Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to ...preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy fields in the Yangtze Plain, China. Four machine learning approaches-partial least squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) and the Cubist regression model (Cubist)-were used to compare the prediction accuracy based on vis-NIR full bands and bands reduced using the genetic algorithm (GA). The coefficient of determination (R²), root mean square error (RMSE), and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy. The ELM with GA reduced bands was the best model for SOM (SOM: R² = 0.81, RMSE = 5.17, RPIQ = 2.87) and pH (R² = 0.76, RMSE = 0.43, RPIQ = 2.15). The performance of the LS-SVM for pH prediction did not differ significantly between the model with GA (R² = 0.75, RMSE = 0.44, RPIQ = 2.08) and without GA (R² = 0.74, RMSE = 0.45, RPIQ = 2.07). Although a slight increase was observed when ELM were used for prediction of SOM and pH using reduced bands (SOM: R² = 0.81, RMSE = 5.17, RPIQ = 2.87; pH: R² = 0.76, RMSE = 0.43, RPIQ = 2.15) compared with full bands (R² = 0.81, RMSE = 5.18, RPIQ = 2.83; pH: R² = 0.76, RMSE = 0.45, RPIQ = 2.07), the number of wavelengths was greatly reduced (SOM: 201 to 44; pH: 201 to 32). Thus, the ELM coupled with reduced bands by GA is recommended for prediction of properties of paddy soil (SOM and pH) in the middle-lower Yangtze Plain.
In this study we systematically reviewed 1203 research papers published between 2008 and 2018 in China and recorded related data on eight kinds of soil heavy metals (Cr, Pb, Cd, Hg, As, Cu, Zn, and ...Ni). Based on that, the pollution levels, ecological risk and health risk caused by soil heavy metals were evaluated and the pollution hot spots and potential driving factors of different heavy metals in different provinces were also identified. Results indicated accumulation of heavy metals in soils of most provinces in China compared with background values. Consistent with previous findings, the most prevalent polluted heavy metals were Cd and Hg. Polluted regions are mainly located in central, southern and southwestern China. Hunan, Guangxi, Yunnan, and Guangdong provinces were the most polluted provinces. For the potential health risk caused by heavy metals pollution, children are more likely confront with non-carcinogenic risk than adults and seniors. And children in Hunan and Guangxi province were experiencing relatively larger non-carcinogenic risk. In addition, children in part of provinces were undergoing potentially carcinogenic risks due to soil heavy metals exposure. Furthermore, in our study the 31 provinces in mainland China were divided into six subsets according to corresponding potential driving factors for heavy metal accumulation. Our study provide more comprehensive and updated information for contributing to better soil management, soil remediation, and soil contamination control in China.
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•Cd and Hg were the most polluted heavy metals in soil across China.•The ER values for Cd in 30 provinces varied from moderate to very high risk.•Hunan, Guangxi, Yunnan and Guangdong were confirmed as priority control provinces.•Children are undergo larger health risk than adults and seniors in China.•Controlling factors of heavy metals accumulation in different provinces were mapped.
Rapid heavy metal soil surveys at large scale with high sampling density could not be conducted with traditional laboratory physical and chemical analyses because of the high cost, low efficiency and ...heavy workload involved. This study explored a rapid approach to assess heavy metals contamination in 301 farmland soils from Fuyang in Zhejiang Province, in the southern Yangtze River Delta, China, using portable proximal soil sensors. Portable X-ray fluorescence spectroscopy (PXRF) was used to determine soil heavy metals total concentrations while soil pH was predicted by portable visible-near infrared spectroscopy (PVNIR). Zn, Cu and Pb were successfully predicted by PXRF (R2 >0.90 and RPD >2.50) while As and Ni were predicted with less accuracy (R2 <0.75 and RPD <1.40). The pH values were well predicted by PVNIR. Classification of heavy metals contamination grades in farmland soils was conducted based on previous results; the Kappa coefficient was 0.87, which showed that the combination of PXRF and PVNIR was an effective and rapid method to determine the degree of pollution with soil heavy metals. This study provides a new approach to assess soil heavy metals pollution; this method will facilitate large-scale surveys of soil heavy metal pollution.
Agricultural pollution poses a considerable challenge to grain security and human health, especially in economically developed areas. Mineral exploitation, chemical enterprise operation, pesticide ...and fertilizer application, sewage discharge, and vehicle emissions are the pollution sources of agricultural land. Identifying and assessing potential agricultural pollution (PAP) is, therefore, the most urgent task to achieve grain security and the human health. Large-scale (e.g., regional or national) PAP assessment can be very expensive, which could also generate a certain amount of information that usually discourages evaluation by decision-makers. To identify areas for regional priority investigation, here we proposed an assessment framework for PAP in economically developed areas. The framework consisted of PAP assessment, vulnerability assessment, hazard assessment, and socio-economic assessment. Then, we conducted a case study by using the proposed framework in one of China’s economically developed areas, Zhejiang Province. The results showed that PAP, especially soil heavy metal pollution, soil acidification, and surface water pollution involved almost the entire study area. High-vulnerability high-hazard areas were mainly associated with high socio-economic development or high grain yield. These areas had negatively affected grain security and increased carcinogenic risk, potentially contributing to the formation of cancer villages. Based on the results, we proposed measures for environmental risk managers to alleviate the impact of PAP on grain security and human health in economically developed areas.
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•A new assessment framework for potential agricultural pollution (PAP) was proposed.•Exposure to PAP impacted grain security and human health.•Hazard of mines and chemical companies in economically developed areas were highlighted.•The distribution of centers of grain production shifted toward PAP areas in Zhejiang.
The proposed assessment framework can effectively assess priority investigation areas of PAP in China’s economically developed areas.
Soils constitute one of the most critical natural resources and maintaining their health is vital for agricultural development and ecological sustainability, providing many essential ecosystem ...services. Driven by climatic variations and anthropogenic activities, soil degradation has become a global issue that seriously threatens the ecological environment and food security. Remote sensing (RS) technologies have been widely used to investigate soil degradation as it is highly efficient, time-saving, and broad-scope. This review encompasses recent advances and the state-of-the-art of ground, proximal, and novel RS techniques in soil degradation-related studies. We reviewed the RS-related indicators that could be used for monitoring soil degradation-related properties. The direct indicators (mineral composition, organic matter, surface roughness, and moisture content of soil) and indirect proxies (vegetation condition and land use/land cover change) for evaluating soil degradation were comprehensively summarized. The results suggest that these above indicators are effective for monitoring soil degradation, however, no indicators system has been established for soil degradation monitoring to date. We also discussed the RS's mechanisms, data, and methods for identifying specific soil degradation-related phenomena (e.g., soil erosion, salinization, desertification, and contamination). We investigated the potential relations between soil degradation and Sustainable Development Goals (SDGs) and also discussed the challenges and prospective use of RS for assessing soil degradation. To further advance and optimize technology, analysis and retrieval methods, we identify critical future research needs and directions: (1) multi-scale analysis of soil degradation; (2) availability of RS data; (3) soil degradation process modelling and prediction; (4) shared soil degradation dataset; (5) decision support systems; and (6) rehabilitation of degraded soil resource and the contribution of RS technology. Because it is difficult to monitor or measure all soil properties in the large scale, remotely sensed characterization of soil properties related to soil degradation is particularly important. Although it is not a silver bullet, RS provides unique benefits for soil degradation-related studies from regional to global scales.
Soil visible and near-infrared (Vis-NIR, 350–2500 nm) spectroscopy has been proven as an alternative to conventional laboratory analysis due to its advantages being rapid, cost-effective, ...non-destructive and environmentally friendly. Different variable selection methods have been used to deal with the high redundancy, heavy computation, and model complexity of using full spectra in spectral modelling. However, most previous studies used a linear algorithm in the variable selection, and the application of a non-linear algorithm remains poorly explored. To address the current knowledge gap, based on a regional soil Vis-NIR spectral library (1430 soil samples), we evaluated seven variable selection algorithms together with three predictive algorithms in predicting seven soil properties. Our results showed that Cubist overperformed partial least squares regression (PLSR) and random forests (RF) in most soil properties (R2 > 0.75 for soil organic matter, total nitrogen and pH) when using the full spectra. Most of variable selection can greatly reduce the number of spectral bands and therefore simplified predictive models without losing accuracy. The results also showed that there was no silver bullet for the optimal variable selection algorithm among different predictive algorithms: (1) competitive adaptive reweighted sampling (CARS) always performed best for the PLSR algorithm, followed by forward recursive feature selection (FRFS); (2) recursive feature elimination (RFE) and genetic algorithm (GA) generally had better accuracy than others for the Cubist algorithm; and (3) FRFS had the best model performance for the RF algorithm. In addition, the performance was generally better when the algorithm used in the variable selection matched the predictive algorithm. The outcome of this study provides a valuable reference for predicting soil information using spectroscopic techniques together with variable selection algorithms.
•Model performance was stable for a large calibration size.•Independent validation had a large uncertainty of accuracy for a small data set.•One-time random sampling for independent validation is ...appropriate for a large data set.•Cross validation and/or repeated independent validation are suggested for a small data set.
Visible-near infrared (vis–NIR) spectroscopy has been widely used to characterize soil information from field to global scales. Before applying a calibrated spectral predictive model to acquire soil information, either independent validation or k-fold cross validation is used to evaluate model performance. However, there is no consensus on which validation strategy is more suitable and robust when evaluating model performance for the studies in different scales. The objective of this study is to evaluate and compare the model performance of two validation strategies coupling different calibration sizes (a ratio of calibration to validation of 2:1, 4:1 and 9:1) and calibration sampling strategies (random sampling (RS), rank, Kennard-Stone (KS), rank-Kennard-Stone (RKS) and conditioned Latin hypercube sampling (cLHS)) across scales. A total of 17,272 vis–NIR spectra of mineral soils from LUCAS data (continental scale) and their soil organic carbon (SOC) and clay contents were used in this study, and the dataset was further split into national (2761 samples in France) and five regional datasets (110 to 248 samples from five French administrative regions). To eliminate the effect of changing validation set on the model performance, a consistent test set (20% of total samples at each scale) was split to evaluate all the combinations involved in two validation strategies. The Lin’s concordance correlation coefficient (CCC) of the cubist model were stable for both SOC and clay for different calibration sizes, calibration sampling and validation strategies for a large calibration size (>1400) at the national and continental scales. A larger calibration size can potentially improve model performance for a small dataset (<300) at the regional scale, and a wider calibration range would result in better model performance. No silver bullet was found among the different calibration sampling strategies at the regional scale. For five French regions (small data set), we found a high variation (95th percentile minus the 5th percentile) in the CCC among the models built from 50 repeated RS (0.10–0.44 for SOC, 0.16–0.52 for clay) and cLHS (0.08–0.40 for SOC, 0.12–0.36 for clay). This finding indicates that a one-time RS or cLHS for selecting the calibration set has high uncertainty in model evaluation for a small dataset and therefore should be used with caution. Therefore, we suggest the following: (1) for a large data set (thousands), either one-time random sampling for independent validation or k-fold cross validation would be appropriate; (2) for a small data set (dozens to hundreds), k-fold cross validation and/or repeated random sampling for independent validation would be more robust for spectral predictive model evaluation.
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Polycyclic aromatic hydrocarbons (PAHs) and carbon dioxide primarily originate from the combustion of fossil fuels and biomass. The implementation of the Chinese “double carbon ...strategy” is expected to impact the distribution of PAH emissions, consequently influencing the spatial distribution trend of PAHs in surface soil. Therefore, it is crucial to quantitatively evaluate the effectiveness of the Chinese “double carbon strategy” on soil PAH pollution for the purpose of “the reduction of pollution and carbon emissions”. This study utilized 15,088 individual PAH concentration data from 943 soil samples collected between 2003 and 2020 in China, in conjunction with PAH emissions at a 10 km resolution, for meta-analysis. The calculated PAH emissions in this study are in line with the global PAH emission inventory (PKU-PAH-2007), with a relative standard deviation at the provincial level of less than 25 %. Subsequently, a novel method was developed using emission density and Kow of PAHs to predict PAH concentrations in surface soil based on a least-squares regression model. Compared to other environmental models, the method established in this study significantly reduced the percent sample deviation to less than 70 %. Furthermore, energy consumption data for China were simulated based on the implementation plan of the “double carbon strategy” to project PAH emissions and soil PAH levels for the years 2030 and 2060. The predicted PAH emissions in China were estimated to decrease to 41,300 t in 2030 and 10,406.5 t in 2060 from 78,815 t in 2020. Moreover, the heavily contaminated areas of soil PAHs (i.e., total PAH concentrations in soil exceeding 1000 μg kg−1) were projected to decrease by 45 % and 82 % in 2030 and 2060, respectively, compared to levels in 2020. These findings suggest that the implementation of the “double carbon strategy” can fundamentally reduce the pollution of PAHs in surface soil of China.
Diffuse reflectance spectroscopy (DRS), including visible and near-infrared (VNIR) and mid-infrared (MIR) radiation, is a rapid, accurate and cost-effective technique for estimating soil organic ...carbon (SOC). We examined 24 soil cores (0-100 cm) from the Sygera Mountains on the Qinghai-Tibet Plateau, considering field-moist intact VNIR, air-dried ground VNIR and air-dried ground MIR spectra at 5-cm intervals. Preprocessed spectra were used to predict the SOC in the soil cores using partial least squares regression (PLSR) and a support vector machine (SVM). The SVM models performed better with three predictors, with the ratio of performance to inter-quartile distance (RPIQ) and R
values typically exceeding 1.74 and 0.73, respectively. The SVM using the DRS technique indicated accurate predictive results of SOC in each core. The RPIQ values of the shrub meadow, forest and total dataset prediction using air-dried ground VNIR were 1.97, 2.68 and 1.99, respectively; the values using field-moist intact VNIR were 1.95, 2.07 and 1.76 and those using air-dried ground MIR were 1.78, 1.96 and 1.74, respectively. We conclude that the DRS technique is an efficient and rapid method for SOC prediction and has the potential for dynamic monitoring of SOC stock density on the Qinghai-Tibet Plateau.
•Hand-feel soil texture and particle-size distribution are compared using a large database.•The overall accuracy of hand-feel soil texture class allocation was 73%•Most discrepancies were explained ...by very fine and coarse sand content.•Predicting soil water retention at pF2 using hand-feel texture gave satisfactory results.
Due to cost constraints, field texture classes estimated by hand-feel by soil surveyors are more abundant than laboratory measurements of particle-size distribution. Thus, there is a considerable potential to use field-estimated soil textures for mapping on the condition that they are reliable and can be characterized by a probability distribution function similar to values obtained by laboratory measurements. This study aimed to investigate and elucidate the differences between the field texture classes estimated by hand-feel and soil texture determined from particle-size analysis under laboratory conditions in a region of Central France. We tested several hypotheses to explain the discrepancies between field estimates and laboratory measurements (organic C content, pH, more detailed particle-size analyses, and CEC). Finally, we simulated the consequences of using particle-size distribution estimated from field texture on a pedotransfer function (PTF) for water retention. Laboratory measurements of clay, silt, and sand content for each field texture class were available for about 17,400 samples. Considering laboratory measurements and the French texture triangle as the reference, the overall accuracy of field texture class allocation was 73%, which was better than most of the results previously reported in the literature. When looking at each field texture class, most predictions were consistent; however, there were noticeable differences between a few field texture classes and particle-size classes. The extreme texture classes located at the corners of the texture triangle were better predicted than those located at the centre of the triangle. We found the discrepancy of field texture classes can be explained by the very fine sand (50–100 µm) and very coarse sand (1000–2000 µm) contents. Based on the particle-size distribution from each field texture class, we calculated their joint probability distribution function of their corresponding laboratory measurements of clay, silt, and sand content. Results showed that PTF values predicted using hand-feel texture were consistent with those obtained with the measured particle-size distribution. Overall, we demonstrated the value of hand-feel texture in expanding the soil texture database and supporting the expansion of the national database to inform soil water retention properties.
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