This page has only limited features, please log in for full access.

Mr. Bifeng Hu
Unité de Recherche en Science du Sol, INRA, 45075 Orléans, France

Basic Info


Research Keywords & Expertise

0 Geostatistics
0 Machine Learning
0 Soil Science
0 Spatial Analysis
0 Digital Soil Mapping

Fingerprints

Machine Learning
Geostatistics
Soil Science
Digital Soil Mapping

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 22 April 2021 in Geoderma
Reads 0
Downloads 0

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.

ACS Style

Songchao Chen; Hanyi Xu; Dongyun Xu; Wenjun Ji; Shuo Li; Meihua Yang; Bifeng Hu; Yin Zhou; Nan Wang; Dominique Arrouays; Zhou Shi. Evaluating validation strategies on the performance of soil property prediction from regional to continental spectral data. Geoderma 2021, 400, 115159 .

AMA Style

Songchao Chen, Hanyi Xu, Dongyun Xu, Wenjun Ji, Shuo Li, Meihua Yang, Bifeng Hu, Yin Zhou, Nan Wang, Dominique Arrouays, Zhou Shi. Evaluating validation strategies on the performance of soil property prediction from regional to continental spectral data. Geoderma. 2021; 400 ():115159.

Chicago/Turabian Style

Songchao Chen; Hanyi Xu; Dongyun Xu; Wenjun Ji; Shuo Li; Meihua Yang; Bifeng Hu; Yin Zhou; Nan Wang; Dominique Arrouays; Zhou Shi. 2021. "Evaluating validation strategies on the performance of soil property prediction from regional to continental spectral data." Geoderma 400, no. : 115159.

Journal article
Published: 26 February 2021 in Remote Sensing
Reads 0
Downloads 0

As an important parameter to characterize physical and biogeochemical processes, sea surface salinity (SSS) has received extensive attention. Cubist is a data mining model, which can be well-suited to estimate and analyze SSS in the Gulf of Mexico (GOM) because it can reflect the SSS internal heterogeneity in the GOM—overall circular distribution, and the seasonality related to temperature and river discharge changes. Using remote sensing reflectance (Rrs) at 412, 443, 488 (490), 555, and 667 (670) nm and sea surface temperature (SST), a cubist model was developed to estimate SSS with high accuracy with the overall performance demonstrates a root mean square error (RMSE) of 0.27 psu and correlation coefficient of 0.97 of R2. The model divides the GOM area according to model rules into four sub-regions, which include estuary, nearshore, and open sea, reflecting the gradient distribution of SSS. The division of sub-regions and seasonal changes can be explained by the distribution of water bodies, river discharges, and local wind forces since it is obvious that the estuary region reaches the largest low-value area and spreads eastward with the monsoon in the spring when the river flow increases to the highest value. While the east to west wind in the non-summer monsoon period guides the plume westward, and the lowest river discharge in winter corresponds to the smallest low value area. After comparison with other statistical models, the cubist model showed satisfactory results in independent verification of cruise data, proving the estimation capability under different geographical conditions (such as estuaries and open seas) and seasons. Therefore, considering high accuracy and heterogeneity mining, the cubist-based model is an ideal method for coastal SSS estimation and spatial-temporal heterogeneity analysis, and can provide ideas for model construction for coastal areas with similar geographic environments.

ACS Style

Zhiyi Fu; Fangfang Wu; Zhengliang Zhang; Linshu Hu; Feng Zhang; Bifeng Hu; Zhenhong Du; Zhou Shi; Renyi Liu. Sea Surface Salinity Estimation and Spatial-Temporal Heterogeneity Analysis in the Gulf of Mexico. Remote Sensing 2021, 13, 881 .

AMA Style

Zhiyi Fu, Fangfang Wu, Zhengliang Zhang, Linshu Hu, Feng Zhang, Bifeng Hu, Zhenhong Du, Zhou Shi, Renyi Liu. Sea Surface Salinity Estimation and Spatial-Temporal Heterogeneity Analysis in the Gulf of Mexico. Remote Sensing. 2021; 13 (5):881.

Chicago/Turabian Style

Zhiyi Fu; Fangfang Wu; Zhengliang Zhang; Linshu Hu; Feng Zhang; Bifeng Hu; Zhenhong Du; Zhou Shi; Renyi Liu. 2021. "Sea Surface Salinity Estimation and Spatial-Temporal Heterogeneity Analysis in the Gulf of Mexico." Remote Sensing 13, no. 5: 881.

Journal article
Published: 25 January 2021 in International Journal of Environmental Research and Public Health
Reads 0
Downloads 0

Potentially toxic elements (PTEs) pollution in the agricultural soil of China, especially in developed regions such as the Yangtze River Delta (YRD) in eastern China, has received increasing attention. However, there are few studies on the long-term assessment of soil pollution by PTEs over large regions. Therefore, in this study, a meta-analysis was conducted to evaluate the current state and temporal trend of PTEs pollution in the agricultural land of the Yangtze River Delta. Based on a review of 118 studies published between 1993 and 2020, the average concentrations of Cd, Hg, As, Pb, Cr, Cu, Zn, and Ni were found to be 0.25 mg kg−1, 0.14 mg kg−1, 8.14 mg kg−1, 32.32 mg kg−1, 68.84 mg kg−1, 32.58 mg kg−1, 92.35 mg kg−1, and 29.30 mg kg−1, respectively. Among these elements, only Cd and Hg showed significant accumulation compared with their background values. The eastern Yangtze River Delta showed a relatively high ecological risk due to intensive industrial activities. The contents of Cd, Pb, and Zn in soil showed an increasing trend from 1993 to 2000 and then showed a decreasing trend. The results obtained from this study will provide guidance for the prevention and control of soil pollution in the Yangtze River Delta.

ACS Style

Shufeng She; Bifeng Hu; Xianglin Zhang; Shuai Shao; Yefeng Jiang; Lianqing Zhou; Zhou Shi. Current Status and Temporal Trend of Potentially Toxic Elements Pollution in Agricultural Soil in the Yangtze River Delta Region: A Meta-Analysis. International Journal of Environmental Research and Public Health 2021, 18, 1033 .

AMA Style

Shufeng She, Bifeng Hu, Xianglin Zhang, Shuai Shao, Yefeng Jiang, Lianqing Zhou, Zhou Shi. Current Status and Temporal Trend of Potentially Toxic Elements Pollution in Agricultural Soil in the Yangtze River Delta Region: A Meta-Analysis. International Journal of Environmental Research and Public Health. 2021; 18 (3):1033.

Chicago/Turabian Style

Shufeng She; Bifeng Hu; Xianglin Zhang; Shuai Shao; Yefeng Jiang; Lianqing Zhou; Zhou Shi. 2021. "Current Status and Temporal Trend of Potentially Toxic Elements Pollution in Agricultural Soil in the Yangtze River Delta Region: A Meta-Analysis." International Journal of Environmental Research and Public Health 18, no. 3: 1033.

Journal article
Published: 10 December 2020 in Remote Sensing
Reads 0
Downloads 0

Information on spatial, temporal, and depth variability of soil salinity at field and landscape scales is important for a variety of agronomic and environment concerns including irrigation in arid and semi-arid areas. However, challenges remain in characterizing and monitoring soil secondary salinity as it can largely be impacted by managements including irrigation and mulching in addition to natural factors. The objective of this study is to evaluate apparent electrical conductivity (ECa)-directed soil sampling as a basis for monitoring management-induced spatio-temporal change in soil salinity in three dimensions. A field experiment was conducted on an 18-ha saline-sodic field from Alar’s Agricultural Science and Technology Park, China between March, and November 2018. Soil ECa was measured using an electromagnetic induction (EMI) sensor for four times over the growing season and soil core samples were collected from 18 locations (each time) selected using EMI survey data as a-priori information. A multi-variate regression-based predictive relationship between ECa and laboratory-measured electrical conductivity (ECe) was used to predict EC with confidence (R2 between 0.82 and 0.99). A three-dimensional inverse distance weighing (3D-IDW) interpolation clearly showed a strong variability in space and time and with depths within the study field which were mainly attributed to the human management factors including irrigation, mulching, and uncovering of soils and natural factors including air temperature, evaporation, and groundwater level. This study lays a foundation of characterizing secondary salinity at a field scale for precision and sustainable management of agricultural lands in arid and semi-arid areas.

ACS Style

Hongyi Li; Xinlu Liu; Bifeng Hu; Asim Biswas; Qingsong Jiang; Weiyang Liu; Nan Wang; Jie Peng. Field-Scale Characterization of Spatio-Temporal Variability of Soil Salinity in Three Dimensions. Remote Sensing 2020, 12, 4043 .

AMA Style

Hongyi Li, Xinlu Liu, Bifeng Hu, Asim Biswas, Qingsong Jiang, Weiyang Liu, Nan Wang, Jie Peng. Field-Scale Characterization of Spatio-Temporal Variability of Soil Salinity in Three Dimensions. Remote Sensing. 2020; 12 (24):4043.

Chicago/Turabian Style

Hongyi Li; Xinlu Liu; Bifeng Hu; Asim Biswas; Qingsong Jiang; Weiyang Liu; Nan Wang; Jie Peng. 2020. "Field-Scale Characterization of Spatio-Temporal Variability of Soil Salinity in Three Dimensions." Remote Sensing 12, no. 24: 4043.

Journal article
Published: 17 November 2020 in Remote Sensing
Reads 0
Downloads 0

Soil pollution by potentially toxic elements (PTEs) has become a core issue around the world. Knowledge of the spatial distribution of PTEs in soil is crucial for soil remediation. Portable X-ray fluorescence spectroscopy (p-XRF) provides a cost-saving alternative to the traditional laboratory analysis of soil PTEs. In this study, we collected 293 soil samples from Fuyang County in Southeast China. Subsequently, we used several geostatistical methods, such as inverse distance weighting (IDW), ordinary kriging (OK), and empirical Bayesian kriging (EBK), to estimate the spatial variability of soil PTEs measured by the laboratory and p-XRF methods. The final maps of soil PTEs were outputted by the model averaging method, which combines multiple maps previously created by IDW, OK, and EBK, using both lab and p-XRF data. The study results revealed that the mean PTE content measured by the laboratory methods was as follows: Zn (127.43 mg kg−1) > Cu (31.34 mg kg−1) > Ni (20.79 mg kg−1) > As (10.65 mg kg−1) > Cd (0.33 mg kg−1). p-XRF measurements showed a spatial prediction accuracy of soil PTEs similar to that of laboratory analysis measurements. The spatial prediction accuracy of different PTEs outputted by the model averaging method was as follows: Zn (R2 = 0.71) > Cd (R2 = 0.68) > Ni (R2 = 0.67) > Cu (R2 = 0.62) > As (R2 = 0.50). The prediction accuracy of the model averaging method for five PTEs studied herein was improved compared with that of the laboratory and p-XRF methods, which utilized individual geostatistical methods (e.g., IDW, OK, EBK). Our results proved that p-XRF was a reliable alternative to the traditional laboratory analysis methods for mapping soil PTEs. The model averaging approach improved the prediction accuracy of the soil PTE spatial distribution and reduced the time and cost of monitoring and mapping PTE soil contamination.

ACS Style

Fang Xia; Bifeng Hu; Youwei Zhu; Wenjun Ji; Songchao Chen; Dongyun Xu; Zhou Shi. Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods. Remote Sensing 2020, 12, 3775 .

AMA Style

Fang Xia, Bifeng Hu, Youwei Zhu, Wenjun Ji, Songchao Chen, Dongyun Xu, Zhou Shi. Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods. Remote Sensing. 2020; 12 (22):3775.

Chicago/Turabian Style

Fang Xia; Bifeng Hu; Youwei Zhu; Wenjun Ji; Songchao Chen; Dongyun Xu; Zhou Shi. 2020. "Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods." Remote Sensing 12, no. 22: 3775.

Journal article
Published: 15 August 2020 in Journal of Cleaner Production
Reads 0
Downloads 0

Modelling the mass balance and forecasting the temporal variations of cadmium (Cd) in farmland soil play a critical role in the development of mitigation strategies for Cd pollution. In this study, a novel framework integrating the mass balance model with model-independent parameter estimation, geostatistics, and bagging algorithms were integrated to simulate the long-term changes in the Cd content of farmland soil in Zhejiang Province, China. The predicted Cd content in farmland soil in 2013 was compared to observed data (R value = 0.568 and root-mean-square error = 0.177 mg kg−1), demonstrating the feasibility of our model. The prediction results for 2050 indicated that the average concentration of Cd in farmland soil from Zhejiang Province will increase to 0.30 mg kg−1 if the current trend continues, and that 37.4% of the farmland soil in the province will be classified as a “security utilisation region”, indicating great risk of soil Cd contamination in these areas. Reducing industrial emissions and soil acidification to reduce the Cd pollution risk should receive great attention. This study provides a new perspective for forecasting the temporal trends of Cd accumulation in farmland soil and facilitates improved management and risk prevention of Cd pollution in agricultural soils and products.

ACS Style

Tingting Fu; Ruiying Zhao; Bifeng Hu; Xiaolin Jia; Zhige Wang; Lianqing Zhou; Mingxiang Huang; Yan Li; Zhou Shi. Novel framework for modelling the cadmium balance and accumulation in farmland soil in Zhejiang Province, East China: Sensitivity analysis, parameter optimisation, and forecast for 2050. Journal of Cleaner Production 2020, 279, 123674 .

AMA Style

Tingting Fu, Ruiying Zhao, Bifeng Hu, Xiaolin Jia, Zhige Wang, Lianqing Zhou, Mingxiang Huang, Yan Li, Zhou Shi. Novel framework for modelling the cadmium balance and accumulation in farmland soil in Zhejiang Province, East China: Sensitivity analysis, parameter optimisation, and forecast for 2050. Journal of Cleaner Production. 2020; 279 ():123674.

Chicago/Turabian Style

Tingting Fu; Ruiying Zhao; Bifeng Hu; Xiaolin Jia; Zhige Wang; Lianqing Zhou; Mingxiang Huang; Yan Li; Zhou Shi. 2020. "Novel framework for modelling the cadmium balance and accumulation in farmland soil in Zhejiang Province, East China: Sensitivity analysis, parameter optimisation, and forecast for 2050." Journal of Cleaner Production 279, no. : 123674.

Journal article
Published: 19 July 2020 in Science of The Total Environment
Reads 0
Downloads 0

Research on the carbon cycle of coastal marine systems has been of wide concern recently. Accurate knowledge of the temporal and spatial distributions of sea-surface partial pressure (pCO2) can reflect the seasonal and spatial heterogeneity of CO2 flux and is, therefore, essential for quantifying the ocean's role in carbon cycling. However, it is difficult to use one model to estimate pCO2 and determine its controlling variables for an entire region due to the prominent spatiotemporal heterogeneity of pCO2 in coastal areas. Cubist is a commonly-used model for zoning; thus, it can be applied to the estimation and regional analysis of pCO2 in the Gulf of Mexico (GOM). A cubist model integrated with satellite images was used here to estimate pCO2 in the GOM, a river-dominated coastal area, using satellite products, including chlorophyll-a concentration (Chl-a), sea-surface temperature (SST) and salinity (SSS), and the diffuse attenuation coefficient at 490 nm (Kd-490). The model was based on a semi-mechanistic model and integrated the high-accuracy advantages of machine learning methods. The overall performance showed a root mean square error (RMSE) of 8.42 μatm with a coefficient of determination (R2) of 0.87. Based on the heterogeneity of environmental factors, the GOM area was divided into 6 sub-regions, consisting estuaries, near-shores, and open seas, reflecting a gradient distribution of pCO2. Factor importance and correlation analyses showed that salinity, chlorophyll-a, and temperature are the main controlling environmental variables of pCO2, corresponding to both biological and physical effects. Seasonal changes in the GOM region were also analyzed and explained by changes in the environmental variables. Therefore, considering both high accuracy and interpretability, the cubist-based model was an ideal method for pCO2 estimation and spatiotemporal heterogeneity analysis.

ACS Style

Zhiyi Fu; Linshu Hu; ZhenDe Chen; Feng Zhang; Zhou Shi; Bifeng Hu; Zhenhong Du; Renyi Liu. Estimating spatial and temporal variation in ocean surface pCO2 in the Gulf of Mexico using remote sensing and machine learning techniques. Science of The Total Environment 2020, 745, 140965 .

AMA Style

Zhiyi Fu, Linshu Hu, ZhenDe Chen, Feng Zhang, Zhou Shi, Bifeng Hu, Zhenhong Du, Renyi Liu. Estimating spatial and temporal variation in ocean surface pCO2 in the Gulf of Mexico using remote sensing and machine learning techniques. Science of The Total Environment. 2020; 745 ():140965.

Chicago/Turabian Style

Zhiyi Fu; Linshu Hu; ZhenDe Chen; Feng Zhang; Zhou Shi; Bifeng Hu; Zhenhong Du; Renyi Liu. 2020. "Estimating spatial and temporal variation in ocean surface pCO2 in the Gulf of Mexico using remote sensing and machine learning techniques." Science of The Total Environment 745, no. : 140965.

Journal article
Published: 25 April 2020 in Environmental Pollution
Reads 0
Downloads 0

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.

ACS Style

Yefeng Jiang; Songchao Chen; Bifeng Hu; Yin Zhou; ZongZheng Liang; Xiaolin Jia; Mingxiang Huang; Jing Wei; Zhou Shi. A comprehensive framework for assessing the impact of potential agricultural pollution on grain security and human health in economically developed areas. Environmental Pollution 2020, 263, 114653 .

AMA Style

Yefeng Jiang, Songchao Chen, Bifeng Hu, Yin Zhou, ZongZheng Liang, Xiaolin Jia, Mingxiang Huang, Jing Wei, Zhou Shi. A comprehensive framework for assessing the impact of potential agricultural pollution on grain security and human health in economically developed areas. Environmental Pollution. 2020; 263 ():114653.

Chicago/Turabian Style

Yefeng Jiang; Songchao Chen; Bifeng Hu; Yin Zhou; ZongZheng Liang; Xiaolin Jia; Mingxiang Huang; Jing Wei; Zhou Shi. 2020. "A comprehensive framework for assessing the impact of potential agricultural pollution on grain security and human health in economically developed areas." Environmental Pollution 263, no. : 114653.

Preprint content
Published: 23 March 2020
Reads 0
Downloads 0

Source identification and apportionment of heavy metals (HMs) has been a vital issue of soil contamination restoration. In this study, qualitive approaches (Finite mixture distribution model (FMDM) and raster based principal components analysis (RB-PCA)) as well as quantitative approach (positive matrix factorization (PMF)) were composed to identify and apportion sources of five HMs (Cd, Hg, As, Pb, Cr) with the help of several crucial auxiliary variables in Wenzhou City, China. The result of FMDM showed Cd, and Pb fitted for single log-normal distribution, while Hg fitted for double log-normal mixed distribution, and As, Cr presented triple log-normal distribution. Each element was identified and separated from natural or anthropogenic sources. An improved score interpolation map of PCA attached with corresponded auxiliary variables analysis suggested three main contribution sources including parental materials, mines exploiting and industrial emissions contributes most in the whole study area. Each element was further discussed for quantitative contributions through PMF model. Parental materials contributed to all elements (Cd, Hg, As, Pb, Cr) as 89.22%, 84.81%, 7.31%, 35.84%, 27.42%. Industrial emissions had a contribution as 2.94%, 80.77%, 15.93%, 4.79%, 25.63% for each element respectively. While Mine exploiting mixed with fertilizers inputs has dedicated for such five HMs as 7.84%,11.92%, 48.23%, 10.40% and 46.95%. Such results could efficiently be devoted to scientific decisions and strategies making regarding HMs pollution regulation in soils.

ACS Style

Shuai Shao; Bifeng Hu; Yin Zhou; Zhou Shi. Comprehensive Source Identification and Apportionment Analysis of five Heavy Metals in Soils in Wenzhou City, China. 2020, 1 .

AMA Style

Shuai Shao, Bifeng Hu, Yin Zhou, Zhou Shi. Comprehensive Source Identification and Apportionment Analysis of five Heavy Metals in Soils in Wenzhou City, China. . 2020; ():1.

Chicago/Turabian Style

Shuai Shao; Bifeng Hu; Yin Zhou; Zhou Shi. 2020. "Comprehensive Source Identification and Apportionment Analysis of five Heavy Metals in Soils in Wenzhou City, China." , no. : 1.

Journal article
Published: 02 March 2020 in Environmental Pollution
Reads 0
Downloads 0

The prediction and identification of the factors controlling heavy metal transfer in soil-crop ecosystems are of critical importance. In this study, random forest (RF), gradient boosted machine (GBM), and generalised linear (GLM) models were compared after being used to model and identify prior factors that affect the transfer of heavy metals (HMs) in soil-crop systems in the Yangtze River Delta, China, based on 13 covariates with 1822 pairs of soil-crop samples. The mean bioaccumulation factors (BAFs) for all crops followed the order Cd > Zn > As > Cu > Ni > Hg > Cr > Pb. The RF model showed the best prediction ability for the BAFs of HMs in soil-crop ecosystems, followed by GBM and GLM. The R2 values of the RF models for the BAFs of Zn, Cu, Cr, Ni, Hg, Cd, As, and Pb were 0.84, 0.66, 0.59, 0.58, 0.58, 0.51, 0.30, and 0.17, respectively. The primary controlling factor in soil-to-crop transfer of all HMs under study was plant type, followed by soil heavy metal content and soil organic materials. The model used herein could be used to assist the prediction of heavy metal contents in crops based on heavy metal contents in soil and other covariates, and can significantly reduce the cost, labour, and time requirements involved with laboratory analysis. It can also be used to quantify the importance of variables and identify potential control factors in heavy metal bioaccumulation in soil-crop ecosystems.

ACS Style

Bifeng Hu; Jie Xue; Yin Zhou; Shuai Shao; Zhiyi Fu; Yan Li; Songchao Chen; Lin Qi; Zhou Shi. Modelling bioaccumulation of heavy metals in soil-crop ecosystems and identifying its controlling factors using machine learning. Environmental Pollution 2020, 262, 114308 .

AMA Style

Bifeng Hu, Jie Xue, Yin Zhou, Shuai Shao, Zhiyi Fu, Yan Li, Songchao Chen, Lin Qi, Zhou Shi. Modelling bioaccumulation of heavy metals in soil-crop ecosystems and identifying its controlling factors using machine learning. Environmental Pollution. 2020; 262 ():114308.

Chicago/Turabian Style

Bifeng Hu; Jie Xue; Yin Zhou; Shuai Shao; Zhiyi Fu; Yan Li; Songchao Chen; Lin Qi; Zhou Shi. 2020. "Modelling bioaccumulation of heavy metals in soil-crop ecosystems and identifying its controlling factors using machine learning." Environmental Pollution 262, no. : 114308.

Journal article
Published: 28 February 2020 in Journal of Hazardous Materials
Reads 0
Downloads 0

From the perspective of the mechanism of soil pollution, it is difficult to explain the process of predicting the spatial distributions of soil heavy metal pollution using traditional geostatistical methods at a regional scale. Furthermore, few methods are available to proactively identify potential risk areas for preventing soil contamination. In this study, we selected 13 environmental factors related to the accumulation of soil heavy metals based on the source-sink theory. Then, the fuzzy k-means method in combination with the random forest (RF) method was used to classify potential risk areas. The concentrations and spatial distributions of the heavy metals were well predicted by RF, and the average values of the root mean square error of the prediction and R2 were 4.84 mg kg−1 and 0.57, respectively. The results indicated that the soil pH, fine particulate matter, and proximity to polluting enterprises significantly influenced the heavy metal pollution in soils, and the environmental variables varied significantly across the identified subregions. This study provides a theoretical basis for the sustainable management and control of soil pollution at the regional scale.

ACS Style

Xiaolin Jia; Tingting Fu; Bifeng Hu; Zhou Shi; Lianqing Zhou; Youwei Zhu. Identification of the potential risk areas for soil heavy metal pollution based on the source-sink theory. Journal of Hazardous Materials 2020, 393, 122424 .

AMA Style

Xiaolin Jia, Tingting Fu, Bifeng Hu, Zhou Shi, Lianqing Zhou, Youwei Zhu. Identification of the potential risk areas for soil heavy metal pollution based on the source-sink theory. Journal of Hazardous Materials. 2020; 393 ():122424.

Chicago/Turabian Style

Xiaolin Jia; Tingting Fu; Bifeng Hu; Zhou Shi; Lianqing Zhou; Youwei Zhu. 2020. "Identification of the potential risk areas for soil heavy metal pollution based on the source-sink theory." Journal of Hazardous Materials 393, no. : 122424.

Data descriptor
Published: 03 January 2020 in Scientific Data
Reads 0
Downloads 0

Depth to bedrock influences or controls many of the Earth’s physical and chemical processes. It plays important roles in soil science, geology, hydrology, land surface processes, civil engineering, and other related fields. However, information about depth to bedrock in China is very deficient, and there is no independent map of depth to bedrock in China currently. This paper describes the materials and methods to produce high-resolution (100 m) depth-to-bedrock maps of China. For different research and application needs, two sets of data are provided for users. One is the prediction by the ensemble of the random forests and gradient boosting tree models, and the other is the prediction and the uncertainty of prediction based on quantile regression forests model. In comparison with depth-to-bedrock maps of China extracted from previous global predictions, our predictions showed higher accuracy and more spatial details. These data sets can provide more accurate information for Earth system research compared with previous depth-to-bedrock maps.

ACS Style

Fapeng Yan; Wei Shangguan; Jing Zhang; Bifeng Hu. Depth-to-bedrock map of China at a spatial resolution of 100 meters. Scientific Data 2020, 7, 1 -13.

AMA Style

Fapeng Yan, Wei Shangguan, Jing Zhang, Bifeng Hu. Depth-to-bedrock map of China at a spatial resolution of 100 meters. Scientific Data. 2020; 7 (1):1-13.

Chicago/Turabian Style

Fapeng Yan; Wei Shangguan; Jing Zhang; Bifeng Hu. 2020. "Depth-to-bedrock map of China at a spatial resolution of 100 meters." Scientific Data 7, no. 1: 1-13.

Journal article
Published: 09 December 2019 in Journal of Geochemical Exploration
Reads 0
Downloads 0

Accumulation of potentially toxic elements (PTEs) in farmland soils and agricultural products is an issue of considerable concern related to food safety and human health. Assessment of composite health risks caused by exposure to PTEs through different pathways and the spatial probability of these risks could help in soil management and reduction in the corresponding human health risk. In this study, we collected 932 soil samples and corresponding samples of rice planted at the same locations in an industrial city in eastern China. The composite human health risk, including the health risks caused by Cr, Pb, Cd, Hg, As, Cu, Zn, and Ni from soil inhalation, ingestion, and dermal contact combined with the consumption of rice were assessed. Sequential Gaussian stochastic simulation and probability kriging were employed to explore the spatial pattern of the composite health risk. The results showed that 13.52%, 5.47%, 2.68%, 2.58%, 1.61%, 0.86%, and 0.21% of soil samples collected from the study area were polluted by Hg, Cd, Pb, Cu, Zn, Ni, and Cr, respectively. Furthermore, 65.02%, 20.28%, 10.94%, 4.72%, 0.75%, 0.11%, and 0.11% of rice samples were polluted by high levels of Ni, Cr, As, Cd, Pb, Hg, and Zn, respectively. Children had a higher hazard index than adults for non-carcinogenic health risks. Both children and adults had potential carcinogenic risks. The largest contributor to non-carcinogenic health risks was As, whereas Ni was the largest contributor to carcinogenic risk. Consumption of contaminated rice accounted for >90% of the total non-carcinogenic and carcinogenic health risks, suggesting that PTEs accumulation in rice could exert harmful effects on human health. In terms of their spatial patterns, both non-carcinogenic and carcinogenic risks were associated with areas with a high density of anthropogenic activities. Residents in most areas in the study region have a high probability of experiencing significant carcinogenic and non-carcinogenic health effects caused by exposure to PTEs. Children have a higher probability of non-carcinogenic health risk than adults across the study area. The results revealed that consumption of contaminated crops poses essential potential health risks to humans. Measures should be undertaken to reduce the content of contaminated PTEs in farmland soils and rice, and children should be listed as a priority for protection from exposure to PTEs.

ACS Style

Bifeng Hu; Shuai Shao; Tingting Fu; Zhiyi Fu; Yin Zhou; Yan Li; Lin Qi; Songchao Chen; Zhou Shi. Composite assessment of human health risk from potentially toxic elements through multiple exposure routes: A case study in farmland in an important industrial city in East China. Journal of Geochemical Exploration 2019, 210, 106443 .

AMA Style

Bifeng Hu, Shuai Shao, Tingting Fu, Zhiyi Fu, Yin Zhou, Yan Li, Lin Qi, Songchao Chen, Zhou Shi. Composite assessment of human health risk from potentially toxic elements through multiple exposure routes: A case study in farmland in an important industrial city in East China. Journal of Geochemical Exploration. 2019; 210 ():106443.

Chicago/Turabian Style

Bifeng Hu; Shuai Shao; Tingting Fu; Zhiyi Fu; Yin Zhou; Yan Li; Lin Qi; Songchao Chen; Zhou Shi. 2019. "Composite assessment of human health risk from potentially toxic elements through multiple exposure routes: A case study in farmland in an important industrial city in East China." Journal of Geochemical Exploration 210, no. : 106443.

Research article
Published: 15 November 2019 in Land Degradation & Development
Reads 0
Downloads 0

Soil quality in alpine ecosystems requires regular monitoring to assess its dynamics under changes in climate and land use. Visible near‐infrared (vis‐NIR) spectroscopy could offer an option, as sampling and transporting large numbers of soil samples in the Qinghai‐Tibet Plateau is extremely difficult. However, the potential for in situ vis‐NIR spectra and the optimal algorithms need to be defined in this region. We have therefore evaluated the performance of a deep learning method, multilayer perceptron (MLP), for in situ spectral measurement of soil organic carbon (SOC) with in situ vis‐NIR spectroscopy in Southeastern Tibet, China. A total of 39 soil cores (maximum depth 1 m), including 547 soil samples taken from each 5‐cm depth interval, were collected. The spectra were also measured at each 5‐cm depth interval accordingly. After spectral preprocessing, 4096 MLP models were generated by taking all the combinations from six parameters defined in the MLP. The 10‐fold‐core cross‐validation showed that MLP had a good performance for in situ SOC prediction, and the best MLP model had an R2 of 0.92, which were much better than those of the partial least squares regression (PLSR) model (R2 = 0.80). The results also suggested that the number of epochs, number of neurons, and dropout rate were the most important parameters in the MLP model. We concluded that in situ vis‐NIR spectroscopy coupled with an MLP model has high potential for large‐scale SOC monitoring in the Qinghai‐Tibet Plateau. Our results also provide a reference for rapid hyperparameter optimization using MLP for future soil spectroscopic modeling.

ACS Style

Songchao Chen; Dongyun Xu; Shuo Li; Wenjun Ji; Meihua Yang; Yin Zhou; Bifeng Hu; Hanyi Xu; Zhou Shi. Monitoring soil organic carbon in alpine soils using in situ vis‐NIR spectroscopy and a multilayer perceptron. Land Degradation & Development 2019, 31, 1026 -1038.

AMA Style

Songchao Chen, Dongyun Xu, Shuo Li, Wenjun Ji, Meihua Yang, Yin Zhou, Bifeng Hu, Hanyi Xu, Zhou Shi. Monitoring soil organic carbon in alpine soils using in situ vis‐NIR spectroscopy and a multilayer perceptron. Land Degradation & Development. 2019; 31 (8):1026-1038.

Chicago/Turabian Style

Songchao Chen; Dongyun Xu; Shuo Li; Wenjun Ji; Meihua Yang; Yin Zhou; Bifeng Hu; Hanyi Xu; Zhou Shi. 2019. "Monitoring soil organic carbon in alpine soils using in situ vis‐NIR spectroscopy and a multilayer perceptron." Land Degradation & Development 31, no. 8: 1026-1038.

Journal article
Published: 28 July 2019 in International Journal of Environmental Research and Public Health
Reads 0
Downloads 0

To verify the feasibility of portable X-ray fluorescence (PXRF) for rapidly analyzing, assessing and improving soil heavy metals mapping, 351 samples were collected from Fuyang District, Hangzhou City, in eastern China. Ordinary kriging (OK) and co-ordinary kriging (COK) combined with PXRF measurements were used to explore spatial patterns of heavy metals content in the soil. The Getis-Ord index was calculated to discern hot spots of heavy metals. Finally, multi-variable indicator kriging was conducted to obtain a map of multi-heavy metals pollution. The results indicated Cd is the primary pollution element in Fuyang, followed by As and Pb. Application of PXRF measurements as covariates in COK improved model accuracy, especially for Pb and Cd. Heavy metals pollution hot spots were mainly detected in northern Fuyang and plains along the Fuchun River in southern Fuyang because of mining, industrial and traffic activities, and irrigation with polluted water. Area with high risk of multi-heavy metals pollution mainly distributed in plain along the Fuchun River and the eastern Fuyang. These findings certified the feasibility of using PXRF as an efficient and reliable method for soil heavy metals pollution assessment and mapping, which could contribute to reduce the cost of surveys and pollution remediation.

ACS Style

Fang Xia; Bifeng Hu; Shuai Shao; Dongyun Xu; Zhou; Mingxiang Huang; Yan Li; Songchao Chen; Zhou Shi; Yue Zhou; Yin Zhou; Xia; Hu; Shao; Xu; Li; Chen; Shi. Improvement of Spatial Modeling of Cr, Pb, Cd, As and Ni in Soil Based on Portable X-ray Fluorescence (PXRF) and Geostatistics: A Case Study in East China. International Journal of Environmental Research and Public Health 2019, 16, 2694 .

AMA Style

Fang Xia, Bifeng Hu, Shuai Shao, Dongyun Xu, Zhou, Mingxiang Huang, Yan Li, Songchao Chen, Zhou Shi, Yue Zhou, Yin Zhou, Xia, Hu, Shao, Xu, Li, Chen, Shi. Improvement of Spatial Modeling of Cr, Pb, Cd, As and Ni in Soil Based on Portable X-ray Fluorescence (PXRF) and Geostatistics: A Case Study in East China. International Journal of Environmental Research and Public Health. 2019; 16 (15):2694.

Chicago/Turabian Style

Fang Xia; Bifeng Hu; Shuai Shao; Dongyun Xu; Zhou; Mingxiang Huang; Yan Li; Songchao Chen; Zhou Shi; Yue Zhou; Yin Zhou; Xia; Hu; Shao; Xu; Li; Chen; Shi. 2019. "Improvement of Spatial Modeling of Cr, Pb, Cd, As and Ni in Soil Based on Portable X-ray Fluorescence (PXRF) and Geostatistics: A Case Study in East China." International Journal of Environmental Research and Public Health 16, no. 15: 2694.

Journal article
Published: 12 April 2019 in Environmental Pollution
Reads 0
Downloads 0

It is a great challenge to identify the many and varied sources of soil heavy metal pollution. Often little information is available regarding the anthropogenic factors and enterprises that could potentially pollute soils. In this study we use freely available geographical data from a search engine in conjunction with machine learning methodologies to identify and classify potentially polluting enterprises in the Yangtze Delta, China. The data were classified into 31 separate and five integrated industry types by five different machine learning approaches. Multinomial naive Bayesian (NB) methods achieved an accuracy of 87% and Kappa coefficient of 0.82 and were used to classify the geographic data from more than 250,000 enterprises. The relationship between the different industry classes and measurements of soil cadmium (Cd) and mercury (Hg) concentrations was explored using bivariate local Moran's I analysis. The analysis revealed areas where different industry classes had led to soil pollution. In the case of Cd, elevated concentrations also occurred in some areas because of excessive fertilization and coal mining. This study provides a new approach to investigate the interaction between anthropogenic pollution and natural sources of soil heavy metals to inform pollution control and planning decisions regarding the location of industrial sites.

ACS Style

Xiaolin Jia; Bifeng Hu; Ben P. Marchant; Lianqing Zhou; Zhou Shi; Youwei Zhu. A methodological framework for identifying potential sources of soil heavy metal pollution based on machine learning: A case study in the Yangtze Delta, China. Environmental Pollution 2019, 250, 601 -609.

AMA Style

Xiaolin Jia, Bifeng Hu, Ben P. Marchant, Lianqing Zhou, Zhou Shi, Youwei Zhu. A methodological framework for identifying potential sources of soil heavy metal pollution based on machine learning: A case study in the Yangtze Delta, China. Environmental Pollution. 2019; 250 ():601-609.

Chicago/Turabian Style

Xiaolin Jia; Bifeng Hu; Ben P. Marchant; Lianqing Zhou; Zhou Shi; Youwei Zhu. 2019. "A methodological framework for identifying potential sources of soil heavy metal pollution based on machine learning: A case study in the Yangtze Delta, China." Environmental Pollution 250, no. : 601-609.

Journal article
Published: 23 March 2019 in Geoderma
Reads 0
Downloads 0

Soil thickness (ST) is a crucial factor in earth surface modelling and soil storage capacity calculations (e.g., available water capacity and carbon stocks). However, the observed depths recorded in soil information systems for some profiles are often less than the actual ST (i.e., right censored data). The use of such data will negatively affect model and map accuracy, yet few studies have been done to resolve this issue or propose methods to correct for right censored data. Therefore, this work demonstrates how right censored data can be accounted for in the ST modelling of mainland France. We propose the use of Random Survival Forest (RSF) for ST probability mapping within a Digital Soil Mapping framework. Among 2109 sites of the French Soil Monitoring Network, 1089 observed STs were defined as being right censored. Using RSF, the probability of exceeding a given depth was modelled using freely available spatial data representing the main soil-forming factors. Subsequently, the models were extrapolated to the full spatial extent of mainland France. As examples, we produced maps showing the probability of exceeding the thickness of each GlobalSoilMap standard depth: 5, 15, 30, 60, 100, and 200 cm. In addition, a bootstrapping approach was used to assess the 90% confidence intervals. Our results showed that RSF was able to correct for right censored data entries occurring within a given dataset. RSF was more reliable for thin (0.3 m) and thick soils (1 to 2 m), as they performed better (overall accuracy from 0.793 to 0.989) than soils with a thickness between 0.3 and 1 m. This study provides a new approach for modelling right censored soil information. Moreover, RSF can produce probability maps at any depth less than the maximum depth of the calibration data, which is of great value for designing additional sampling campaigns and decision making in geotechnical engineering.

ACS Style

Songchao Chen; Vera Leatitia Mulder; Manuel P. Martin; Christian Walter; Marine Lacoste; Anne C. Richer-De-Forges; Nicolas P.A. Saby; Thomas Loiseau; Bifeng Hu; Dominique Arrouays. Probability mapping of soil thickness by random survival forest at a national scale. Geoderma 2019, 344, 184 -194.

AMA Style

Songchao Chen, Vera Leatitia Mulder, Manuel P. Martin, Christian Walter, Marine Lacoste, Anne C. Richer-De-Forges, Nicolas P.A. Saby, Thomas Loiseau, Bifeng Hu, Dominique Arrouays. Probability mapping of soil thickness by random survival forest at a national scale. Geoderma. 2019; 344 ():184-194.

Chicago/Turabian Style

Songchao Chen; Vera Leatitia Mulder; Manuel P. Martin; Christian Walter; Marine Lacoste; Anne C. Richer-De-Forges; Nicolas P.A. Saby; Thomas Loiseau; Bifeng Hu; Dominique Arrouays. 2019. "Probability mapping of soil thickness by random survival forest at a national scale." Geoderma 344, no. : 184-194.

Journal article
Published: 11 December 2018 in Science of The Total Environment
Reads 0
Downloads 0

The soil-rice system in China is subjected to increasing concentrations of heavy metals (HMs) which derived from various sources. It is very critical to investigate the concentrations, spatial characteristics and hot spots of HMs content in the soil-rice system. This study presents work completed on 915 soil-rice sample pairs collected from South of Yangtze River Delta, China. These samples were evaluated for HM concentrations. Ordinary Kriging and the Getis-Ord index were used to explore spatial distributions and pollution hot spots. Averaged HMs content in soil is shown to be Zn > Cr > Pb > Cu > Ni > As > Hg > Cd, and concentrations in rice arrange as Zn > Cu > Cr > Ni > As > Cd > Pb > Hg. Compared with Chinese maximum permissible limits, mean content of all HMs in farmland soil are at safe levels and averaged content of all HMs in rice were also at safe levels except As and Ni. Ni was most polluted HM in soil Most of and showed relatively high content in farmland soil in southeastern part. As and Ni are the most polluted in rice, with highest content distributed in the northwestern and southern area, respectively. The majority of HMs pollution hot spots in soil clustered in the central area. Pollution hot spots of Ni and As in rice are mainly concentrated in the central part and southeastern part, correspondingly. Our results found a weak link between content and spatial pattern of pollution status of HMs in soil and rice. The results are anticipated to contribute to more efficient and accurate control of HMs pollution in soil-rice system, and assist decision-makers achieve a balance between cost and regulation of HM pollution.

ACS Style

Bifeng Hu; Shuai Shao; Zhiyi Fu; Yan Li; Hao Ni; Songchao Chen; Yin Zhou; Bin Jin; Zhou Shi. Identifying heavy metal pollution hot spots in soil-rice systems: A case study in South of Yangtze River Delta, China. Science of The Total Environment 2018, 658, 614 -625.

AMA Style

Bifeng Hu, Shuai Shao, Zhiyi Fu, Yan Li, Hao Ni, Songchao Chen, Yin Zhou, Bin Jin, Zhou Shi. Identifying heavy metal pollution hot spots in soil-rice systems: A case study in South of Yangtze River Delta, China. Science of The Total Environment. 2018; 658 ():614-625.

Chicago/Turabian Style

Bifeng Hu; Shuai Shao; Zhiyi Fu; Yan Li; Hao Ni; Songchao Chen; Yin Zhou; Bin Jin; Zhou Shi. 2018. "Identifying heavy metal pollution hot spots in soil-rice systems: A case study in South of Yangtze River Delta, China." Science of The Total Environment 658, no. : 614-625.

Journal article
Published: 18 November 2018 in Science of The Total Environment
Reads 0
Downloads 0

The soil’s pH is the single most important indicator of the soil’s quality, whether for agriculture, pollution control or environmental health and ecosystem functioning. Well documented data on soil pH are sparse for the whole of China—data for only 4700 soil profiles were available from China’s Second National Soil Inventory. By combining those data, standardized for the topsoil (0–20 cm), with 17 environmental covariates at a fine resolution (3 arc-second or 90 m) we have predicted the soil’s pH at that resolution, that is at more than 109 points. We did so by parallel computing over tiles, each 100 km × 100 km, with two machine learning techniques, namely Random Forest and XGBoost. The predictions for the tiles were then merged into a single map of soil pH for the whole of China. The quality of the predictions were assessed by cross-validation. The root mean squared error (RMSE) was an acceptable 0.71 pH units per point, and Lin’s Concordance Correlation Coefficient was 0.84. The hybrid model revealed that climate (mean annual precipitation and mean annual temperature) and soil type were the main factors determining the soil’s pH. The pH map showed acid soil mainly in southern and north-eastern China, and alkaline soil dominant in northern and western China. This map can provide a benchmark against which to evaluate the impacts of changes in land use and climate on the soil’s pH, and it can guide advisors and agencies who make decisions on remediation and prevention of soil acidification, salinization and pollution by heavy metals, for which we provide examples for cadmium and mercury.

ACS Style

Songchao Chen; ZongZheng Liang; Richard Webster; Ganlin Zhang; Yin Zhou; Hongfen Teng; Bifeng Hu; Dominique Arrouays; Zhou Shi. A high-resolution map of soil pH in China made by hybrid modelling of sparse soil data and environmental covariates and its implications for pollution. Science of The Total Environment 2018, 655, 273 -283.

AMA Style

Songchao Chen, ZongZheng Liang, Richard Webster, Ganlin Zhang, Yin Zhou, Hongfen Teng, Bifeng Hu, Dominique Arrouays, Zhou Shi. A high-resolution map of soil pH in China made by hybrid modelling of sparse soil data and environmental covariates and its implications for pollution. Science of The Total Environment. 2018; 655 ():273-283.

Chicago/Turabian Style

Songchao Chen; ZongZheng Liang; Richard Webster; Ganlin Zhang; Yin Zhou; Hongfen Teng; Bifeng Hu; Dominique Arrouays; Zhou Shi. 2018. "A high-resolution map of soil pH in China made by hybrid modelling of sparse soil data and environmental covariates and its implications for pollution." Science of The Total Environment 655, no. : 273-283.

Preprint content
Published: 13 September 2018 in Earth System Science Data Discussions
Reads 0
Downloads 0

Depth to bedrock serves as the lower boundary of soil, which influences or controls many of the Earth’s physical and chemical processes. It plays important roles in geology, hydrology, land surface processes, civil engineering, and other related fields. This paper describes the materials and methods to produce a high-resolution (100 m) depth-to-bedrock map of China. Observations were interpreted from borehole log data (ca. 6,382 locations) sampled from the Chinese National Important Geological Borehole Database. To fill in large sampling gaps, additional pseudo-observations generated based on expert knowledge were added. Then, we overlaid the training points on a stack of 133 covariates including climatic images, DEM-derived parameters, land-cover and land-use maps, MODIS surface reflectance bands, vegetation index images, and the Harmonized World Soil Database. Spatial prediction models were developed using the random forests and gradient boosting tree, and ensemble prediction results were then obtained by these two independently fitted models. Finally, uncertainty estimation was generated by the quantile regression forest model. The 10-fold cross-validation showed that the ensemble models explain 57 % of the variation in depth to bedrock. Based on comparison with depth-to-bedrock maps of China extracted from previous global predictions, our predictions showed higher accuracy. More observations, especially those in data-sparse areas, should be added to training data, and more covariates with high precision should be used to further improve the accuracy of spatial predictions. The resulting maps of this study are available on Figshare at the following DOI: https://doi.org/10.6084/m9.figshare.7011524.v1. And they are also available for download at http://globalchange.bnu.edu.cn/ .

ACS Style

Fapeng Yan; Wei Shangguan; Jing Zhang; Bifeng Hu. Depth-to-Bedrock Map of China at a Spatial Resolution of 100 Meters. Earth System Science Data Discussions 2018, 1 .

AMA Style

Fapeng Yan, Wei Shangguan, Jing Zhang, Bifeng Hu. Depth-to-Bedrock Map of China at a Spatial Resolution of 100 Meters. Earth System Science Data Discussions. 2018; ():1.

Chicago/Turabian Style

Fapeng Yan; Wei Shangguan; Jing Zhang; Bifeng Hu. 2018. "Depth-to-Bedrock Map of China at a Spatial Resolution of 100 Meters." Earth System Science Data Discussions , no. : 1.