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机器学习用于耕地土壤有机碳空间预测对比研究——以亚热带复杂地貌区为例 总被引：1，自引：0，他引：1
耕地土壤有机碳(SOC)是土壤质量的重要指标,也是生态系统健康的重要表征。当前机器学习(Machine Learning, ML)用于SOC数字制图日益热门,但不同算法在高空间分辨率SOC数字制图中的对比研究尚有欠缺。本研究以福建省东北部复杂地形地貌区为例,采用10m空间分辨率Sentinel-2影像数据,选取地形、气候、遥感植被变量为驱动因子,重点分析当前常用的机器学习算法——支持向量机(SupportVector Machine,SVM)、随机森林(RandomForest,RF)在SOC预测中的差异,并与传统普通克里格模型(Ordinary Kriging, OK)进行比较。结果表明:基于地形、遥感植被因子和气候因子构建的RF模型表现最佳(RMSE=2.004,r=0.897),其精度优于OK模型(RMSE=4.571, r=0.623),而SVM模型预测精度相对最低(RMSE=5.190, r=0.431);3种模型预测SOC空间分布趋势总体相似,表现为西高东低、北高南低,其中RF模型呈现的空间分异信息更加精细;最优模型反演得到耕地土壤有机碳平均含量为15.33 g·kg-1; RF模型和SVM模型变量重要性程度表明:高程和降水是影响复杂地貌区SOC空间分布的重要变量,而遥感植被因子重要性程度低于高程。 相似文献
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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
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快速测量土壤剖面重金属含量是评估土壤重金属污染状况并选择相应修复技术的关键。为了探讨可见光-近红外光谱法（Visible and Near-Infrared Reflectance Spectroscopy,VNIR）预测原状土壤剖面重金属含量的潜力,以江西省两个典型工矿厂周边农田土壤为研究对象,共采集了19个深度约100 cm的完整土壤剖面样品,分别测定土壤剖面样品的VNIR数据及其Cu含量。采用偏最小二乘回归法（Partial Least Squares Regression,PLSR）、Cubist混合线性回归决策树（Cubist Regression Tree,Cubist）、高斯过程回归（Gaussian Process Regression,GPR）和支持向量机（Support Vector Machine Regression,SVM）方法研究不同光谱预处理方法对土壤Cu含量预测精度的影响。结果显示,Cubist、GPR和SVM这三种机器学习算法的预测精度普遍高于PLSR,其中一阶导数（First-Order Derivative,FD）预处理的SVM模型预测精度最高（R2=0.95,均方根误差为7.94 mg/kg,相对分析误差为4.34）。这表明利用VNIR和机器学习可以对原状土壤剖面Cu含量进行有效预测,为快速监测Cu及其他重金属含量的相关研究提供参考。 相似文献
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基于多源环境变量和随机森林的橡胶园土壤全氮含量预测 总被引：9，自引：4，他引：9
土壤全氮与土壤肥力和土壤氮循环紧密相关。掌握土壤全氮详细的空间分布信息对提高土壤肥力管理效率和更好地了解土壤氮循环至关重要。该文以儋州国营橡胶园为研究区域,采集2511个土壤样品,利用随机森林(random forest,RF)、逐步线性回归(stepwise linear regression,SLR)、广义加性混合模型(generalized additive mixed model,GAMM)以及分类回归树(classification and regression tree,CART)结合多源环境变量(成土母质、平均降雨量、平均气温和归一化植被指数)对研究区橡胶园土壤全氮含量进行空间预测,并通过754个独立验证点比较了4种模型的预测精度。结果表明RF对土壤全氮的预测值和实测值的相关系数(0.82)明显高于SLR(0.68)、GAMM(0.70)和CART(0.69),而RF的预测平均绝对误差(0.08836 g/kg)和均方根误差(0.13090 g/kg)均低于SLR、GAMM和CART。此外,RF模型预测结果能反映更为详细的局部土壤全氮含量空间变化信息,与实际情况更为接近。可见,RF模型可作为橡胶园土壤全氮含量空间分布预测的高效方法,为其他土壤属性的空间分布预测提供了一种新的方法。 相似文献
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Soil pH affects food production, pollution control and ecosystem services. Mapping soil pH levels, therefore, provides policymakers with crucial information for developing sustainable soil use and management policies. In this study, we used the LUCAS 2015 TOPSOIL data to map soil pH at a European level. We used random forest kriging (RFK) to build a predictive model of spatial variability of soil pH, as well as random forest (RF) without co-kriging and boosted regression trees (BRT) modelling techniques. Model accuracy was evaluated using a ten-fold cross-validation procedure. While we found that all methods accurately predicted soil pH, the accuracy of the RFK method was best with regression performance metrics of: R2 = 0.81 for pH (H2O) and pH (CaCl2); RMSE = 0.59 for pH (H2O) and RMSE = 0.61 in pH (CaCl2); MAE = 0.41 for pH (H2O) and MAE = 0.43 in pH (CaCl2). Dominant explanatory variables in the RF and BRT modelling were topography and remote sensing variables, respectively. The generated maps broadly depicted similar spatial patterns of soil pH, with an increasing gradient of soil pH from north to south Europe, with the highest values mainly concentrated along the Mediterranean coast. The mapping could provide spatial reference for soil pH assessment and dynamic monitoring. 相似文献
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[目的] 在滑坡易发性评价中,滑坡预测模型的选取和优化对运算过程的高效性和预测结果的准确性至关重要。针对现有单目标遗传优化算法(genetic algorithm,GA)易陷入早熟、局部搜索能力差、全局优化速度慢等问题,拟提出一种新的优化算法框架,将多目标遗传算法中的经典算法—带精英选择策略的非支配排序算法(the nondominated sorting genetic algorithm with an elite strategy,NSGA-Ⅱ)与常用机器学习模型[随机森林(random forest,RF)、支持向量机(support vector machine,SVM)]相结合,进行滑坡易发性预测。与单目标优化不同的是,NSGA-Ⅱ算法可同时进行特征选择和超参数优化,并使预测模型同时实现最优准确度、召回率、精密度和AUC(area under curve,AUC)。[方法] 以三峡库区重庆段为研究区,从模型精度评价、滑坡灾害易发性分区图、分区统计3个方面对4种优化模型(RF-GA、SVM-GA、RF-NSGA-II、SVM-NSGA-II)进行对比分析。[结果] NSGA-II较GA优化效果更明显,在模型评价和滑坡易发性分区方面,RF-NSGA-II模型具有更高的预测性能,4项评价值分别为80.91%,81.89%,80.07%,88.60%,证明NSGA-II优化算法的有效性;极低至极高危险区面积占比依次为23.06%,22.46%,22.96%,19.99%,11.53%,验证了RF-NSGA-II模型的可靠性。由RF-NSGA-II模型预测得到的易发性图表明,高和极高易发性区集中在研究区北部,且由东向西呈带状分布。[结论] 研究采取的基于多目标选择的RF-NSGA-II模型,为滑坡易发性评价中机器学习模型调优提供新思路。 相似文献
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[目的]基于机器学习算法对东非植被变化进行因子重要性分析,测度不同算法在各情况下的精度差异及适用性,为保护、恢复和促进可持续森林管理、水土流失综合防治提供科学依据。[方法]以东非9个国家2001—2020年的归一化植被指数(normalized difference vegetation index, NDVI)变化为研究对象,选取影响东非植被变化的2个气候因子及5个人类活动因子作为自变量,利用随机森林(random forest, RF)、BP神经网络(BP neural networks, BP)、支持向量机(support vector machines, SVM)、遗传算法(genetic algorithm, GA)、径向基神经网络(radial basis function, RBF)、卷积神经网络(convolutional neural networks, CNN)6种机器学习算法建立NDVI预测模型,以决定系数(R2)、平均绝对误差(MAE, mean absolute error)、平均相对误差(MRE, mean relative error... 相似文献
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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
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A map of soil texture profiles was derived from readily available spatial data in combination with information from soil profiles using CART (classification and regression trees). The primary purpose was to provide a regionalized predictor for the vertical hydraulic conductivity profiles to be used as an input variable to an evapo‐transpiration model. In contrast to former studies, the texture of 110 soil profiles taken in the 10 km2 area was not averaged vertically but the profiles were grouped according to their hydraulic properties. Therefore, it was possible to include site specific profiles, e.g. with histic or argillic horizons. Despite of small sampling quantities (110 soil profiles grouped into 8 classes) a prediction probability of 60 to 70 % was achieved in most classes. The resulting map provides valuable information for the granulometric and hydrologic characterization of the study area.<?show $6#> 相似文献
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基于Google Earth Engine和机器学习的耕地土壤有机质含量预测 总被引：2，自引：2，他引：0
土壤有机质含量是耕地质量定级的依据,是耕地质量评价的核心内容之一,因此,精准高效地获取土壤有机质含量非常重要。高分辨率遥感技术和谷歌地球引擎（Google Earth Engine,GEE）云计算平台的出现,为土壤有机质的高效反演提供了新的途径和方法。该研究以藁城区的Sentinel-2A MSI数据和Landsat8 OLI 数据为主要的数据源,结合Sentinel-1 SAR数据、ECMWF/ERA5气象数据和USGS/SRTMGL1_003高程数据,分别采用随机森林（Random Forest,RF）、梯度升级树（Gradient Boosting Decision Tree,GBDT）和支持向量机（Support Vector Machine,SVM）算法,在GEE平台对藁城耕地土壤有机质含量进行反演。结果表明：1）基于Sentinel-2A建立的模型（模型A*）在预测SOM含量方面优于基于Landsat8建立的模型（模型B*）,GDBT算法下的Sentinel-2A的全变量模型取得了最佳结果（R2=0.759,RMSE= 2.852 g/kg）;2）模型A-1对比模型A-0增加了红边波段,模型A-1比模型A-0提高了9.752%;3）从不同的预测算法来看,GDBT算法能较好地适用于研究区的土壤有机质预测,GDBT算法、Sentinel-2A与GEE的结合是SOM预测制图的一种有效方法。 相似文献
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S. M. Razgulin 《Eurasian Soil Science》2014,47(2):70-77
Net mineralization of nitrogen was measured in the horizons of peaty podzolic gley soil (At1, 0–6 cm, At2, 6–10 cm, and A2, 10–20 cm) of the bilberry-sphagnum birch forest from May to the beginning of November in 2011 and 2012; it comprised 2.1 ± 0.4, 2.7 ± 0.4, 1.2 ± 0.4, and 1.9 ± 0.2, 1.9 ± 0.2, 0.8 ± 0.15 g N/m2, respectively. The total of 6 ± 0.7 and 4.6 ± 0.3 g N/m2 were mineralized in the soil profile in the years 2011 and 2012, respectively, with the contribution of ammonification being more than 99%. The seasonal variations in nitrogen mineralization correlated with the temperature and soil moisture content in At1 and At2 horizons. The efficiency of nitrogen mineralization in soil horizons under the birch forest was expressed by similar values, namely, 0.005 ± 0.0008, 0.0064 ± 0.0008, and 0.003 ± 0.001 mg N/g C per day, and this suggests a high efficiency of substrate utilization in the eluvial part of the soil profile. The seasonal variation of carbon dioxide production correlated with the ammonification in the At1 horizon with r = 0.85 and p = 0.03 in 2011 and 2012, and in the At2 horizon with r = 0.65 and p = 0.16 in 2012. Over the studied period, production of ammonia depended on soil moistening in the dry year of 2011 (r = 0.91; p = 0.01) and soil temperature in the wet year of 2012 (r = 0.78; p = 0.06). The productivity of the process equaled 3 ± 0.9 mg N/m2, or 30 g N/ha in both years of observation. 相似文献
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Using support vector machines to predict cation exchange capacity of different soil horizons in Qingdao City,China 
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下载免费PDF全文 Agricultural, environmental and ecological modeling requires soil cation exchange capacity (CEC) that is difficult to measure. Pedotransfer functions (PTFs) are thus routinely applied to predict CEC from easily measured physicochemical properties (e.g., texture, soil organic matter, pH). This study developed the support vector machines (SVM)‐based PTFs to predict soil CEC based on 208 soil samples collected from A and B horizons in Qingdao City, Shandong Province, China. The database was randomly split into calibration and validation datasets in proportions of 3:1 using the bootstrap method. The optimal SVM parameters were searched by applying the genetic algorithm (GA). The performance of SVM models was compared to those of multiple stepwise regression (MSR) and artificial neural network (ANN) models. Results show that the accuracy of CEC predicted by SVM improves considerably over those predicted by MSR and ANN. The performance of SVM for B horizon (R2 = 0.85) is slightly better than that for A horizon (R2 = 0.81). The SVM is a powerful approach in the simulation of nonlinear relationship between CEC and physicochemical properties of widely distributed samples from different soil horizons. Sensitivity analysis was also conducted to explore the influence of each input parameter on the CEC predictions by SVM. The clay content is the most sensitive parameter, followed by soil organic matter and pH, while sand content has the weakest influence. This suggests that clay is the most important predictor for predicting CEC of both soil horizons. 相似文献
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《国际水土保持研究(英文)》2023,11(1):97-111
As a primary sediment source, gully erosion leads to severe land degradation and poses a threat to food and ecological security. Therefore, identification of susceptible areas is critical to the prevention and control of gully erosion. This study aimed to identify areas prone to gully erosion using four machine learning methods with derived topographic attributes. Eight topographic attributes (elevation, slope aspect, slope degree, catchment area, plan curvature, profile curvature, stream power index, and topographic wetness index) were derived as feature variables controlling gully occurrence from digital elevation models with four different pixel sizes (5.0 m, 12.5 m, 20.0 m, and 30.0 m). A gully inventory map of a small agricultural catchment in Heilongjiang, China, was prepared through a combination of field surveys and satellite imagery. Each topographic attribute dataset was randomly divided into two portions of 70% and 30% for calibrating and validating four machine learning methods, namely random forest (RF), support vector machines (SVM), artificial neural network (ANN), and generalized linear models (GLM). Accuracy (ACC), area under the receiver operating characteristic curve (AUC), root mean square error (RMSE), and mean absolute error (MAE) were calculated to assess the performance of the four machine learning methods in predicting spatial distribution of gully erosion susceptibility (GES). The results suggested that the selected topographic attributes were capable of predicting GES in the study catchment area. A pixel size of 20.0 m was optimal for all four machine learning methods. The RF method described the spatial relationship between the feature variables and gully occurrence with the greatest accuracy, as it returned the highest values of ACC (0.917) and AUC (0.905) at a 20.0 m resolution. The RF was also the least sensitive to resolutions, followed by SVM (ACC = 0.781–0.891, AUC = 0.724–0.861) and ANN (ACC = 0.744–0.808, AUC = 0.649–0.847). GLM performed poorly in this study (ACC = 0.693–0.757, AUC = 0.608–0.703). Based on the spatial distribution of GES determined using the optimal method (RF + pixel size of 20.0 m), 16% of the study area has very high level susceptibility classes, whereas areas with high, moderate, and low levels of susceptibility make up approximately 24%, 30%, and 31% of the study area, respectively. Our results demonstrate that GES assessment with machine learning methods can successfully identify areas prone to gully erosion, providing reference information for future soil conservation plans and land management. In addition, pixel size (resolution) is the key consideration when preparing suitable datasets of feature variables for GES assessment. 相似文献
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Spatial distribution of carbon (C) within a soil profile and across a landscape is influenced by many factors including vegetation, soil erosion, water infiltration, and drainage. For this reason, we attempted to determine the soil C distribution of an eroded soil. A three-dimensional (3D) map of a 0.72 ha field with a Dubuque silt loam soil which has three levels of erosion (slight, moderate, and severe) was developed using soil distribution and profile data collected using a profile cone penetrometer (PCP). This map displays the distribution of the total depth of the Ap and Bt1 horizons and the upper part of the 2Bt2 horizon. A map of soil C distribution was created for this landscape using C content information obtained from soil samples. Based on the C distribution in the upper two horizons, a 3D viewing was developed of soil C distribution for this eroded landscape. The 3D assessment of C distribution provides a better means of assessing the impact of soil erosion on C fate. It was estimated that there were 52 Mg ha−1 of total C in the surface (Ap) horizon and 61 Mg ha−1 in the Bt1 horizon for the 0.72 ha area. This increase in C with depth in the soil can be attributed to an increase in clay content and C leaching resulting in stable carbon–clay complexes. The C content was 16.0, 17.5, and 19.0 g kg−1 for the Ap horizon in the slight, moderate, and severe erosion levels, respectively. However, it was estimated that the total C amount in the respective Ap horizons was 28, 14, and 10 Mg ha−1 for the slight, moderate, and severe areas. The Bt1 horizon had 31, 19, and 11 Mg ha−1 of C in the slight, moderate, and severe areas, respectively. For the 0.72 ha area, 25% was severely eroded with 31 and 44% being moderate and slight, respectively. Soil C distribution information, such as that presented here, can be very valuable for soil management and could be used to determine possible C storage credits. 相似文献
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The lack of comprehensive data on the bulk density of soil types at the European scale is a serious limitation for pan‐European environmental risk assessment studies. Although many predictive methods have been published, most have limitations for application across Europe. We therefore developed a semi‐empirical method of prediction using a large UK dataset and tested it and some other methods against a pan‐European dataset. Our method indicated that five separate conceptual groupings of the development dataset were valid. Predictive equations based on multiple regression analysis for each of the five groups explained between 40 and 69% of the measured variation in each one. When used to predict measured bulk density from the European dataset, the equations explained 63% of the measured variation in mineral horizons from soil environments similar to those of the development dataset with a predictive mean percentage error of ±11%. The equation for organic horizons explained 29% of the measured variation in bulk density with a mean percentage error of ±39%. For those horizons from soil environments outside those of the development dataset, prediction of bulk density was relatively poor, even when using soil region‐specific PTFs derived from its data. It was concluded that, for these soils, factors other than organic carbon, particle size, horizon depth, mechanical cultivation or parent material have a major influence on bulk density and need further investigation. 相似文献
18.
基于Sentinel-2A的棉花种植面积提取及产量预测 总被引：1，自引：1，他引：0
及时、准确预测棉花产量在棉田经营管理、农业决策制定等方面具有重要的价值和意义。为了提高棉花产量预测精度并确定估产的最佳生育时期,该研究利用谷歌地球引擎（Google Earth Engine,GEE）获取2020年Sentinel-2A的3个时间段影像,采用随机森林（Radom Forest, RF）、支持向量机（Support Vector Machine, SVM）、决策树（Classification and Regression Tree, CART）进行棉花种植区域提取,利用顺序向前选择（Sequential Forward Selection, SFS）和偏最小二乘算法（Partial Least Squares Regression, PLSR）确定棉花产量预测最佳生育时期,最终形成莫索湾垦区棉花产量预测分布图。结果表明,1）RF分类效果最佳,农田与非农田分类总体精度为0.94,Kappa 系数为0.89;棉田与非棉田分类总体精度为0.92,Kappa 系数为0.83。2）红边波段（B6）在3个生育时期中与产量相关性较好,相关系数随着生育时期的递进而增加,分别为0.37、0.47、0.53。3）基于PLSR构建的产量预测模型中,铃期预测效果最佳（决定系数R2=0.62,均方根误差RMSE=625.5 kg/hm2,相对误差RE=8.87%）,优于吐絮期（R2=0.51,RMSE=789.45 kg/hm2,RE=11.06%）和花期（R2=0.48,RMSE=686.4 kg/hm2,RE=9.86%）,铃期为棉花产量预测的最佳生育时期。该研究利用GEE和Sentinel-2A影像数据,为新疆莫索湾垦区棉花种植面积提取及产量预测提供一种新的思路,可为合理水肥配置、精准种植、农作物生长过程监测提供数据支撑。 相似文献
19.
S. M. Razgulin 《Eurasian Soil Science》2013,46(2):127-134
The total mineralization of nitrogen in the AO-A1 (0–6 cm), A1 (6–11 cm), and A2 (11–21 cm) horizons of a soddy pale-podzolic soil under an oxalis birch forest in Yaroslavl oblast was measured from May to November in 2009 and 2010 and comprised 6.7 ± 0.9, 3.0 ± 0.4, and 5.5 ± 0.6 g of N/m2 in 2009 and 5.6 ± 0.5, 2.5 ± 0.2, and 2.1 ± 0.5 g of N/m2 in 2010, respectively. The total nitrification reached 0.4 ± 0.1, 1.1 ± 0.2, and 1.4 ±0.1 g of N/m2 in 2009 and 1.0, 0.6, and 0.7 g of N/m2 in 2010. Overall, the amount of mineralized nitrogen in the 21-cm-deep soil layer in 2009 and 2010 constituted 15.2 ± 1.1 and 10.2 ± 0.7 g of N/m2, respectively. The contribution of nitrification to the nitrogen mineralization amounted to 20%. The seasonal variations in the soil temperature and moistening affected the concentrations of ammonium in the upper horizons and the accumulation of ammonium in the AO-A1 and A1 horizons. The combined effect of the temperature and moisture controlled the ammonification in the AO-A1 horizon (R = 0.83 at p = 0.16 in 2010), the nitrification in all the studied horizons (R = 0.86 at p= 0.13 in 2009), and the ammonia emission from the soil surface (R = 0.92 at p = 0.06 in 2010). A correlation between the seasonal dynamics of the ammonification and the CO2 emission was found for the AO-A1 horizon (r = 0.64 at p = 0.16 in 2010) and was absent in the deeper layers of the soil profile. The nitrogen losses from the soil surface due to the ammonia emission in the investigated periods reached 95 ± 31 g of N/ha (2009) and 33 ± 30 g of N/ha (2010). 相似文献
20.
Data scarcity often prevents the estimate of regional (or national) scale soil organic carbon (SOC) stock and its spatial distribution. This study attempts to overcome the data limitations by combining two existing Irish soil databases [SoilC and national soil database (NSD)] at the national scale, to create an improved estimate of the national SOC stock. Representative regression models between the near‐surface SOC concentration and those of deeper depths, and between SOC concentration and bulk density (BD) were developed based on the SoilC database. These regression models were then applied to the NSD derived SOC concentration map, resulting in an improved SOC stock and spatial distribution map for the top 10 cm, 30 cm and 50 cm depths. Western Ireland, particularly coastal areas, was found to have higher SOC densities than eastern Ireland, corresponding to the spatial distribution of peatland. We estimated the national SOC stock at 383 ± 38 Tg for the near‐surface of 0–10 cm depth; 1016 ± 118 Tg for 0–30 cm depth; and 1474 ± 181 Tg for 0–50 cm depth. 相似文献