25～0.61（P<0.001）。SOM含量空间分布以空间聚集特征为主,"高-高"聚集区主要分布在苏中和苏南地区,"低-低"聚集区主要分布在苏北地区。2）GWR建模结果均优于传统的传统全局回归建模,其残差在不同的空间尺度上均不存在空间自相关性。不同建模集的GWR的R2adj较全局建模均提高0.15～0.20,其AIC和RSS均比全局模型有大幅降低,为56.08～360.19和17.40～76.67。不同建模样本数量的GWR模型对SOM的解释能力差异较小。3）建模样点数量（除建模样本n=243）对GWR预测制图结果的精度影响不大,RMSE介于5.56～5.75 g/kg之间,MAE介于3.87～4.05 g/kg之间,R2介于0.52～0.48之间,均优于全部建模样点的普通克里格插值验证结果。该研究可为样点数较少的省级尺度地区SOM空间建模与制图提供借鉴。     

一种基于样点代表性等级的土壤采样设计方法

采样设计是获取土壤空间分布信息的关键环节,直接影响到土壤制图的精度。目前常用的采样设计方法大多存在着设计样本量大、采样效率不高的问题。当可投入资源难以完成一次性大量采样时,采样往往需要多次、分批进行。然而现有分批采样方法多考虑各批采样点在地理空间的互补性,可能造成样本点在属性空间的重叠,影响采样资源的高效利用。鉴于此,本研究通过对与土壤在空间分布具有协同变化的环境因子进行聚类分析,寻找可代表土壤性状空间分布的不同等级类型的代表性样点,建立一套基于代表性等级的采样设计方法。将该采样方法应用于位于黑龙江省嫩江县鹤山农场的研究区,利用所采集的不同代表性等级的样点进行数字土壤制图并进行验证,探讨采样方案与数字土壤制图精度的关系,以评价本文所提出的采样方法。结果表明,通过代表性等级最高的少量样点可获取研究区的大部分主要土壤类型(中国土壤系统分类的亚类级别),且制图精度较高;随着代表性等级较低样点的加入,土壤图精度提高;但当样点增加到一定数量时,土壤图的精度变化不大。因此,与样点数相比,样点的代表性高低对制图精度的影响更大。该方法所提出的代表性等级可以为样点采集顺序提供参考,有助于设计高效的逐步采样方案。     

河套灌区土壤盐渍化微波雷达反演

目前中国西北干旱、半干旱地区的土壤盐渍化情况日益趋于严重,动态、快速而精确地监测与评价土壤盐渍化显得尤为重要。微波遥感所具有的优点使其成为探测土壤盐分分布的新兴而有潜力的方法。快速获取大范围地表土壤盐渍化的空间分布是一个迫切急需解决的科学难题。该文目的是试验与评价 C 波段 RADARSAT-2 SAR（synthetic aperture radar）数据反演土壤盐渍化的性能。以受盐渍化影响较严重的内蒙古河套灌区解放闸灌域为试验区,基于 SAR 后向散射系数和土壤盐分实测值,利用多元线性回归（multiple linear regress,MLR）、地理加权回归（geographically weighted regression,GWR）和 BP 人工神经网络（back propagation artificial neural networks,BP ANN）方法建立土壤含盐量的定量反演模型,重点构建了8∶140∶1结构的3层 BP ANN 模型,经模型验证发现 MLR、GWR 模型均偏向于弱相关,其标准误差 SE 分别为0.55、0.47 mg/g,而 ANN（BP）模型的内部、外部检验标准误差 SE 分别为0.24、0.33 mg/g,优于前2种模型,其反演的盐渍化面积占比65.4%,与地面验证结果基本一致。该文建立的考虑土壤水分影响、组合雷达后向散射系数反演土壤盐分的人工智能模型,无需复杂的介电常数模型,能够在一定程度上满足土壤盐渍化监测的需要,可促进微波遥感在土壤盐渍化监测中的开拓应用。     

Visible and Near-Infrared Reflectance Spectroscopy for Assessment of Soil Properties in the Caucasus Mountains,Azerbaijan

ABSTRACT

This study aimed to predict soil properties using visible–near infrared (VIS-NIR) spectroscopy combined with partial least square regression (PLSR) modeling. Special emphasis was given to evaluating effect of pre-processing methods on prediction accuracy and important wavelengths. A total of 114 samples were collected and involved in chemical and spectral analyzes. PLSR model of each soil property was calibrated for all pre-processing methods using all samples, and leave-one-out cross-validation was used to make comparisons between them. Then, PLSR model of each best pre-processing method was calibrated using a 75% of all samples and correspondingly validated with the remaining a 25%. Model accuracy was evaluated based on coef?cient of determination (R²), root mean-squared errors (RMSE), and residual prediction deviations (RPD). The high correlation coefficients were found between the tested soil properties and reflectance spectra. The pre-processing methods considerably improved prediction accuracy and filtering methods outperformed linearization methods, and the latter outperformed normalization methods. The performance of cross-validation, calibration and independent validation was similar. An excellent prediction (RPD>2.5) model was obtained for soil organic carbon (SOC) and calcium-carbonate (CaCO₃), good quantitative (2.0< RPD<2.5) prediction for sand, silt, and clay, fair prediction (1.4< RPD<1.8) for pH, and poor prediction (1.0< RPD<1.4) for hygroscopic water content (WC). Important wavelengths varied depending on soil property, but some wavelengths were common. This study can be a precursor to building a pioneering soil spectral database, calibrating satellite data, and hyperspectral image mapping of soils as well as digital soil mapping, environmental, and erosion modeling in the Caucasus Mountains.