河北铅锌尾矿库区土壤重金属含量高光谱反演方法对比

为更好地研究利用光谱反映的土壤重金属信息，实现具有多重金属复合污染问题的铅锌矿区土壤重金属含量高光谱快速估测，该研究以河北省某铅锌矿区为例，首先对研究区土壤的Cu、Cr、Ni、Zn、Cd、Pb污染状况进行了评价分析，其次基于实验室高光谱数据，组合变换光谱、特征变量和反演算法形成不同反演策略，通过各反演策略下的重金属反演精度比较，定量分析不同光谱预处理、特征选择和建模算法的优劣与适应性，构建最优反演模型。研究结果表明：1）研究区土壤Cr、Ni清洁程度较好，其余Cu、Zn、Cd、Pb均有不同程度污染；参比当地土壤背景值，区域内梅罗综合污染指数均值29.7，为重度污染，潜在生态风险因子均值1330.3，处于高生态风险状态；2）光谱预处理可以增强土壤重金属信息表达。其中，光谱微分效果较好，但易受噪声影响，而多元散射校正、标准正态变量、倒数对数变换可以进行光谱去噪，提升处理效果；3）特征选择方法中，相关系数法选择特征波段数目多，不同重金属反演R² 差异较大；Boruta法选择特征波段数目少，不同重金属反演R² 差异较小；4）BPNN、XGBoost可以较好描述重金属含量与光谱的非线性关系，相较于其他算法具有更好表现，分别实现了Cr、Ni、Zn和Pb、Cd的最优反演，SVMR实现了Cu的最优反演。研究表明，不同的光谱预处理、特征选择与建模算法对于土壤重金属含量的反演均具有较大影响，选择合适的处理、建模算法可以有效提升反演精度。该研究为进一步实现高效、准确、大范围遥感监测铅锌矿区土壤重金属污染状况提供参考依据。     

Application of VNIR and machine learning technologies to predict heavy metals in soil and pollution indices in mining areas

Purpose

Soil pollution indices are an effective tool in the computation of metal contamination in soil. They monitor soil quality and ensure future sustainability in agricultural systems. However, calculating a soil pollution index requires laboratory measurements of multiple soil heavy metals, which increases the cost and complexity of evaluating soil heavy metal pollution. Visible and near-infrared spectroscopy (VNIR, 350–2500 nm) has been widely used in predicting soil properties due to its advantages of a rapid analysis, non-destructiveness, and a low cost.

Methods

In this study, we evaluated the ability of the VNIR to predict soil heavy metals (As, Cu, Pb, Zn, and Cr) and two commonly used soil pollution indices (Nemerow integrated pollution index, NIPI; potential ecological risk index, RI). Three nonlinear machine learning techniques, including cubist regression tree (Cubist), Gaussian process regression (GPR), and support vector machine (SVM), were compared with partial least squares regression (PLSR) to determine the most suitable model for predicting the soil heavy metals and pollution indices.

Results

The results showed that the nonlinear machine learning models performed significantly better than the PLSR model in most cases. Overall, the SVM model showed a higher prediction accuracy and a stronger generalization for Zn (R²_V?=?0.95, RMSE_V?=?6.75 mg kg^?1), Cu (R²_V?=?0.95, RMSE_V?=?8.04 mg kg^?1), Cr (R²_V?=?0.90, RMSE_V?=?6.57 mg kg^?1), Pb (R²_V?=?0.86, RMSE_V?=?4.14 mg kg^?1), NIPI (R²_V?=?0.93, RMSE_V?=?0.31), and RI (R²_V?=?0.90, RMSE_V 3.88). In addition, the research results proved that the high prediction accuracy of the three heavy metal elements Cu, Pb, and Zn and their significant positive correlations with the soil pollution indices were the reason for the accurate prediction of NIPI and RI.

Conclusion

Using VNIR to obtain soil pollution indices quickly and accurately is of great significance for the comprehensive evaluation, prevention, and control of soil heavy metal pollution.

     

Rapid estimation of microbial biomass in acid red soils with and without substrate incorporation

Purpose

A rapid and alternative measurement of microbial biomass in acid red soils with and without substrate incorporation is proposed for soil quality evaluation.

Materials and methods

Soil microbial biomass C (SMBC) and N (SMBN) in 24 typical red soil samples developed from two parent materials (granite and arenaceous shale) were measured using fumigation-extraction followed by dry combustion method in comparison with ultraviolet (UV) spectrophotometry (increase in absorbance at 280 nm, ΔUV₂₈₀). The reliability of microbial biomass estimation by UV spectrophotometry was verified using six representative red soils amended with biochar (0, 1, 3 and 5%) and glucose (0, 100, 500 and 1000 mg kg^?1) separately.

Results and discussion

ΔUV₂₈₀ was strongly correlated with SMBC and SMBN measured by dry combustion, regardless of biochar/glucose incorporation. Validated conversion equations from unamended soil data were dependent on confounding effects of organic C and particle size and can be described as follows: SMBC?=?27.08?×?ΔUV₂₈₀ (R²?=?0.67, n?=?24) and SMBN?=?3.62?×?ΔUV₂₈₀ (R²?=?0.69, n?=?24). Regression models for rapid estimation of microbial biomass in red soils from different parent materials had to be calibrated separately in case of amendments. In most cases, SMBC (R² of 0.75–0.76 and root mean square error (RMSE) of 22.2–29.3 mg kg^?1) and SMBN (R² of 0.74–0.80 and RMSE of 2.60–14.2 mg kg^?1) can be predicted from ΔUV₂₈₀ in biochar/glucose-amended soils using these equations. The slope of the regression of SMBC against ΔUV₂₈₀ shifted in biochar-amended granite soils, mainly due to uncoordinated changes of SMBC in response to the difference in parent material-induced nutrient availability, while shifts of SMBC (or SMBN) against ΔUV₂₈₀ in glucose-amended arenaceous shale soils were attributed to particle size distribution.

Conclusions

Soil microbial biomass (SMBC and SMBN) in red soils can be rapidly predicted by fumigation-extraction with UV spectrophotometry detection and corresponding correction of calibration curves, depending on soil nutrient availability, particle size distribution and organic C levels.

     

基于可见/近红外光谱技术的土壤总磷分析研究

Overabundance of phosphorus (P) in soils and water is of great concern and has received much attention in Florida, USA. Therefore, it is essential to analyze and predict the distribution of P in soils across large areas. This study was undertaken to model the variation of soil total phosphorus (TP) in Florida. A total of 448 soil samples were collected from different soil types. Soil samples were analyzed by chemical reference method and scanned in the visible/near-infrared (VNIR) region of 350--2 500 nm. Partial least squares regression (PLSR) calibration model was developed between chemical reference values and VNIR values. The coefficient of determination (R²) and the root mean squares error (RMSE) of calibration and validation sets, and the residual prediction deviation (RPD) were used to evaluate the models. The R² in calibration and validation for log-transformed TP (log TP) were 0.69 and 0.65, respectively, indicating that VNIR calibration obtained in this study accounted for at least 65% of the variance in log TP using only VNIR spectra, and the high RPD of 2.82 obtained suggested that the spectral model derived in this study was suitable and robust to predict TP in a wide range of soil types, being representative of Florida soil conditions.     

Predicting Soil Organic Carbon and Total Nitrogen in the Russian Chernozem from Depth and Wireless Color Sensor Measurements

Color sensor technologies offer opportunities for affordable and rapid assessment of soil organic carbon (SOC) and total nitrogen (TN) in the field, but the applicability of these technologies may vary by soil type. The objective of this study was to use an inexpensive color sensor to develop SOC and TN prediction models for the Russian Chernozem (Haplic Chernozem) in the Kursk region of Russia. Twenty-one dried soil samples were analyzed using a Nix Pro? color sensor that is controlled through a mobile application and Bluetooth to collect CIEL*a*b* (darkness to lightness, green to red, and blue to yellow) color data. Eleven samples were randomly selected to be used to construct prediction models and the remaining ten samples were set aside for cross validation. The root mean squared error (RMSE) was calculated to determine each model’s prediction error. The data from the eleven soil samples were used to develop the natural log of SOC (lnSOC) and TN (lnTN) prediction models using depth, L*, a*, and b* for each sample as predictor variables in regression analyses. Resulting residual plots, root mean square errors (RMSE), mean squared prediction error (MSPE) and coefficients of determination (R², adjusted R²) were used to assess model fit for each of the SOC and total N prediction models. Final models were fit using all soil samples, which included depth and color variables, for lnSOC (R² = 0.987, Adj. R² = 0.981, RMSE = 0.003, p-value < 0.001, MSPE = 0.182) and lnTN (R² = 0.980 Adj. R² = 0.972, RMSE = 0.004, p-value < 0.001, MSPE = 0.001). Additionally, final models were fit for all soil samples, which included only color variables, for lnSOC (R² = 0.959 Adj. R² = 0.949, RMSE = 0.007, p-value < 0.001, MSPE = 0.536) and lnTN (R² = 0.912 Adj. R² = 0.890, RMSE = 0.015, p-value < 0.001, MSPE = 0.001). The results suggest that soil color may be used for rapid assessment of SOC and TN in these agriculturally important soils.     

应用拉曼和中红外衰减全反射光谱测定溶液和土壤中的硝酸盐

为比较拉曼光谱和红外光谱在溶液和土壤中硝酸盐含量定量分析的适用性,采用两种光谱对溶液和土壤中的NO3–-N含量(0~200 mg/L)进行快速测定。结果表明,溶液中硝酸盐的拉曼特征峰在1 047 cm–1处,该特征峰强度与NO3–-N浓度成正比,对1 035~1 060 cm-1波段拉曼光谱峰面积和NO3–-N含量进行线性回归,决定系数R2为0.995 4;溶液中硝酸盐的中红外衰减全反射光谱特征吸收峰在1 350 cm–1,吸收峰与NO3–-N含量成正比,特征吸收区1 200~1 500 cm–1峰面积与NO3–-N含量的决定系数R2为0.991 1,表明两种光谱都可用于溶液中硝酸盐的测定。对于土壤样品,红外光谱在1 250~1 500 cm–1处有硝酸盐吸收峰,且吸收峰与NO3–-N含量成正比,峰面积与NO3–-N含量之间的决定系数R2为0.968 4;而对于拉曼光谱,硝酸盐的拉曼峰因受较强干扰导致吸收峰不明显,峰面积与NO3–-N含量之间的决定系数R2仅为0.000 9,表明中红外衰减全反射光谱可用于土壤中硝酸盐的测定,而拉曼光谱则很困难。因此,拉曼光谱和中红外衰减全反射光谱都可用于溶液中硝酸盐的测定,且前者灵敏度要高于后者;中红外衰减全反射光谱可用于土壤中硝酸盐的测定,而拉曼光谱难以用于土壤中硝酸盐定量分析,这为硝酸盐的快速测定提供理论依据和技术支持。     

Transferability and scalability of soil total carbon prediction models in Florida, USA

The applicability, transferability, and scalability of visible/near-infrared (VNIR)-derived soil total carbon (TC) models are still poorly understood. The objectives of this study were to: i) compare models of three multivariate statistical methods, partial least squares regression (PLSR), support vector machine (SVM), and random forest methods, to predict soil logarithm-transformed TC (logTC) using five fields (local scale) and a pooled (regional-scale) VNIR spectral dataset (a total of 560 TC spectral datasets), ii) assess the model transferability among fields, and iii) evaluate their up- and downscaling behaviors in Florida, USA. The transferability and up- and downscaling of the models were limited by the following factors: i) the spectral data domain, ii) soil attribute domain, iii) methods that describe the internal model structure of VNIR-TC relationships, and iv) environmental domain space of attributes that control soil carbon dynamics. All soil logTC models showed excellent performance based on all three methods with R² > 0.86, bias < 0.01%, root mean squared error (RMSE) = 0.09%, residual predication deviation (RPD) > 2.70%, and ratio of prediction error to interquartile range (RPIQ) > 4.54. The PLSR method performed substantially better than the SVM method to scale and transfer the TC models. This could be attributed to the tendency of SVM to overfit models, while the asset of the PLSR method was its robustness when the models were validated with independent datasets, transferred, and/or scaled. The upscaled soil TC models performed somewhat better in terms of model fit (R²), RPD, and RPIQ, whereas the downscaled models showed less bias and smaller RMSE based on PLSR. We found no universal trend indicating which of the four limiting factors mentioned above had the most impact that constrained the transferability and scalability of the models. Given that several factors can impinge on the empirically derived soil spectral prediction models, as demonstrated by this study, more focus on their applicability and scalability is needed.     

Near-Infrared-Spectroscopy for Determination of Carbon and Nitrogen in Indian Soils

The present study aims to evaluate the potential of near-infrared reflectance (NIR) spectroscopy to determine the carbon and nitrogen content in soils and also to assess the effectiveness of NIR spectroscopy to predict carbon and nitrogen content in freshly collected soil samples. Soil samples (n = 179) were collected from different locations in India. Soil carbon and nitrogen contents were successfully predicted (R² = 0.90 for carbon and R² = 0.85 for nitrogen) by NIR spectroscopy. The root mean square error (RMSE) and ratio performance deviation (RPD) for the validation of predicted equations for carbon and nitrogen were 0.83 and 2.83 and 0.01 and 6.98, respectively. The efficacy of NIR spectroscopy on the prediction of carbon and nitrogen content in Indian soils is highly reliable. Water content in soil samples could affect the NIR absorbance spectra and in turn affect the quantification of carbon and nitrogen.     

Prediction of macronutrients in plant leaves using chemometric analysis and wavelength selection

Purpose

Fast and real-time prediction of leaf nutrient concentrations can facilitate decision-making for fertilisation regimes on farms and address issues raised with over-fertilisation. Cacao (Theobroma cacao L.) is an important cash crop and requires nutrient supply to maintain yield. This project aimed to use chemometric analysis and wavelength selection to improve the accuracy of foliar nutrient prediction.

Materials and methods

We used a visible-near infrared (400–1000 nm) hyperspectral imaging (HSI) system to predict foliar calcium (Ca), potassium (K), phosphorus (P) and nitrogen (N) of cacao trees. Images were captured from 95 leaf samples. Partial least square regression (PLSR) models were developed to predict leaf nutrient concentrations and wavelength selection was undertaken.

Results and discussion

Using all wavelengths, Ca (R²_CV?=?0.76, RMSE_CV?=?0.28), K (R²_CV?=?0.35, RMSE_CV?=?0.46), P (R²_CV?=?0.75, RMSE_CV?=?0.019) and N (R²_CV?=?0.73, RMSE_CV?=?0.17) were predicted. Wavelength selection increased the prediction accuracy of Ca (R²_CV?=?0.79, RMSE_CV?=?0.27) and N (R²_CV?=?0.74, RMSE_CV?=?0.16), while did not affect prediction accuracy of foliar K (R²_CV?=?0.35, RMSE_CV?=?0.46) and P (R²_CV?=?0.75, RMSE_CV?=?0.019).

Conclusions

Visible-near infrared HSI has a good potential to predict Ca, P and N concentrations in cacao leaf samples, but K concentrations could not be predicted reliably. Wavelength selection increased the prediction accuracy of foliar Ca and N leading to a reduced number of wavelengths involved in developed models.

     

A comparison of indexes to estimate corn S uptake and S mineralization in the field

The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (S_uptake) and apparent S mineralization (S_min-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (S_min-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (S_min-7d?+?S_inorg), and N mineralized during a short-term (7 days) anaerobic incubation (N_an). Additionally, 18 field experiments were carried out to quantify S_uptake and S_min-app. The C-PF, S_min-7d?+?S_inorg, N_an, and SOC were variables significantly correlated with S_min-10wk (r?=?0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict S_min-10wk from selected edaphic variables (S_min-10wk?=?0.038*N_an?+?0.106*SOC?+?0.74; R_a² =?0.87). The S_min-10wk, C-PF, and S_min-7d?+?S_inorg showed a liner-plateau association with S_uptake (R²?=?0.73, 0.53, and 0.48, respectively). We modified the method to estimate S_min-app to account for S losses (S_{min-app (modified)}) and developed a model to predict S_{min-app (modified)} from C-PF (S_{min-app (modified)}?=?4.65*C-PF?+?9.86; R²?=?0.62) or S_min-10wk (S_{min-app (modified)}?=?3.0*S_min-10wk?+?7.4; R²?=?0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.     

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.     

基于BP神经网络的不同人为干扰强度下盐渍土SO42-定量分析

SO_4~(2-)是盐渍土阴离子中的主要离子,但目前针对不同人为干扰区域土壤中SO_4~(2-)反演研究却鲜有报道。土壤高光谱与土壤某元素间的关系表现为非线性,传统线性偏最小二乘模型(PLSR)对土壤元素的反演精度有限。本文以新疆昌吉回族自治州境内不同人为干扰区域的盐渍化土壤为研究对象,以土壤的野外高光谱和SO_4~(2-)含量为数据源,对原始(R)和对数(LogR)变换后的高光谱分别进行0阶、一阶和二阶微分预处理,选择通过0.05显著性水平的波段为敏感波段,将敏感波段对应的高光谱反射率作为非线性BP神经网络模型的输入变量,并设定BP的隐藏节点为300,学习速率为0.01,最大迭代次数为1 000,训练函数为trainscg。从SO_4~(2-)的真实值与预测值的散点图、拟合效果图和BP训练过程3个方面,定量分析无人为干扰(A区)和有人为干扰(B区)土壤SO_4~(2-)含量,并与PLSR对比预测精度。仿真显示, A区二阶微分后的BP预测精度优于一阶微分,而B区一阶微分后的BP预测精度优于二阶微分。且不论在A区还是B区, LogR光谱变换的反演精度均优于R。最佳BP模型的相对预测性能(RPD)、决定系数(R2)、均方根误差(RMSE)和迭代次数,在A区分别为3.309、0.906、0.253和8次,在B区分别为2.234、0.844、0.786和45次,表明BP对A区SO_4~(2-)的预测能力非常强(RPD2.5),对B区SO_4~(2-)的预测能力较强(RPD为2.0～2.5)。而在A区和B区两种光谱变换的一阶和二阶微分中, PLSR的RPD值均在1.4与1.8之间,其预测性能一般;在B区的0阶微分中, PLSR的RPD值均小于1.0,其不能对SO_4~(2-)进行预测。因此, BP模型能对不同人为干扰区域的SO_4~(2-)进行有效的定量分析。     

Major controlling factors and prediction models for mercury transfer from soil to carrot

Purpose

Soil-plant transfer models are needed to predict levels of mercury (Hg) in vegetables when evaluating food chain risks of Hg contamination in agricultural soils.

Materials and methods

A total of 21 soils covering a wide range of soil properties were spiked with HgCl₂ to investigate the transfer characteristics of Hg from soil to carrot in a greenhouse experiment. The major controlling factors and prediction models were identified and developed using path analysis and stepwise multiple linear regression analysis.

Results and discussion

Carrot Hg concentration was positively correlated with soil total Hg concentration (R ²?=?0.54, P?<?0.001), and the log-transformation greatly improved the correlation (R ²?=?0.76, P?<?0.001). Acidic soil exhibited the highest bioconcentration factor (BCF) (ratio of Hg concentration in carrot to that in soil), while calcareous soil showed the lowest BCF among the 21 soil types. The significant direct effects of soil total Hg (Hg_soil), pH, and free Al oxide (Al_OX) on the carrot Hg concentration (Hg_carrot) as revealed by path analysis were consistent with the result from stepwise multiple linear regression that yielded a three-term regression model: log [Hg_carrot]?=?0.52log [Hg_soil]???0.06pH???0.64log [Al_OX]???1.05 (R ²?=?0.81, P?<?0.001).

Conclusions

Soil Hg concentration, pH, and Al_OX content were the three most important variables associated with carrot Hg concentration. The extended Freundlich-type function could well describe Hg transfer from soil to carrot.     

Soil properties prediction of western Mediterranean islands with similar climatic environments by means of mid‐infrared diffuse reflectance spectroscopy

This study investigated the suitability of mid‐infrared diffuse reflectance Fourier transform (MIR‐DRIFT) spectroscopy, with partial least squares (PLS) regression, for the determination of variations in soil properties typical of Italian Mediterranean off‐shore environments. Pianosa, Elba and Sardinia are typical of islands from this environment, but developed on different geological substrates. Principal components analysis (PCA) showed that spectra could be grouped according to the soil composition of the islands. PLS full cross‐validation of soil property predictions was assessed by the coefficient of determination (R²), the root mean square error of cross‐validation and prediction (RMSECV and RMSEP), the standard error (SECV for cross‐validation and SEP for prediction), and the residual predictive deviation (RPD). Although full cross‐validation appeared to be the most accurate (R² = 0.95 for organic carbon (OC), 0.96 for inorganic carbon (IC), 0.87 for CEC, 0.72 for pH and 0.74 for clay; RPD = 4.4, 6.0, 2.7, 1.9 and 2.0, respectively), the prediction errors were considered to be optimistic and so alternative calibrations considered to be more similar to ‘true’ predictions were tested. Predictions using individual calibrations from each island were the least efficient, while predictions using calibration selection based on a Euclidian distance ranking method, using as few as 10 samples selected from each island, were almost as accurate as full cross‐validation for OC and IC (R² = 0.93 for OC and 0.96 for IC; RPD = 3.9 and 4.7, respectively). Prediction accuracy for CEC, pH and clay was less accurate than expected, especially for clay (R² = 0.73 for CEC, 0.50 for pH and 0.41 for clay; RPD = 1.8, 1.5 and 1.4, respectively). This study confirmed that the DRIFT PLS method was suitable for characterizing important properties for soils typical of islands in a Mediterranean environment and capable of discriminating between the variations in soil properties from different parent materials.     

基于地类分层的土壤有机质光谱反演校正样本集的构建

以江汉平原滨湖地区不同土地利用类型的土壤样本为例,比较了基于目标土壤理化性质的浓度梯度法、扩展的基于多种理化性质的综合法(P-KS)、基于光谱信息的KS法、最邻近样本去除法(reduce nearest neighbor samples,RNNS)法和基于浓度分层并结合光谱信息的C-KS、C-RNNS法,基于地类分层再结合上述方法,构建具有不同层次土壤信息代表性的校正集,采用偏最小二乘回归法,建立土壤有机质可见光/近红外光谱反演模型。结果表明,具有单一代表性的浓度梯度法、KS法、RNNS法难以建立适用模型;具有光谱与理化性质二元代表性的C-KS方法模型预测精度得到了明显的提升,相对分析误差(ratio of performance to standard deviation,RPD)为1.66;考虑土地利用类型后,浓度梯度法、RNNS法与C-KS法模型预测精度有明显的提升,RPD分别达到了1.84、1.51、1.75,模型具有良好的适用性。说明具有多层次土壤信息代表性的校正集构建方法对提高土壤有机质可见光/近红外光谱反演模型的适用性具有较好作用。     

Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms

The development of accurate calibration models for selected soil properties is a crucial prerequisite for successful implementation of visible and near infrared (Vis‐NIR) spectroscopy for soil analysis. This paper compares the performance of calibration models developed for individual farms with that of general models valid for three farms in three European countries. Fresh soil samples collected from farms in the Czech Republic, Germany and Denmark were scanned with a fibre‐type Vis‐NIR spectrophotometer. After dividing spectra into calibration (70%) and validation (30%) sets, spectra in the calibration set were subjected to partial least squares regression (PLSR) with leave‐one‐out cross‐validation to establish calibration models of soil properties. Except for the Czech Republic farm, individual farm models provided successful calibration for total carbon (TC), total nitrogen (TN) and organic carbon (OC), with coefficients of determination (R²) of 0.85–0.93 and 0.74–0.96 and residual prediction deviations (RPD) of 2.61–3.96 and 2.00–4.95 for the cross‐validation and independent validation respectively. General calibration models gave improved prediction accuracies compared with models of farms in the Czech Republic and Germany, which was attributed to larger ranges in the variation of soil properties in general models compared with those in individual farm models. The results revealed that larger standard deviations (SDs) and wider variation ranges have resulted in larger R² and RPD, but also larger root mean square errors of prediction (RMSEP). Therefore, a compromise solution, which also results in small RMSEP values, should be found when selecting soil samples for Vis‐NIR calibration to cover a wide variation range.     

Photochemical Formation of Hydroxyl Radicals in Red Soil-Polluted Seawater on the North of Okinawa Island, Japan

Land development has caused runoff of red soil into the ocean on the north side of Okinawa Island, Japan. In an attempt to clarify the impacts of this “red soil pollution” on the oxidizing power of seawater, we studied the formation of hydroxyl radical (?OH), the most potent oxidant in the environment, in red soil-polluted waters using a 313-nm monochromatic light. ?OH was photochemically formed in the red soil-polluted water samples, and the formation rates of ?OH decreased as salinity increased, i.e., as red soil-polluted river water gets mixed with seawater. The photo-formation rates of ?OH showed good correlations with dissolved Fe concentrations (R ²?=?0.96) and [NO₂ ^?]?+?[NO₃ ^?] concentrations (R ²?=?0.87), while a negative and weak correlation was found with dissolved organic carbon concentrations (R?=??0.78). Theoretical calculation showed that direct photolysis of NO₃ ^?, Fe(OH)²⁺, and hydrogen peroxide all together accounted for less than 10% of the observed ?OH formation in the red soil-polluted waters. Comparison between filtered and unfiltered samples showed that red soil particles were not the main sources of ?OH, and the photolysis of NO₂ ^? could account for at most 78% of the observed ?OH formation rates. We found that the Fenton’s reaction (a reaction between Fe(II) and H₂O₂) could possibly account for the observed formation of ?OH in the red soil-polluted waters.     

Spatial and seasonal variations in soil respiration in a temperate deciduous forest with fluctuating water table

Our objectives were to determine both spatial and temporal variations in soil respiration of a mixed deciduous forest, with soils exhibiting contrasting levels of hydromorphy. Soil respiration (R_S) showed a clear seasonal trend that reflected those of soil temperature (T_S) and soil water content (W_S), especially during summer drought. Using a bivariate model (RMSE=1.03), both optimal soil water content for soil respiration (W_SO) and soil respiration at both 10 °C and optimal soil water content (R_S10) varied among plots, ranging, respectively, from 0.25 to 0.40 and from 2.30 to 3.60 μmol m⁻² s⁻¹. Spatial variation in W_SO was related to bulk density and to topsoil N content, while spatial variation in R_S10 was related to basal area and the difference in pH measured in water or KCl suspensions. These results offer promising perspectives for spatializing ecosystem carbon budget at the regional scale.     

Rapid Analysis of Apple Leaf Nitrogen using Near Infrared Spectroscopy and Multiple Linear Regression

A quick method was developed for diagnosis of nitrogen (N) in apple trees based on multiple linear regressions to establish the relationship between near-infrared reflectance spectra (NIRS) and the N contents of fresh and dry tissue. Spectral pretreatment methods such as derivatives, smoothing, and normalization were used. The derivatives appeared to be the most effective. The best calibration for fresh leaf gave 0.842 for the correlation coefficient of validation (R_v), 1.119 g kg^?1 for the root mean square error of prediction (RMSEP), and 8.311 for the ratio of the range in reference data from the validation samples to the root mean square error of prediction (RER). The best calibration for dried ground samples was obtained with R_v = 0.952, RMSEP = 0.633 g kg^?1, the ratio performance deviation (RPD) = 3.27, and RER = 13.728. The results showed that calibrations of dry-apple leaf are robust enough for an accurate prediction of N.     

Assessment of the relative uptake of added and indigenous soil nitrogen by nodulated legumes and reference plants in the 15N dilution measurement of N2 fixation: Glasshouse application of method

A method for calculating the relative uptake (R) of added N and indigenous soil N by a legume (Trifolium subterraneum) and non-legume (Lolium rigidum), growing together, was investigated in two pot experiments. In the first experiment, ¹⁵N-labelled sodium nitrate was applied to the soil surface at rates equivalent to 0.3 or 1.0kg N ha^?1. Twenty one days later, the legume had fixed about 95% of its total N and this was unaffected by N addition. There was no difference in R values between legume and non-legume at both N rates.In the second experiment using a soil of higher total N, sodium nitrate or ammonium sulphate were surface-applied at a rate equivalent to 1 kg N ha^?1 and harvests were made at 3, 6, 12 and 27 days after N addition. Fixation of atmospheric N₂ by the legume did not begin until day 12 but accounted for about 40% of the total N assimilated by the legume by day 27. There was no difference in R values between legume and non-legume throughout the growth period when sodium nitrate was applied. However, when ammonium sulphate was added to label to soil N, the uptake of added N relative to indigenous soil N was greater for the non-legume than the legume. This caused an overestimation (51 vs 43%) of the proportion on N fixed by the legume when compared with that for the control or sodium nitrate treatments.