作者机构：
[Yu, Lei; Xu, Yueling; Zhou, Xueyan; Lv, Tianqi; Wang, Caiyun; Huang, Fan] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Yu, Lei; Xu, Yueling; Zhou, Xueyan; Lv, Tianqi; Wang, Caiyun; Huang, Fan] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Yu, L ] C;Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
摘要：
This study evaluates the effects of a combined rice-crayfish farming model and compares this model with traditional paddy fields. The focus is on soil aggregate characteristics, organic matter content, and also the distribution of soil aggregates. This research was conducted in Qianjiang, Hubei Province. The surface soil samples were collected from two types of arable land: paddy fields (WR) and rice-crayfish fields (CR). We performed an analysis of soil aggregate distribution and organic matter content. Results reveal that the majority of soil aggregates exceed 2 mm in size (>= 74.94%). The integrated rice-crayfish farming model significantly enhances the presence of large soil aggregates. And these parameters such as the average weight diameter (MWD), average geometric diameter (GWD), and agglomerate stability (PAD) also increase. Moreover, it mitigates agglomerate fragmentation (WASR). However, the net increase in total soil organic matter due to the integrated farming model remains modest. Organic matter content within the agglomerates follows an initial increase followed by a decrease. The highest content occurs in the 0.25-0.5 mm grain size (D4). When examining the distribution of soil aggregates and organic matter, it becomes evident that organic matter primarily originates from grain sizes larger than 2 mm (>= 71.92%). Notably, the rice-crayfish paddy field (CR) exhibits a substantially higher contribution compared to the traditional rice paddy field (WR). This study demonstrates several positive outcomes of the integrated rice-crayfish farming model compared to traditional paddy farming. It promotes the development of larger soil aggregates, enhances the structural integrity of soil aggregates, and improves their mechanical and hydrological stability. Additionally, it marginally increases the organic matter content within each component of soil aggregates. Furthermore, integrated modelling increases the impact of larger soil aggregates on soil organic matter. This improves the quality of the soil and as a result, crop yields are increased. The health of the soil is also improved and this contributes positively to food security.
期刊：
Environmental Science and Pollution Research,2023年30(42):96329-96349 ISSN：0944-1344
通讯作者：
Yu, J
作者机构：
[Li, Yimin; Nie, Yan; Yin, Chen; Zhou, Yong; Yu, Lei] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan 430079, Peoples R China.;[Li, Yimin; Qin, Hong; Nie, Yan; Yin, Chen; Zhou, Yong; Yu, Lei] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Yu, J; Yu, Jing] Hubei Univ, Hubei Key Lab Reg Dev & Environm Response, Wuhan 430062, Peoples R China.
通讯机构：
[Yu, J ] H;Hubei Univ, Hubei Key Lab Reg Dev & Environm Response, Wuhan 430062, Peoples R China.
关键词：
Arable land multifunction;Functional trade-offs;Root mean square deviation method;Ecological compensation;The West Mountain Regions of Hubei Province
作者机构：
[Nie Yan; Yu Lei; Yi Jun; Zhang Tao; Zhou Yong] Cent China Normal Univ, Hubei Prov Key Lab Anal & Simulat Geog Proc, Wuhan 430079, Hubei, Peoples R China.;[Nie Yan; Yu Lei; Yi Jun; Zhang Tao; Zhou Yong] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Hubei, Peoples R China.;[Nie Yan; Yu Lei; Yi Jun; Zhang Tao; Zhou Yong] Cent China Normal Univ, Res Inst Sustainable Dev, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Yu Lei] C;Cent China Normal Univ, Hubei Prov Key Lab Anal & Simulat Geog Proc, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Res Inst Sustainable Dev, Wuhan 430079, Hubei, Peoples R China.
关键词：
土壤高光谱;小波系数;小波能量特征;土壤有机质;水稻土
摘要：
土壤高光谱在采集过程中难以避免噪声干扰, 造成高光谱数据信噪比较低, 影响土壤有机质含量估测精度。 尝试探究小波能量特征方法, 降低高光谱噪声, 提升土壤有机质含量高光谱估测模型性能。 选取湖北省潜江市运粮湖管理区为试验区, 于2016年9月采集80份深度为0~20 cm的水稻土样本; 土壤样本经风干、 碾磨、 过筛等一系列处理后, 在实验室内采集样本光谱, 并通过重铬酸钾-外加热法测定土壤有机质含量; 利用浓度梯度法, 将总体样本集(80个样本)划分为建模集(54个样本)和验证集(26个样本); 以mexh为小波基函数进行连续小波变换(continuous wavelet transformation), 将土壤高光谱转换为10个分解尺度的小波系数(wavelet coefficients); 逐尺度计算小波系数的均方根作为小波能量特征(energy features), 将10个尺度的小波能量特征组成小波能量特征向量(energy features vector); 逐尺度逐波长计算小波系数与有机质含量的相关系数, 将达到极显著水平(p<0.01)的小波系数作为敏感小波系数(sensitive wavelet coefficients); 利用主成分分析法(principal component analysis)分别计算土壤高光谱和小波能量特征向量的各主成分载荷, 通过比较两者第一主成分贡献率的高低和两者前三个主成分得分的空间离散程度, 判断小波能量特征转换前后建模自变量的主成分信息变化趋势; 基于小波能量特征向量和敏感小波系数分别建立多元线性回归和偏最小二乘回归土壤有机质含量估测模型。 结果表明, 土壤有机质含量越高, 全波段反射率越低, 但不同土样的光谱反射率曲线特征相似, 近红外部分的反射率(780~2 400 nm)高于可见光部分(400~780 nm); 敏感小波系数对应的波长为494, 508, 672, 752, 1 838和2 302 nm; 土壤高光谱与小波能量特征向量的第一主成分贡献率分别为96.28%和99.13%, 小波能量特征向量的前三个主成分散点较土壤高光谱的主成分散点在空间上更为聚集, 表明小波能量特征方法有效减少了噪声影响; 比较全部土壤有机质含量估测模型, 以小波能量特征向量为自变量的多元线性回归模型具有最佳估测精度, 其验证集决定系数(R2)、 相对估测误差(RPD)和均方根误差(RMSE)分别为0.77, 1.82和0.82。 因此, 小波能量特征方法既能够提高数据的信噪比, 提升土壤有机质含量的估测精度, 又实现了土壤高光谱数据降维, 降低了模型复杂度, 可用于土壤有机质含量快速测定和土壤肥力动态监测等研究。 There is no silver-bullet solution of eliminating noise during the acquisition process of soil hyperspectral. As the noise interference, the observations of soil spectra are in low signal-to-noise ratio, which affects the estimation accuracy of soil organic matter content. This paper attempts to adopt the wavelet energy features method to reduce the noise in soil hyperspectral and improve the estimation accuracy of soil organic matter content. The Yunlianghu Farm of Qianjiang City, Hubei Province, located in the hinterland of Jianghan Plain, was selected as the experimental area, and 80 samples of paddy soil with a depth of 0~20 cm were collectedin September 2016. After pretreatment (air drying, milling, sieving), soil sample spectral reflectance and determine soil organic matter contentwere collected in the laboratory. The concentration gradient method was employed to divide the whole sample set (80 samples) into a calibration set (54 samples) and a validation set (26 samples). Continuous wavelet transformation was performed using mexh as a wavelet basis function, transforming the soil hyperspectral into sensitive wavelet coefficients of 10 decomposition scales. Then the root mean square of the wavelet coefficients was calculated scale by scaleto define wavelet energy features, and the wavelet energy features vector was determined by the wavelet energy features. The correlation coefficients between the wavelet coefficients and the organic matter content were calculated scale by scale and wavelength by wavelength, and the wavelet coefficient which reaches the extremely significant level (p<0.01) was defined as the sensitive wavelet coefficients. Principal component analysis was conducted to calculate the principal component loads of soil hyperspectral and wavelet energy features vector, respectively. The trend of principal component information of modeled independent variables before and after wavelet energy features transformation would be judged from the difference between the first principal component contribution rate and the spatial dispersion of the first three principal component scores degree. Moreover, regression models were established based on wavelet energy features vector and sensitive wavelet coefficients, respectively, to estimate soil organic matter content. The results showed that with the increase of soil organic matter content, the full-band reflectance decreased, but the spectral reflectance curves of different soil samples were similar, and the reflectance in the near-infrared bands (780~2 400 nm) was higher than that in the visible bands (400~780 nm). The sensitive wavelet coefficients corresponded to wavelengths of 494, 508, 672, 752, 1 838, and 2 302 nm. The first principal component contribution rates of soil hyperspectral and wavelet energy features vector were 96.28% and 99.13%, respectively. The first three principal component scatter points of wavelet energy features vector were more spatially aggregated than those of soil hyperspectral, which demonstrated that the wavelet energy features method effectively reduces the influence of noise. Comparing the estimation models of soil organic matter content, the multivariate linear regression model adopting wavelet energy features vector as the independent variable had the highest estimation accuracy, whose determination coefficients (R2), relative estimate deviation (RPD), and the root mean squared error (RMSE) of validation set were 0.77, 1.82, and 0.82, respectively. Therefore, the wavelet energy features method which is proved to raise the signal-to-noise ratio of the data without adding to the complexity, could improve the estimation accuracy of soil organic matter and realize the dimensional reduction of soil hyperspectral data. This method can be applied to studies like on-the-go soil properties measurement and soil quality monitoring.
关键词：
Estimation mechanism;Predictive model;Proximal soil sensing;Soil heavy metal;Spectral derivative
摘要：
Heavy metal contamination of peri-urban agricultural soil is detrimental to soil environmental quality and human health. A rapid assessment of soil pollution status is fundamental for soil remediation. Heavy metals can be monitored by visible and near-infrared spectroscopy coupled with chemometric models. First and second derivatives are two commonly used spectral preprocessing methods for resolving overlapping peaks. However, these methods may lose the detailed spectral information of heavy metals. Here, we proposed a fractional-order derivative (FOD) algorithm for preprocessing reflectance spectra. A total of 170 soil samples were collected from a typical peri-urban agricultural area in Wuhan City, Hubei Province. The reflectance spectra and lead (Pb) and zinc (Zn) concentrations of the samples were obtained in the laboratory. Two calibration methods, namely, partial least square regression and random forest (RF), were used to establish the relation between the spectral data and the two heavy metals. In addition, we aimed to explore the use of spectral estimation mechanism to predict the Pb and Zn concentrations. Three model evaluation parameters, namely, coefficient of determination (R(2)), root mean squared error, and ratio of performance to inter-quartile range (RPIQ), were used. Overall, the spectral reflectance decreased with the increase in Pb and Zn contents. The FOD algorithm gradually removed spectral baseline drifts and overlapping peaks. However, the spectral strength slowly decreased with the increase in fractional order. High fractional-order spectra underwent more spectral noises than low fractional-order spectra. The optimal prediction accuracies were achieved by the 0.25- and 0.5-order reflectance RF models for Pb (validation R(2)=0.82, RPIQ=2.49) and Zn (validation R(2)=0.83, RPIQ=2.93), respectively. A spectral detection of Pb and Zn mainly relied on their covariation with soil organic matter, followed by Fe. In summary, our results provided theoretical bases for the rapid investigation of Pb and Zn pollution areas in peri-urban agricultural soils.
摘要：
Accurate estimation of soil organic matter (SOM) is essential in understanding the spatial distribution of SOM to identify areas that need fertilization and the required grade of those fertilizers. Visible and near-infrared spectroscopy is a promising alternative to time consuming and costly conventional soil assessment methods. However, this approach is highly dependent on selecting suitable preprocessing strategies and data mining techniques for regression analysis. In this study, 2D correlation coefficients, including ratio, difference, and normalized difference indices, were introduced to select sensitive spectral parameters. The performance of extreme learning machine (ELM) was evaluated via comparison with that of support vector machine (SVM) for SOM estimation. A total of 257 soil samples were collected from Hubei Province, Central China, with SOM contents and reflectance spectra measured in the laboratory. Five spectral pretreatments, except for the raw spectra, were applied. SVM and ELM models were calibrated on spectral parameters selected by one-dimensional and 2D correlation coefficients and subsequently applied to predict SOM. Results showed that 2D correlation coefficient can effectively highlight the detailed SOM information compared with that of one-dimensional correlation coefficient. The ELM models yielded superior predictability relative to SVM models in all eight established models. The most excellent estimation accuracy was obtained by 2D ratio index and ELM (TRI-ELM) method, with an independent validation R-2 and a ratio of performance to interquartile range of 0.83 and 3.49, respectively. The SOM fertility levels of predicted SOM showed that TRI-ELM method presented the largest similarity to laboratory-measured SOM levels, and misclassified samples were all concentrated within one error level. In summary, our study indicates that the TRI-ELM model is a rapid, inexpensive, and relatively accurate method for identifying SOM fertility level. (c) 2018 Elsevier B.V. All rights reserved.
关键词：
Fuzzy k-means clustering;Normalized soil moisture index;Partial least square-support vector machine;Soil moisture;Soil organic matter;Visible and near-infrared spectroscopy
摘要：
Soil organic matter (SOM) is an important parameter of soil fertility, and visible and near-infrared (VIS-NIR) spectroscopy combined with multivariate modeling techniques have provided new possibilities to estimate SOM. However, the spectral signal is strongly influenced by soil moisture (SM) in the field. Interest in using spectral classification to predict soils in themoist conditions tominimize the influence of SMis growing. The objective of this study was to investigate the transferability of two approaches, SM-based clustermethod with known SM(classifying the VIS-NIR spectra into different SM clusters to develop models separately), the normalized soil moisture index (NSMI)-based cluster method with unknown SM(utilizing NSMI to indicate the SMand establish models separately), to predict SOM directly inmoist soil spectra. One hundred and twenty one soil sampleswere collected fromCentral China, and eight SM levels were obtained for each sample through rewetting experiments. Their reflectance spectra and SOMconcentrations were measured in the laboratory. Partial least square-support vector machine (PLS-SVM) was employed to construct SOMprediction models. Specifically, prediction models were developed for NSMI-based clusters with unknown SMdata. The models were assessed through three statistics in the processes of calibration and validation: the coefficient of determination (R<sup>2</sup>), root mean square error (RMSE) and the ratio of the performance to deviation (RPD). Results showed that the variable SMled to reduced VIS-NIR reflectance nonlinearly across the entire spectral range. NSMI was an effective spectral index to indicate the SM. Classifying the VIS-NIR spectra into different SMclusters in known SMstates could improve the performance of PLS-SVMmodels to acceptable prediction accuracies (R<sup>2</sup><inf>cv</inf>= 0.69-0.77, RPD = 1.79-2.08). The estimation of SOM, when using the NSMI-based cluster method with unknown SM(RPD = 1.95-2.04), was similar to the use of the SM-based cluster method with known SM (RPD = 1.79-2.08). The predictive results (RPD = 1.87-2.06) demonstrated that the NSMI-based cluster method has potential for application outside the laboratory for SOMprediction without knowing the SMexplicitly, and this method is also easy to carry out and only requires spectral information.<br/>
期刊：
Soil Science Society of America Journal,2018年82(5):1231-1242 ISSN：0361-5995
通讯作者：
Chen, Yiyun;Liu, Yanfang
作者机构：
[Hong, Yongsheng; Chen, Yiyun; Liu, Yanfang; Cheng, Hang; Liu, Yaolin; Liu, Yi] Wuhan Univ, Sch Resource & Environ Sci, Wuhan 430079, Hubei, Peoples R China.;[Hong, Yongsheng; Cheng, Hang; Chen, Yiyun] Chinese Acad Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Jiangsu, Peoples R China.;[Zhang, Yong] Anhui Univ Finance & Econ, Sch Publ Finance & Admin, Bengbu 233030, Peoples R China.;[Yu, Lei] Cent China Normal Univ, Sch Urban & Environ Sci, Wuhan 430079, Hubei, Peoples R China.;[Yu, Lei] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Chen, YY; Liu, YF] W;[Chen, Yiyun] C;Wuhan Univ, Sch Resource & Environ Sci, Wuhan 430079, Hubei, Peoples R China.;Chinese Acad Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Jiangsu, Peoples R China.
摘要：
Visible and near infrared (Vis-NIR) spectroscopy technique has been shown to be a cost-effective alternative for rapidly analyzing soil organic carbon (SOC). However, great challenges remain when applying a Vis-NIR model for SOC estimation developed in one study area to other study areas without further calibration. The scope of this study was to use spiking strategy to improve the transferability of Vis-NIR models between two study areas. Specifically, we explored the optimal spiking subset by adding different quantities of spiking samples to construct different-sized models, and the strategy of spiking with extra-weighting was used for comparison. Soil data was acquired in two independent study areas (WH area and HH area) in Hubei Province, Central China. The reflectance spectra and SOC contents were measured in the laboratory. Partial least squares regression (PLSR) was used for model calibration. The representativeness of the spiking samples was assessed through the absolute difference between the selected sample variance (s(2)) and the original variance (sigma(2)) in the principal component space derived from soil spectra. Results showed that the initial models yielded successful SOC predictions for the soil samples from the same area as the calibration samples, but failed in those samples from the other area. Spiking improved the model transferability between these two study areas. Approximately 33%/48% of the HH/WH calibration set was required as spiking samples in model calibrations and applications in the other area. Spiking with extra-weighting was of limited use in small-sized spectral libraries. The use of vertical bar s(2)-sigma(2)vertical bar is potentially effective in identifying the optimal spiking samples to improve model transferability between different small-sized study areas in the Vis-NIR assessments of SOC.
