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Prof. Zhou Shi
Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China

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0 Data Fusion
0 soil sensing
0 Pedometrics
0 Digital Soil Mapping
0 Soil Spectroscopy

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Data Fusion
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Soil Spectroscopy

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Journal article
Published: 01 June 2021 in Environmental Research Letters
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ACS Style

Hongfen Teng; Zhongkui Luo; Jinfeng Chang; Zhou Shi; Songchao Chen; Yin Zhou; Philippe Ciais; Hanqin Tian. Climate change-induced greening on the Tibetan Plateau modulated by mountainous characteristics. Environmental Research Letters 2021, 16, 064064 .

AMA Style

Hongfen Teng, Zhongkui Luo, Jinfeng Chang, Zhou Shi, Songchao Chen, Yin Zhou, Philippe Ciais, Hanqin Tian. Climate change-induced greening on the Tibetan Plateau modulated by mountainous characteristics. Environmental Research Letters. 2021; 16 (6):064064.

Chicago/Turabian Style

Hongfen Teng; Zhongkui Luo; Jinfeng Chang; Zhou Shi; Songchao Chen; Yin Zhou; Philippe Ciais; Hanqin Tian. 2021. "Climate change-induced greening on the Tibetan Plateau modulated by mountainous characteristics." Environmental Research Letters 16, no. 6: 064064.

Journal article
Published: 26 May 2021 in Land
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Potentially toxic element (PTE) pollution in farmland soils and crops is a serious cause of concern in China. To analyze the bioaccumulation characteristics of chromium (Cr), zinc (Zn), copper (Cu), and nickel (Ni) in soil-rice systems, 911 pairs of top soil (0–0.2 m) and rice samples were collected from an industrial city in Southeast China. Multiple linear regression (MLR), support vector machines (SVM), random forest (RF), and Cubist were employed to construct models to predict the bioaccumulation coefficient (BAC) of PTEs in soil–rice systems and determine the potential dominators for PTE transfer from soil to rice grains. Cr, Cu, Zn, and Ni contents in soil of the survey region were higher than corresponding background contents in China. The mean Ni content of rice grains exceeded the national permissible limit, whereas the concentrations of Cr, Cu, and Zn were lower than their thresholds. The BAC of PTEs kept the sequence of Zn (0.219) > Cu (0.093) > Ni (0.032) > Cr (0.018). Of the four algorithms employed to estimate the bioaccumulation of Cr, Cu, Zn, and Ni in soil–rice systems, RF exhibited the best performance, with coefficient of determination (R2) ranging from 0.58 to 0.79 and root mean square error (RMSE) ranging from 0.03 to 0.04 mg kg−1. Total PTE concentration in soil, cation exchange capacity (CEC), and annual average precipitation were identified as top 3 dominators influencing PTE transfer from soil to rice grains. This study confirmed the feasibility and advantages of machine learning methods especially RF for estimating PTE accumulation in soil–rice systems, when compared with traditional statistical methods, such as MLR. Our study provides new tools for analyzing the transfer of PTEs from soil to rice, and can help decision-makers in developing more efficient policies for regulating PTE pollution in soil and crops, and reducing the corresponding health risks.

ACS Style

Modian Xie; Hongyi Li; Youwei Zhu; Jie Xue; Qihao You; Bin Jin; Zhou Shi. Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China. Land 2021, 10, 558 .

AMA Style

Modian Xie, Hongyi Li, Youwei Zhu, Jie Xue, Qihao You, Bin Jin, Zhou Shi. Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China. Land. 2021; 10 (6):558.

Chicago/Turabian Style

Modian Xie; Hongyi Li; Youwei Zhu; Jie Xue; Qihao You; Bin Jin; Zhou Shi. 2021. "Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China." Land 10, no. 6: 558.

Journal article
Published: 22 April 2021 in Geoderma
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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: 06 April 2021 in Science of The Total Environment
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Ranking assessment of potentially contaminated sites (PCS) provides a great quantity of information (namely the risk screening list) that is usually examined by environmental managers, and therefore reduces the cost of risk management in terms of site investigation. Here we propose an integrated assessment methodology to establish a risk screening list of PCS in China using the Choquet integral correlation coefficient (ICC), which takes the uncertainty and interaction of PCS attributes into explicit account. The proposed method globally considers the importance and ordered positions of PCS attributes while reflecting their overall ranking. The model evaluation and actual validation results demonstrate the success in PCS ranking by the proposed method, which is superior to other methods such as the intuitionistic fuzzy multiple attribute decision-making, the technique for order preference by similarity to an ideal solution, and the weighted average. The resulting spatial distribution of Choquet ICC indicates that high-attention PCS in China are mainly located in Guangdong, Jiangsu, Zhejiang, and Shandong Provinces. This study is the first attempt to conduct a ranking assessment of PCS across China. The proposed assessment method based on Choquet ICC offers a step towards establishing a risk screening list of PCS globally.

ACS Style

Yefeng Jiang; Hanlin Wang; Mei Lei; Deyi Hou; Songchao Chen; Bifeng Hu; Mingxiang Huang; Weiwei Song; Zhou Shi. An integrated assessment methodology for management of potentially contaminated sites based on public data. Science of The Total Environment 2021, 783, 146913 .

AMA Style

Yefeng Jiang, Hanlin Wang, Mei Lei, Deyi Hou, Songchao Chen, Bifeng Hu, Mingxiang Huang, Weiwei Song, Zhou Shi. An integrated assessment methodology for management of potentially contaminated sites based on public data. Science of The Total Environment. 2021; 783 ():146913.

Chicago/Turabian Style

Yefeng Jiang; Hanlin Wang; Mei Lei; Deyi Hou; Songchao Chen; Bifeng Hu; Mingxiang Huang; Weiwei Song; Zhou Shi. 2021. "An integrated assessment methodology for management of potentially contaminated sites based on public data." Science of The Total Environment 783, no. : 146913.

Journal article
Published: 05 March 2021 in Journal of Cleaner Production
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The concentration of fine particulate matter (PM2.5) has a significant impact on the environment and human health. However, strong spatial heterogeneity and spatiotemporal dependence increases the difficulty of prediction. Moreover, due to the lag of the update of auxiliary variables at national scale in the prediction application, it is still difficult to achieve the timely nationwide PM2.5 prediction at present. To better model and predict real time concentrations and spatial distributions of PM2.5, this study developed a workflow of future PM2.5 concentrations prediction based on long short-term memory (LSTM) model. Using ground-based station PM2.5 data in 2014–2018, the 1 km Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) product and other auxiliary data to predict PM2.5 concentrations in the next year and generate a high-resolution national PM2.5 concentration spatial distribution map. The LSTM model outperformed random forest (RF) and Cubist approaches for prediction PM2.5 because of its recurrent neural network structure that can capture time dependence and nonlinear relationships among PM2.5 concentrations and other independent variables, and exhibited a stable accuracy with an R2 of 0.83, by applying the annual time series, with an improvement of 0.04–0.09, compared to daily and monthly data. The results indicated that PM2.5 pollution had gradually decreased in 2019 after application of pollution controls, with annual mean PM2.5 concentrations of 27.33 ± 15.56 μg m−3, although there were still some areas with severe pollution, including the North China Plain, parts of the Loess Plateau, and the Taklimakan Desert. The LSTM model makes it possible to predict fine-scale PM2.5 spatial distributions nationwide in the future and may thus be useful for sustainable management and control of air pollution at a national scale.

ACS Style

Zhige Wang; Yue Zhou; Ruiying Zhao; Nan Wang; Asim Biswas; Zhou Shi. High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model. Journal of Cleaner Production 2021, 297, 126493 .

AMA Style

Zhige Wang, Yue Zhou, Ruiying Zhao, Nan Wang, Asim Biswas, Zhou Shi. High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model. Journal of Cleaner Production. 2021; 297 ():126493.

Chicago/Turabian Style

Zhige Wang; Yue Zhou; Ruiying Zhao; Nan Wang; Asim Biswas; Zhou Shi. 2021. "High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model." Journal of Cleaner Production 297, no. : 126493.

Editorial
Published: 04 March 2021 in Remote Sensing
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Recent advances in remote and proximal sensing technologies provide a valuable source of information for enriching our geo-datasets, which are necessary for soil management and the precision application of farming input resources

ACS Style

Abdul Mouazen; Zhou Shi. Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion. Remote Sensing 2021, 13, 978 .

AMA Style

Abdul Mouazen, Zhou Shi. Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion. Remote Sensing. 2021; 13 (5):978.

Chicago/Turabian Style

Abdul Mouazen; Zhou Shi. 2021. "Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion." Remote Sensing 13, no. 5: 978.

Journal article
Published: 26 February 2021 in Remote Sensing
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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.

Correction
Published: 20 February 2021 in Remote Sensing
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The authors wish to make the following correction to this paper

ACS Style

Yeneng Lin; Dongyun Xu; Nan Wang; Zhou Shi; Qiuxiao Chen. Correction: Lin, Y., et al. Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sens. 2020, 12, 2985. Remote Sensing 2021, 13, 783 .

AMA Style

Yeneng Lin, Dongyun Xu, Nan Wang, Zhou Shi, Qiuxiao Chen. Correction: Lin, Y., et al. Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sens. 2020, 12, 2985. Remote Sensing. 2021; 13 (4):783.

Chicago/Turabian Style

Yeneng Lin; Dongyun Xu; Nan Wang; Zhou Shi; Qiuxiao Chen. 2021. "Correction: Lin, Y., et al. Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sens. 2020, 12, 2985." Remote Sensing 13, no. 4: 783.

Journal article
Published: 25 January 2021 in International Journal of Environmental Research and Public Health
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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: 16 December 2020 in Remote Sensing
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Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of salinization is critical for management, remediation, and utilization of salinized soil; however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in diverse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil samples in a field campaign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were divided into vegetation-covered and bare soil samples and were used for accuracy assessment. The RF model was the best regression model in this study, with R2 = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of soil salinity in arid and semi-arid areas from the integration of remote sensing, landscape characteristics, and using machine learning model.

ACS Style

Nan Wang; Jie Xue; Jie Peng; Asim Biswas; Yong He; Zhou Shi. Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China. Remote Sensing 2020, 12, 4118 .

AMA Style

Nan Wang, Jie Xue, Jie Peng, Asim Biswas, Yong He, Zhou Shi. Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China. Remote Sensing. 2020; 12 (24):4118.

Chicago/Turabian Style

Nan Wang; Jie Xue; Jie Peng; Asim Biswas; Yong He; Zhou Shi. 2020. "Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China." Remote Sensing 12, no. 24: 4118.

Journal article
Published: 17 November 2020 in Remote Sensing
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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: 14 September 2020 in Remote Sensing
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Automatic road extraction from very-high-resolution remote sensing images has become a popular topic in a wide range of fields. Convolutional neural networks are often used for this purpose. However, many network models do not achieve satisfactory extraction results because of the elongated nature and varying sizes of roads in images. To improve the accuracy of road extraction, this paper proposes a deep learning model based on the structure of Deeplab v3. It incorporates squeeze-and-excitation (SE) module to apply weights to different feature channels, and performs multi-scale upsampling to preserve and fuse shallow and deep information. To solve the problems associated with unbalanced road samples in images, different loss functions and backbone network modules are tested in the model’s training process. Compared with cross entropy, dice loss can improve the performance of the model during training and prediction. The SE module is superior to ResNext and ResNet in improving the integrity of the extracted roads. Experimental results obtained using the Massachusetts Roads Dataset show that the proposed model (Nested SE-Deeplab) improves F1-Score by 2.4% and Intersection over Union by 2.0% compared with FC-DenseNet. The proposed model also achieves better segmentation accuracy in road extraction compared with other mainstream deep-learning models including Deeplab v3, SegNet, and UNet.

ACS Style

Yeneng Lin; Dongyun Xu; Nan Wang; Zhou Shi; Qiuxiao Chen. Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sensing 2020, 12, 2985 .

AMA Style

Yeneng Lin, Dongyun Xu, Nan Wang, Zhou Shi, Qiuxiao Chen. Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sensing. 2020; 12 (18):2985.

Chicago/Turabian Style

Yeneng Lin; Dongyun Xu; Nan Wang; Zhou Shi; Qiuxiao Chen. 2020. "Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model." Remote Sensing 12, no. 18: 2985.

Journal article
Published: 07 August 2020 in Pedosphere
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The Tibetan Plateau of China is uniquely vulnerable to the global climate change and anthropogenic disturbances. As soil bacteria exert a considerable influence on the ecosystem function, understanding their response to different climates and land-use types is important. Here, we characterized the bacterial community composition and diversity across three major ecosystems (cropland, forest, and grassland) in the Sygera Mountains of Tibet, along a typical elevational gradient (3 300–4 600 m). The abundance of taxa that preferentially inhabit neutral or weak alkaline soil environments (such as Actinobacteria, Thermoleophilia, and some non-acidophilus Acidobacteria) was significantly greater in the cropland than in the forest and grassland. Furthermore, the diversity of soil bacterial communities was also significantly greater in the cropland than in the forest and grassland. We observed a unimodal distribution of bacterial species diversity along the elevation gradient. The dominant phyla Acidobacteria and Proteobacteria exhibited consistent elevational distribution patterns that mirrored the abundance of their most abundant classes, while different patterns were observed for Acidobacteria and Proteobacteria at the class level. Soil pH was the primary edaphic property that regulated bacterial community composition across the different land-use types. Additionally, soil pH was the main factor distinguishing bacterial communities in managed soils (i.e., cropland) from the communities in the natural environments (i.e., forest and grassland). In conclusion, land use (particularly anthropogenic disturbances such as cropping) largely controlled soil environment, played a major role in driving bacterial community composition and distribution, and also surpassed climate in affecting bacterial community distribution.

ACS Style

Yuanyuan Yang; Yin Zhou; Zhou Shi; Raphael A. Viscarra Rossel; ZongZheng Liang; Haizhen Wang; Lianqing Zhou; Wu Yu. Interactive effects of elevation and land use on soil bacterial communities in the Tibetan Plateau. Pedosphere 2020, 30, 817 -831.

AMA Style

Yuanyuan Yang, Yin Zhou, Zhou Shi, Raphael A. Viscarra Rossel, ZongZheng Liang, Haizhen Wang, Lianqing Zhou, Wu Yu. Interactive effects of elevation and land use on soil bacterial communities in the Tibetan Plateau. Pedosphere. 2020; 30 (6):817-831.

Chicago/Turabian Style

Yuanyuan Yang; Yin Zhou; Zhou Shi; Raphael A. Viscarra Rossel; ZongZheng Liang; Haizhen Wang; Lianqing Zhou; Wu Yu. 2020. "Interactive effects of elevation and land use on soil bacterial communities in the Tibetan Plateau." Pedosphere 30, no. 6: 817-831.

Journal article
Published: 19 July 2020 in Science of The Total Environment
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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.

Data description paper
Published: 08 July 2020 in Earth System Science Data
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Precipitation estimates with fine quality and spatio-temporal resolutions play significant roles in understanding the global and regional cycles of water, carbon, and energy. Satellite-based precipitation products are capable of detecting spatial patterns and temporal variations of precipitation at fine resolutions, which is particularly useful over poorly gauged regions. However, satellite-based precipitation products are the indirect estimates of precipitation, inherently containing regional and seasonal systematic biases and random errors. In this study, focusing on the potential drawbacks in generating Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and its recently updated retrospective IMERG in the Tropical Rainfall Measuring Mission (TRMM) era (finished in July 2019), which were only calibrated at a monthly scale using ground observations, Global Precipitation Climatology Centre (GPCC, 1.0∘/monthly), we aim to propose a new calibration algorithm for IMERG at a daily scale and to provide a new AIMERG precipitation dataset (0.1∘/half-hourly, 2000–2015, Asia) with better quality, calibrated by Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 0.25∘/daily) at the daily scale for the Asian applications. The main conclusions include but are not limited to the following: (1) the proposed daily calibration algorithm (Daily Spatio-Temporal Disaggregation Calibration Algorithm, DSTDCA) is effective in considering the advantages from both satellite-based precipitation estimates and the ground observations; (2) AIMERG performs better than IMERG at different spatio-temporal scales, in terms of both systematic biases and random errors, over mainland China; and (3) APHRODITE demonstrates significant advantages compared to GPCC in calibrating IMERG, especially over mountainous regions with complex terrain, e.g. the Tibetan Plateau. Additionally, results of this study suggest that it is a promising and applicable daily calibration algorithm for GPM in generating the future IMERG in either an operational scheme or a retrospective manner. The AIMERG data are freely accessible at https://doi.org/10.5281/zenodo.3609352 (for the period from 2000 to 2008) (Ma et al., 2020a) and https://doi.org/10.5281/zenodo.3609507 (for the period from 2009 to 2015) (Ma et al., 2020b). Highlights. A new effective daily calibration approach, DSTDCA, for improving the GPM-era IMERG is provided. New AIMERG precipitation data (0.1∘/half-hourly, 2000–2015, Asia) are provided. Bias of AIMERG is significantly improved compared with that of IMERG. APHRODITE is more suitable than GPCC in anchoring IMERG over Asia.

ACS Style

Ziqiang Ma; Jintao Xu; Siyu Zhu; Jun Yang; Guoqiang Tang; YuanJian Yang; Zhou Shi; Yang Hong. AIMERG: a new Asian precipitation dataset (0.1°/half-hourly, 2000–2015) by calibrating the GPM-era IMERG at a daily scale using APHRODITE. Earth System Science Data 2020, 12, 1525 -1544.

AMA Style

Ziqiang Ma, Jintao Xu, Siyu Zhu, Jun Yang, Guoqiang Tang, YuanJian Yang, Zhou Shi, Yang Hong. AIMERG: a new Asian precipitation dataset (0.1°/half-hourly, 2000–2015) by calibrating the GPM-era IMERG at a daily scale using APHRODITE. Earth System Science Data. 2020; 12 (3):1525-1544.

Chicago/Turabian Style

Ziqiang Ma; Jintao Xu; Siyu Zhu; Jun Yang; Guoqiang Tang; YuanJian Yang; Zhou Shi; Yang Hong. 2020. "AIMERG: a new Asian precipitation dataset (0.1°/half-hourly, 2000–2015) by calibrating the GPM-era IMERG at a daily scale using APHRODITE." Earth System Science Data 12, no. 3: 1525-1544.

Research article
Published: 27 June 2020 in Journal of Environmental Management
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The spatio-temporal variation and temporal changes in the sources of Cr, Pb, Cd, Hg, and As in soil on the Hangzhou–Jiaxing–Huzhou (H-J-H) Plain were analysed based on 4,359 soil samples collected in 2002 and 2012. Geostatistical and spatial analysis methods were used to explore the spatio-temporal variation in the pollution levels and ‘pollution hotspots’ for potentially toxic elements (PTEs), and the positive matrix factor model was used to quantitatively appoint and analyse temporal changes in PTE sources. The results indicated that the PTE content in most parts of the survey area were at a safe level in both 2002 and 2012, but a clearly upward trend was detected for Cr, Pb, and Cd. Moreover the pollution index for Cr, Pb, Cd, and the Nemerow composite pollution index increased in the west but decreased in the east of the H-J-H Plain from 2002 to 2012. The pollution index for Hg and As presented the opposite spatial pattern. It is obvious that there have been changes in the spatial pattern of pollution hotspots for PTEs on the H-J-H Plain from 2002 to 2012. Four sources of PTEs in soil were quantitatively appointed. In 2002, 2012, the dominant sources of Cr, Cd, Hg, and As were soil parent materials, industrial activities, atmospheric deposition and agricultural inputs, respectively. The dominant source of Pb in the soil changed from traffic emissions to soil parent materials, indicating the benefit of banning the use of leaded gasoline in China. This study highlights the importance of monitoring soil environmental quality and highlights the significance of spatio-temporal variation in PTEs in suburban zones or transitional areas undergoing rapid industrialization and urbanization, like the H-J-H Plain.

ACS Style

Bifeng Hu; Yin Zhou; Yefeng Jiang; Wenjun Ji; Zhiyi Fu; Shuai Shao; Shuo Li; Mingxiang Huang; Lianqing Zhou; Zhou Shi. Spatio-temporal variation and source changes of potentially toxic elements in soil on a typical plain of the Yangtze River Delta, China (2002–2012). Journal of Environmental Management 2020, 271, 110943 .

AMA Style

Bifeng Hu, Yin Zhou, Yefeng Jiang, Wenjun Ji, Zhiyi Fu, Shuai Shao, Shuo Li, Mingxiang Huang, Lianqing Zhou, Zhou Shi. Spatio-temporal variation and source changes of potentially toxic elements in soil on a typical plain of the Yangtze River Delta, China (2002–2012). Journal of Environmental Management. 2020; 271 ():110943.

Chicago/Turabian Style

Bifeng Hu; Yin Zhou; Yefeng Jiang; Wenjun Ji; Zhiyi Fu; Shuai Shao; Shuo Li; Mingxiang Huang; Lianqing Zhou; Zhou Shi. 2020. "Spatio-temporal variation and source changes of potentially toxic elements in soil on a typical plain of the Yangtze River Delta, China (2002–2012)." Journal of Environmental Management 271, no. : 110943.

Journal article
Published: 09 June 2020 in Environmental Pollution
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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.

ACS Style

Bifeng Hu; Shuai Shao; Hao Ni; Zhiyi Fu; Linshu Hu; Yin Zhou; Xiaoxiao Min; Shufeng She; Songchao Chen; Mingxiang Huang; Lianqing Zhou; Yan Li; Zhou Shi. Current status, spatial features, health risks, and potential driving factors of soil heavy metal pollution in China at province level. Environmental Pollution 2020, 266, 114961 .

AMA Style

Bifeng Hu, Shuai Shao, Hao Ni, Zhiyi Fu, Linshu Hu, Yin Zhou, Xiaoxiao Min, Shufeng She, Songchao Chen, Mingxiang Huang, Lianqing Zhou, Yan Li, Zhou Shi. Current status, spatial features, health risks, and potential driving factors of soil heavy metal pollution in China at province level. Environmental Pollution. 2020; 266 ():114961.

Chicago/Turabian Style

Bifeng Hu; Shuai Shao; Hao Ni; Zhiyi Fu; Linshu Hu; Yin Zhou; Xiaoxiao Min; Shufeng She; Songchao Chen; Mingxiang Huang; Lianqing Zhou; Yan Li; Zhou Shi. 2020. "Current status, spatial features, health risks, and potential driving factors of soil heavy metal pollution in China at province level." Environmental Pollution 266, no. : 114961.

Withdrawal
Published: 06 June 2020 in Journal of Cleaner Production
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As the Chinese government stated in the Action Plan on Prevention and Control of Soil Pollution, 95% of the contaminated sites in China would be safe for use by 2030. To achieve this ambitious goal, managing potentially contaminated sites (PCS) is the first step. However, China still lacks basic information on the quantity, location, and spatiotemporal distribution of PCS, and the current risk assessment of PCS is particularly rare. In this study, we integrated China’s site survey data on PCS to analyze their spatiotemporal distribution and to assess the health risks posed by PCS. The results showed that the number of PCS in China increased from 7586 in 2000 to 38,020 in 2018, more than a fourfold increase. In 2000, very small areas had either a medium or medium-high density of PCS; however, in 2018, this density in many areas became medium-high or high, especially in Eastern China. The spatiotemporal distribution of PCS was driven by both industrialization and urbanization in China. From 2000 to 2018, the impact of these two factors on the PCS distribution in Central and Western China was generally lower than that in Eastern China. Hazard assessment indicated that relative high-hazard areas were mainly located in Shanxi, Hebei, Xinjiang, and Yunnan provinces. All provinces contained absolute high-hazard areas, except Tibet and Hunan. We present a strategy to promote PCS management based on a revised source-pathway-receptor model and to achieve the ambition on soil pollution control in China.

ACS Style

Yefeng Jiang; Hanlin Wang; Yin Zhou; Bifeng Hu; Mingxiang Huang; Zhe Xu; Zhou Shi. WITHDRAWN: Potentially contaminated sites in China: Spatiotemporal distribution and current risk assessment. Journal of Cleaner Production 2020, 122480 .

AMA Style

Yefeng Jiang, Hanlin Wang, Yin Zhou, Bifeng Hu, Mingxiang Huang, Zhe Xu, Zhou Shi. WITHDRAWN: Potentially contaminated sites in China: Spatiotemporal distribution and current risk assessment. Journal of Cleaner Production. 2020; ():122480.

Chicago/Turabian Style

Yefeng Jiang; Hanlin Wang; Yin Zhou; Bifeng Hu; Mingxiang Huang; Zhe Xu; Zhou Shi. 2020. "WITHDRAWN: Potentially contaminated sites in China: Spatiotemporal distribution and current risk assessment." Journal of Cleaner Production , no. : 122480.

Primary research articles
Published: 13 May 2020 in Global Change Biology
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Soil organic carbon (SOC), the largest terrestrial carbon pool, plays a significant role in soil‐related ecosystem services such as climate regulation, soil fertility, and agricultural production. However, its fate under land use change is difficult to predict. A major issue is that SOC is comprised of numerous organic compounds with potentially distinct and poorly understood turnover properties. Here we use spatiotemporal measurements of the particulate (POC), mineral‐associated (MOC), and charred SOC (COC) fractions from 176 trials involving changes in land‐use to assess their underlying controls. We find that the initial pool sizes of each of the three fractions consistently and dominantly control their temporal dynamics after changes in land‐use (i.e. the baseline effects). The effects of climate, soil physicochemical properties and plant residues, however, are fraction‐ and time‐dependent. Climate and soil properties show similar importance for controlling the dynamics of MOC and COC, while plant residue inputs (in term of their quantity and quality) are much less important. For POC, plant residues and management practices (e.g., the frequency of pasture in crop‐pasture rotation systems) are substantially more important, overriding the influence of climate. These results demonstrate the pivotal role of measuring SOC composition and considering fraction‐specific stabilization and destabilization processes for effective SOC management and reliable SOC predictions.

ACS Style

Zhongkui Luo; Raphael A. Viscarra Rossel; Zhou Shi. Distinct controls over the temporal dynamics of soil carbon fractions after land use change. Global Change Biology 2020, 26, 4614 -4625.

AMA Style

Zhongkui Luo, Raphael A. Viscarra Rossel, Zhou Shi. Distinct controls over the temporal dynamics of soil carbon fractions after land use change. Global Change Biology. 2020; 26 (8):4614-4625.

Chicago/Turabian Style

Zhongkui Luo; Raphael A. Viscarra Rossel; Zhou Shi. 2020. "Distinct controls over the temporal dynamics of soil carbon fractions after land use change." Global Change Biology 26, no. 8: 4614-4625.

Featured cover
Published: 11 May 2020 in Land Degradation & Development
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Songchao Chen; Dongyun Xu; Shuo Li; Wenjun Ji; Meihua Yang; Yin Zhou; Bifeng Hu; Hanyi Xu; Zhou Shi. Featured Cover. Land Degradation & Development 2020, 31, 1 .

AMA Style

Songchao Chen, Dongyun Xu, Shuo Li, Wenjun Ji, Meihua Yang, Yin Zhou, Bifeng Hu, Hanyi Xu, Zhou Shi. Featured Cover. Land Degradation & Development. 2020; 31 (8):1.

Chicago/Turabian Style

Songchao Chen; Dongyun Xu; Shuo Li; Wenjun Ji; Meihua Yang; Yin Zhou; Bifeng Hu; Hanyi Xu; Zhou Shi. 2020. "Featured Cover." Land Degradation & Development 31, no. 8: 1.