基于BP神经网络的土壤水力学参数预测

为了获取区域土壤水分和溶质运移模拟所需的土壤水力学参数,利用黄淮海平原曲周县的试验资料建立基于BP神经网络的土壤转换函数模型。本文采用土壤粒径分布、容重、有机质含量等土壤基本理化性质,来预测土壤饱和导水率Ks、饱和含水量sθ、残余含水量θr、以及van Genuchten公式参数α、n的对数形式ln(α)和ln(n),并与多元线性逐步回归方法进行比较。t检验结果表明,BP神经网络训练和预测得到的模拟值与实测值之间吻合很好,该方法具有较高的预测精度。通过对平均相对误差的比较,得出在粒径分布的基础上增加容重、有机质含量等输入项目,可以提高部分土壤水力学参数的预测精度,而有些参数的预测精度反而降低。以误差平方和为标准的比较结果表明,BP神经网络模型的预测效果总的来看要优于多元线性回归法。     

基于BP人工神经网络的长江河口地区土壤盐分动态模拟及预测

为开展长江河口地区土壤盐分动态的中长期模拟与预测,采用人工神经网络中应用较为成熟和广泛的BP网络建立长江河口地区土壤盐分与降雨量、蒸发量、长江水电导率、内河水电导率、地下水位、地下水电导率6因子间的非线性神经网络响应模型。网络模型结构为6-11-1,隐含层单元数用"试错法"确定。选择合适的参数训练和学习网络模型后,对河口地区2003年各月平均根层土壤电导率进行预测,并与线性回归模型预测结果进行比较。结果表明:BP网络模型较线性回归模型具有更高的预测精度,平均相对预测误差为7.3%,预测值与实测值相关性良好,可以满足实际应用需求。     

饲草料地土壤水分动态变化规律及其预测的人工神经网络模型的研究

根据2005～2006年实测土壤水分资料和气象资料,研究分析了饲草料地土壤水分的动态变化规律和建立了考虑多个因素对土壤水分影响的BP人工神经网络模型,结果表明:表层土壤的含水率变幅较大,主要是受大气降水的影响,20～40cm和40～60cm土层土壤含水率的波动,除大气降水的影响外,还与植物的生长发育状况有关;土壤水分预测模型具有较好的预测效果,用神经网络建立土壤水分预测模型的方法是可行的,对于不同条件的地区具有广泛的适应性和推广应用前景。     

艾比湖流域不同土地覆盖类型土壤养分 高光谱反演模型研究

土壤养分影响着土壤的质量,也影响着植被、农作物等的生长。为快速准确地估测艾比湖流域土壤养分状况,选择艾比湖流域精河县作为研究区,以精河县内不同地表覆盖类型土壤为研究对象,基于实地采集的75个土壤样品的室内ASD Field Spec3实测光谱数据和3种光谱变换形式,利用10 nm间隔重采样进行去噪处理,再结合多元逐步回归法(SMLR)、偏最小二乘法回归法(PLSR)、人工神经网络法(ANN)分别建立土壤养分预测模型,以探索最优模型。结果表明:土壤实测光谱的一阶微分、二阶微分变换形式能显著提高光谱与土壤养分之间的相关性,尤其是一阶微分变换与土壤有机质和全氮的相关性最高分别达0.87和0.91,光谱变换技术能显著增强土壤养分与高光谱之间的敏感度,达到更好的建模效果;SMLR、PLSR和ANN这3种模型都具有良好的预测能力,其中,ANN建立的模型预测效果最好,二阶微分变换的ANN模型对有机质、全氮的预测决定系数(R2)分别为0.886和0.984,均方根误差(RMSE)分别为2.614和0.147,PLSR次之;全氮的预测效果明显优于有机质的预测效果,说明高光谱和全氮之间的敏感性更高。总体来说,光谱二阶微分变换形式的人工神经网络模型可以最精确稳定地完成土壤养分含量的快速预测,能够实现艾比湖流域的土壤养分空间分布状况和动态变化特征的动态监测。     

青海三江源地区土壤水分常数转换函数的建立与比较

利用土壤理化性质数据建立转换函数是间接获得土壤水力参数的重要手段之一。基于测定的土壤理化性质和土壤水分常数数据,本文采用回归分析、BP神经网络和基于BP神经网络的Rosetta模型3种方式分别建立了青海三江源地区土壤饱和含水量、毛管持水量和田间持水量的转换函数,并对其预测精度进行了比较。结果表明:(1)回归分析方法总体预测效果比较理想,特别是田间持水量的平均误差(ME)和均方根误差(RMSE)都在3.397%以下,决定系数(R2)高达0.868;(2)BP神经网络方法的预测效果非常理想,各土壤水分常数平均误差和均方根误差都在4.685%以下,并且决定系数均在0.857以上;(3)Rosetta模型的预测效果相对较差,特别是饱和含水量和毛管持水量,平均误差(ME)和均方根误差(RMSE)相对较大,决定系数(R2)相对较小。3种方式中,BP神经网络方法所建立的毛管持水量和饱和含水量转换函数均为最佳,回归方法所建立的田间持水量的转换函数要好于BP神经网络方法和Rosetta模型,Rosetta模型对土壤水分常数的预测效果不如其他两种方式。研究可为青海三江源地区土壤水力特性参数研究以及区域尺度上土壤水分估算提供科学依据。     

Stand-scale spatial patterns of soil microbial biomass in natural cold-temperate beech forests along an elevation gradient

This study focuses on spatial heterogeneity in the soil microbial biomass (SMB) of typical climax beech (Fagus crenata) at the stand scale in forest ecosystems of the cold-temperate mountain zones of Japan. Three beech-dominated sites were selected along an altitudinal gradient and grid sampling was used to collect soil samples at each site. The highest average SMB density was observed at the site 1500 m a.s.l. (44.9 gC m⁻²), the lowest was recorded at the site 700 m a.s.l. (18.9 gC m⁻²); the average SMB density at the 550 m site (36.5 gC m⁻²) was close to the overall median of all three sites. Geostatistics, which is specifically designed to take spatial autocorrelation into account, was then used to analyze the data collected. All sites generally exhibited stand-scale spatial autocorrelation at a lag distance of 10-18 m in addition to the small-scale spatial dependence noted at <3.5 m at the 550 m site. Correlation analysis with an emphasis on spatial dependency showed SMB to be significantly correlated with bulk density at the 550 and 1500 m sites, dissolved organic carbon (DOC) at the 700 and 1500 m sites, and nitrogen (N) at the 550 and 700 m sites. However, no soil parameter showed a significant correlation with SMB at every site, and some variables were also differently correlated (negative or positive) with SMB at different sites. This suggests that the factors controlling the spatial distribution of SMB are very complex and responsive to local in situ conditions. SMB regression models were generated from both the ordinary least-squares (OLS) and generalized least-squares (GLS) models. GLS performance was only superior to OLS when cross-variograms were accurately fitted. Geostatistics is preferable, however, since these techniques take the spatial non-stationarity of samples into account. In addition, the sampling numbers for given minimum detectable differences (MDD_s) are provided for each site for future SMB monitoring.     

Soil Particulate Organic Matter (POM) Prediction in a Mountainous Watershed using Artificial Neural Networks

Soil organic matter is a very important component of soil that supports the sustainability and quality in all ecosystems, especially in arid and semi-arid regions. A comparison study was carried out to verify when the artificial neural network (ANN) and multiple linear regression (MLR) models are appropriate for the prediction of soil organic matter (SOM) and particulate organic matter (POM). Discussions of advantages and disadvantages are given for both methods. Three different sets of easily available properties (soil properties alone, topographic and vegetation index, a combination of soil and topographic data) were used as inputs and the one affecting the model the most was determined. The smallest prediction errors were obtained by the ANN method; however, the prediction accuracies of the constructed MLR models using different data sets were closed to the ANN models in many cases.     

Comparing the spatial predictions of soil organic matter determined by two laboratory methods

Abstract. The most common way of assessing soil organic matter content is by loss on ignition, which is both simple and inexpensive. This method tends to overestimate organic matter content because additional weight losses occur during ignition. An alternative, more expensive and time-consuming method for determining soil organic matter content is by an acid dichromate oxidation. This paper compares the results of applying these methods to soil on different parent materials in two arable fields. Summary statistics and correlation coefficients showed that there were consistent relationships between the two sets of values: the stronger was for the sandy soil and the weaker was for the clay loam. This relationship can be used to improve the accuracy with which soil organic matter content is known while using fewer of the expensive measurements and more of the inexpensive ones. Two approaches to prediction were compared: the geostatistical method of cokriging, and simple linear regression. These were used to predict organic matter determined by an acid dichromate oxidation from the loss on ignition. The estimates from cokriging were more accurate but the method requires the spatial correlation to be modelled reliably. The regression results showed it to be a valuable and practical approach. Using the information from nine carefully selected sampling sites a regression line could be fitted that was representative of the full data.     

Calibration model of microbial biomass carbon and nitrogen concentrations in soils using ultraviolet absorbance and soil organic matter

There is a need for a rapid, simple and reliable method of determining soil microbial biomass (SMB) for all soils because traditional methods are laborious. Earlier studies have reported that SMB‐C and ‐N concentrations in grassland and arable soils can be estimated by measurement of UV absorbance in soil extracts. However, these previous studies focused on soils with small soil organic matter (SOM) contents, and there was no consideration of SOM content as a covariate to improve the estimation. In this study, using tropical and temperate forest soils with a wide range of total C (5–204 mg C g^?1 soil) and N (1–12 mg N g^?1 soil) contents and pH values (4.1–5.9), it was found that increase in UV absorbance of soil extracts at 280 nm (UV₂₈₀) after fumigation could account for 92–96% of the variance in estimates of the SMB‐C and ‐N concentrations measured by chloroform fumigation and extraction (P < 0.001). The data were combined with those of earlier workers to calibrate UV‐based regression models for all the soils, by taking into account their varying SOM content. The validation analysis of the calibration models indicated that the SMB‐C and ‐N concentrations in the 0–5 cm forest soils simulated by using the increase in UV₂₈₀ and SOM could account for 86–93% of the variance in concentrations determined by chloroform fumigation and extraction (P < 0.001). The slope values of linear regression equations between measured and simulated values were 0.94 ± 0.03 and 0.94 ± 0.04, respectively, for the SMB‐C and ‐N. However, simulation using the regression equations obtained by using only the data for forest profile soils gave less good agreement with measured values. Hence, the calibration models obtained by using the increase in UV₂₈₀ and SOM can give a rapid, simple and reliable method of determining SMB for all soils.     

Neural network models to predict soil water retention

Soil water retention curves are needed to describe the availability of soil water to plants and to model movement of water through unsaturated soils. Measuring these characteristics is time-consuming, labour-intensive and therefore expensive. This study was conducted to develop and evaluate functions based on neural networks to predict soil water retention characteristics. Dutch and Scottish data sets were available; they contained data on 178 and 165 soil horizons, respectively. A series of three neural networks (A, B and C) was developed. Neural network A had the following input variables: topsoil, bulk density, organic matter, clay, silt and sand content. In addition neural network B had matric potential as input, and network C included soil structural data expressed as the upper and lower boundary of the ped-size class. Neural network A had three output variables: the volumetric water content at matric potentials of 0, –100 and –15 000 hPa. Both models B and C had volumetric water content, at the matric potential given as input, as output variable. The networks were tested against independent data that were extracted from the original sets of soil profiles. Accuracy of the predictions was quantified by the root of the mean squared difference (RMSE) between the measured and the predicted water contents, and the coefficient of determination (R²). For network A the RMSE varied for the three estimated water contents from 0.0264 to 0.0476 cm³ cm^–3, and R² varied from 0.80 to 0.93 for the individual model outputs. Networks B and C had an RMSE of 0.0435 and 0.0426 cm³ cm^–3, respectively. For both networks, R² was 0.89. The neural networks performed somewhat better than previous regression functions, but the improvements were not significant.     

基于气象因子的长三角地区农田站点土壤水分时间序列预测

以长三角3省1市为研究区,旨在构建长三角地区土壤水分长时间序列,为农业生产和遥感算法提供数据支撑。研究基于空间匹配的站点土壤水分数据和气象数据,利用主成分分析得到4个有效主成分作为线性回归和BP神经网络模型的输入因子,建立土壤水分与气象因子间的定量关系,并评估所构建模型的精度。结果表明,基于全部站点数据建立的单一BP神经网络模型优于单一线性回归模型。单一线性回归模型的R 2=0.34,RMSE=0.046 m³/m³,MAE=3.67%;而单一BP神经网络模型的训练、验证和测试3个数据集的R 2均在0.64以上,RMSE<0.043 m³/m³,MAE低于3.4%。根据逐个站点分别构建分站点的BP神经网络模型,其总体精度高于基于全部站点数据构建的单一BP神经网络模型。分站点构建的BP神经网络模型的总体精度方面,3个数据集的R 2均值在0.75以上,RMSE<0.039 m³/m³,MAE低于3%。通过对逐个站点分别构建BP神经网络模型,获得了精度较高、较稳定的土壤水分拟合结果。     

黑龙江省黑土区拉林河流域土壤侵蚀强度评价方法比较

为了保护水土资源、改善生态环境,进行区域土壤侵蚀强度评价,以黑龙江省黑土区拉林河流域为研究区,选取坡度、坡向、土壤类型、土地利用状况和标准化植被指数等5项评价指标,分别采用逻辑回归和广义回归神经网络模型,在ArcGIS平台上进行土壤侵蚀强度评价。应用受试者工作特征曲线对2种方法的评价结果进行对比。结果表明:逻辑回归模型和广义回归神经网络模型的受试者工作特征曲线下面积值分别为0.857和0.881,与实际的土壤侵蚀强度情况基本吻合;2种模型的评价结果可以相互校验,广义回归神经网络模型评价结果的精度较高。     

基于人工神经网络的土壤颗粒组成制图

以浙江西苕溪流域为研究区,综合考虑地形和土壤类型等信息,采集典型土壤样本,测定土壤颗粒组成,并基于土壤颗粒组成与景观位置和特征之间的关系,利用径向基函数(RBF)神经网络建立了高程、坡度、平面曲率、剖面曲率、径流强度系数和地形湿度指数6个地形因子与土壤颗粒组成之间的非线性映射关系,预测土壤颗粒组成的空间分布。验证结果表明,RBF神经网络方法能够挖掘出地形因子信息与土壤颗粒组成之间的非线性映射关系,其预测精度较高,模型稳定性较好,是一种低成本、高效率的制图方法。     

农田土壤有效态微量元素空间预测方法及影响因子定量分析

为深入研究土壤有效态微量元素的影响因素,以重庆市江津区农田土壤为研究对象,利用1 265个样点数据,加入种植制度和母岩类型数据,构建土壤微量元素的虚拟变量回归预测模型,采用随机森林算法,定量分析了分类变量对土壤有效态微量元素影响的相对重要性。结果表明:加入种植制度和母岩的回归模型的拟合度高于普通线性回归模型,对土壤有效铁、锰、铜和锌变异的解释度分别提高了9.20%、38.99%、20.75%和29.96%,并且提高了对土壤有效铁的预测精度,但对提高土壤有效锰、铜和锌的预测精度作用不明显。土壤养分和种植制度是影响土壤有效态微量元素含量的重要因素,种植制度和母岩中,种植花椒、种植水稻和遂宁组发育的土壤对农田有效态微量元素含量的影响相对较大。     

BP神经网络在道路土壤分离速率模拟中的应用

 土壤分离是土壤侵蚀的重要过程,为坡面径流的搬运过程提供了物质基础,因而对土壤分离速率的准确模拟具有重要的理论和实践意义。采用变坡水槽实验,利用在较大的坡度(8.8%～46.6%)及较大的流量范围(1～5L/s)内测得的黄土高原道路土壤分离速率数据,分别使用BP神经网络模型及回归模型对土壤分离速率进行模拟,并对比上述2种模型的模拟效果。结果表明:BP神经网络模型可以利用实验中较容易测定的坡度、流量等数据对土壤分离速率进行较为准确的模拟(模型效率系数0.952);相对传统回归模型,BP神经网络模型对不同类型道路的土壤分离速率的模拟精度均有所提高;BP神经网络模型可以将道路类型、坡度、流量与土壤分离速率的关系统一为一个模型,可为道路土壤分离的模拟提供新的方法。     

土壤环境质量指导值与标准研究 Ⅴ.镉在土壤-作物系统中的富集规律与农产品质量安全

通过查阅国内现有镉污染农田土壤和作物中镉含量的相关资料,包括近30年来公开发表文献、国家"七五"科技攻关环保项目"土壤环境容量研究"等科研成果,结合本实验室在长江三角洲、珠江三角洲典型地区污染农田调查数据,经筛选后从88组研究中共收集到镉污染农田土壤和作物中镉含量对应数据509对。根据镉盐来源,将收集的数据分为污染农田调查数据和添加镉盐试验数据;根据作物不同,分为水稻、小麦、茎/叶类蔬菜、根菜和果菜地调查数据。采用富集系数和一元回归模型的方法基于土壤镉含量预测作物可食部分镉含量;采用多元回归模型的方法基于土壤镉含量和土壤pH预测作物可食部分镉含量。结果表明,回归模型对作物可食部分镉含量的预测效果明显优于富集系数中位值;回归模型95%预测上限对作物可食部分镉含量的保守预测优于富集系数90分位值。土壤pH显著影响作物对镉的吸收。影响模型回归效果和预测能力的主要因素是数据分布不均和数据量不足。模型对比后选用多元回归模型的95%预测上限推导稻田和茎/叶类蔬菜用地基于农产品质量安全的土壤镉含量环境基准。     

Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests

Geospatial measurements of ancillary sensor data, such as bulk soil electrical conductivity or remotely sensed imagery data, are commonly used to characterize spatial variation in soil or crop properties. Geostatistical techniques like kriging with external drift or regression kriging are often used to calibrate geospatial sensor data to specific soil or crop properties. More traditional statistical methods such as ordinary linear regression models are also commonly used. Unfortunately, some soil scientists see these as competing and unrelated modeling approaches and are unaware of their relationship. In this article we review the connection between the ordinary linear regression model and the more comprehensive geostatistical mixed linear model and describe when and under what conditions ordinary linear regression models represent valid spatial prediction models. The formulas for the ordinary linear regression model parameter estimates and best linear unbiased predictions are derived from the geostatistical mixed linear model under two different residual error assumptions; i.e., strictly uncorrelated (SU) residuals and effectively uncorrelated (EU) residuals. The theoretically optimal (best linear unbiased) and computable (linear unbiased) predictions and variance estimates derived under the EU error assumption are examined in detail. Statistical tests for detecting spatial correlation in LR model residuals are also reviewed, in addition to three LR model validation tests derived from classical linear modeling theory. Two case studies are presented that highlight and demonstrate the various parameter estimation, response variable prediction and model validation techniques discussed in this article.     

华北平原典型农田土壤微生物生物量碳氮磷库的县域分布特征——以河北省曲周县为例

土壤微生物生物量是土壤中的活性养分库,直接参与土壤碳氮磷硫等元素的形态转化与生物地球化学循环过程,是反映土壤肥力与质量的重要生物指标。基于网格法采样,运用地统计学方法分析华北平原典型农田土壤微生物生物量碳氮磷库的空间分布特征及影响因子。结果表明:河北省曲周县域农田耕层(0~30 cm)土壤微生物生物量库在空间上呈斑块状分布,具有中等变异强度和明显的空间自相关性,微生物生物量碳(MBC)、微生物生物量氮(MBN)、微生物生物量磷(MBP)库储量分别为(C)64.14×10³t、(N)24.55×10³t、(P)2.80×10³t,作物产量与MBC和MBN存在显著正相关关系。不同种植体系下单位质量土壤MBC、MBN、MBP的量存在显著差异,小麦/玉米轮作体系下单位质量土壤微生物生物量的平均量高于棉花连作。土壤微生物生物量库的大小和空间分布均受种植体系和土壤肥力的影响,其中土壤有机碳含量是影响土壤微生物生物量库容及空间分布的一个主要因子。研究结果表明土壤微生物生物量库是我国北方典型农田土壤中不可忽视的潜在有效养分库。     

土壤水分反演的特征变量选择研究综述

土壤水分是水、能量和生物地球化学循环中不可忽略的组成部分,土壤水分信息对水资源管理、农业生产以及气候变化等相关研究有着重要意义。基于遥感数据的土壤水分反演算法是获取土壤水分信息的重要手段,通过对影响土壤水分反演的因素进行梳理,将影响因素抽象为包括土壤特征,植被特征,以及气象特征在内的特征变量,并以此为主线对土壤水分的反演研究进行回顾与梳理。分析了利用不同特征变量反演土壤水分时存在的问题和发展趋势,指出土壤水分反演过程中存在特征变量理论研究不足、综合应用不深的问题,强调耦合使用各类特征变量以提高水分反演精度是未来的研究热点。     

Soil organic matter turnover as a function of the soil clay content: consequences for model applications

Based on a literature review including 201 surface soils from wet, mild, mid-latitude climates and 290 soils from the Lower Saxony soil monitoring programme (Germany), we investigated the relationship between soil clay content and soil organic matter turnover. The relationship was then used to evaluate the clay modifier for microbial decomposition in the organic matter module of the soil-plant-atmosphere model DAISY. A positive relationship was found between soil clay content and soil microbial biomass (SMB) C. Furthermore, a negative relationship was found between soil clay content and metabolic quotient (qCO₂) as an indicator of specific microbial activity. Both findings support the hypothesis of a clay dependent capacity of soils to protect microbial biomass. Under the differing conditions of practical agriculture and forestry, no or only very weak relationships were found between soil clay content and non-living soil organic matter C (humus C). It is concluded that the stabilising effect of clay is much stronger for SMB than for humus. This is in contrast to the DAISY clay modifier assuming the same negative relationship between soil clay content, on the one hand, and turnover of SMB and turnover of soil humus on the other. There is a positive relationship between SMB and microbial decomposition activity under steady-state conditions (microbial growth≈microbial death). The original concept of a biomass-independent simulation of organic matter turnover in the DAISY model must therefore be rejected. In addition to the original modifiers of organic matter turnover, a modifier based on the pool size of decomposing organisms is suggested. Priming effects can be simulated by applying this modifier. When using this approach, the original modifiers are related to specific microbial activity. The DAISY clay modifier is a useful approximation of the relationship between the metabolic quotient (qCO₂) as an indicator of specific microbial activity and soil clay content.