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Nengcheng Chen
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China

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Journal article
Published: 27 July 2021 in Atmosphere
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Tea is one of the most significant cash crops and plays an important role in economic development and poverty reduction. On the other hand, tea is an optimal choice in the extreme weather conditions of Tanuyen Laichau, Vietnam. In our study, the NDVI variation of tea in the growing season from 2009 to 2018 was showed by calculating NDVI trend and the Mann-Kendall analysis to assess trends in the time series. Support Vector Machine (SVM) and Random Forest (RF) model were used for predicting tea yield. The NDVI of tea showed an increasing trend with a slope from −0.001–0.001 (88.9% of the total area), a slope from 0.001–0.002 (11.1% of the total area) and a growing rate of 0.00075/year. The response of tea NDVI to almost climatic factor in a one-month time lag is higher than the current month. The tea yield was estimated with higher accuracy in the RF model. Among the input variables, we detected that the role of Tmean and NDVI is stronger than other variables when squared with each of the independent variables into input data.

ACS Style

Phamchimai Phan; Nengcheng Chen; Lei Xu; Duy Dao; DinhKha Dang. NDVI Variation and Yield Prediction in Growing Season: A Case Study with Tea in Tanuyen Vietnam. Atmosphere 2021, 12, 962 .

AMA Style

Phamchimai Phan, Nengcheng Chen, Lei Xu, Duy Dao, DinhKha Dang. NDVI Variation and Yield Prediction in Growing Season: A Case Study with Tea in Tanuyen Vietnam. Atmosphere. 2021; 12 (8):962.

Chicago/Turabian Style

Phamchimai Phan; Nengcheng Chen; Lei Xu; Duy Dao; DinhKha Dang. 2021. "NDVI Variation and Yield Prediction in Growing Season: A Case Study with Tea in Tanuyen Vietnam." Atmosphere 12, no. 8: 962.

Journal article
Published: 13 January 2021 in ISPRS International Journal of Geo-Information
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The satellite-retrieved Aerosol Optical Depth (AOD) is widely used to estimate the concentrations and analyze the spatiotemporal pattern of Particulate Matter that is less than or equal to 2.5 microns (PM2.5), also providing a way for the related research of air pollution. Many studies generated PM2.5 concentration networks with resolutions of 3 km or 10 km. However, the relatively coarse resolution of the satellite AOD products make it difficult to determine the fine-scale characteristics of PM2.5 distributions that are important for urban air quality analysis. In addition, the composition and chemical properties of PM2.5 are relatively complex and might be affected by many factors, such as meteorological and land cover type factors. In this paper, an AOD product with a 1 km spatial resolution derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, the PM2.5 measurements from ground sites and the meteorological data as the auxiliary variable, are integrated into the Modified Support Vector Regression (MSVR) model that proposed in this paper to estimate the PM2.5 concentrations and analyze the spatiotemporal pattern of PM2.5. Considering the relatively small dataset and the somewhat complex relationship between the variables, we propose a Modified Support Vector Regression (MSVR) model that based on SVR to fit and estimate the PM2.5 concentrations in Hubei province of China. In this paper, we obtained Cross Correlation Coefficient (R²) of 0.74 for the regression of independent and dependent variables, and the conventional SVR model obtained R² of 0.60 as comparison. We think our MSVR model obtained relatively good performance in spite of many complex factors that might impact the accuracy. We then utilized the optimal MSVR model to perform the PM2.5 estimating, analyze their spatiotemporal patterns, and try to explain the possible reasons for these patterns. The results showed that the PM2.5 estimations retrieved from 1 km MAIAC AOD could reflect more detailed spatial distribution characteristics of PM2.5 and have higher accuracy than that from 3 km MODIS AOD. Therefore, the proposed MSVR model can be a better method for PM2.5 estimating, especially when the dataset is relatively small.

ACS Style

Nengcheng Chen; Meijuan Yang; Wenying Du; Min Huang. PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China. ISPRS International Journal of Geo-Information 2021, 10, 31 .

AMA Style

Nengcheng Chen, Meijuan Yang, Wenying Du, Min Huang. PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China. ISPRS International Journal of Geo-Information. 2021; 10 (1):31.

Chicago/Turabian Style

Nengcheng Chen; Meijuan Yang; Wenying Du; Min Huang. 2021. "PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China." ISPRS International Journal of Geo-Information 10, no. 1: 31.

Journal article
Published: 10 December 2020 in International Journal of Environmental Research and Public Health
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The online public opinion is the sum of public views, attitudes and emotions spread on major public health emergencies through the Internet, which maps out the scope of influence and the disaster situation of public health events in real space. Based on the multi-source data of COVID-19 in the context of a global pandemic, this paper analyzes the propagation rules of disasters in the coupling of the spatial dimension of geographic reality and the dimension of network public opinion, and constructs a new gravity model-complex network-based geographic propagation model of the evolution chain of typical public health events. The strength of the model is that it quantifies the extent of the impact of the epidemic area on the surrounding area and the spread of the epidemic, constructing an interaction between the geographical reality dimension and online public opinion dimension. The results show that: The heterogeneity in the direction of social media discussions before and after the “closure” of Wuhan is evident, with the center of gravity clearly shifting across the Yangtze River and the cyclical changing in public sentiment; the network model based on the evolutionary chain has a significant community structure in geographic space, divided into seven regions with a modularity of 0.793; there are multiple key infection trigger nodes in the network, with a spatially polycentric infection distribution.

ACS Style

Yan Zhang; Nengcheng Chen; Wenying Du; Shuang Yao; Xiang Zheng. A New Geo-Propagation Model of Event Evolution Chain Based on Public Opinion and Epidemic Coupling. International Journal of Environmental Research and Public Health 2020, 17, 9235 .

AMA Style

Yan Zhang, Nengcheng Chen, Wenying Du, Shuang Yao, Xiang Zheng. A New Geo-Propagation Model of Event Evolution Chain Based on Public Opinion and Epidemic Coupling. International Journal of Environmental Research and Public Health. 2020; 17 (24):9235.

Chicago/Turabian Style

Yan Zhang; Nengcheng Chen; Wenying Du; Shuang Yao; Xiang Zheng. 2020. "A New Geo-Propagation Model of Event Evolution Chain Based on Public Opinion and Epidemic Coupling." International Journal of Environmental Research and Public Health 17, no. 24: 9235.

Research article
Published: 25 November 2020 in Journal of Sensors
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Along with the rapid development of remote sensing satellites and sensor network technology, vast amounts of remote sensing imagery and in situ observations have been accumulated. Further, various researchers and agencies have released a variety of thematic image products. These heterogeneous observations are therefore difficult to utilize comprehensively. In this study, an ontology-based framework for integrating remote sensing imagery, image products, and in situ observations was developed. It was extended based on the Semantic Sensor Network (SSN) ontology in the Web Ontology Language (OWL). The detailed process of ontology construction and rule establishment was demonstrated. Combined with some actual remote sensing imagery, image products, and in situ observations, semantic queries based on DL Query and SPARQL were conducted to establish the rationality and feasibility of the ontology and framework.

ACS Style

Chao Wang; Xinyan Zhuo; Pengfei Li; Nengcheng Chen; Wei Wang; Zeqiang Chen. An Ontology-Based Framework for Integrating Remote Sensing Imagery, Image Products, and In Situ Observations. Journal of Sensors 2020, 2020, 1 -12.

AMA Style

Chao Wang, Xinyan Zhuo, Pengfei Li, Nengcheng Chen, Wei Wang, Zeqiang Chen. An Ontology-Based Framework for Integrating Remote Sensing Imagery, Image Products, and In Situ Observations. Journal of Sensors. 2020; 2020 ():1-12.

Chicago/Turabian Style

Chao Wang; Xinyan Zhuo; Pengfei Li; Nengcheng Chen; Wei Wang; Zeqiang Chen. 2020. "An Ontology-Based Framework for Integrating Remote Sensing Imagery, Image Products, and In Situ Observations." Journal of Sensors 2020, no. : 1-12.

Journal article
Published: 11 November 2020 in Geophysical Research Letters
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ACS Style

Lei Xu; Chong Zhang; Nengcheng Chen; Hamid Moradkhani; Pao‐Shin Chu; Xiang Zhang. Potential Precipitation Predictability Decreases Under Future Warming. Geophysical Research Letters 2020, 47, 1 .

AMA Style

Lei Xu, Chong Zhang, Nengcheng Chen, Hamid Moradkhani, Pao‐Shin Chu, Xiang Zhang. Potential Precipitation Predictability Decreases Under Future Warming. Geophysical Research Letters. 2020; 47 (22):1.

Chicago/Turabian Style

Lei Xu; Chong Zhang; Nengcheng Chen; Hamid Moradkhani; Pao‐Shin Chu; Xiang Zhang. 2020. "Potential Precipitation Predictability Decreases Under Future Warming." Geophysical Research Letters 47, no. 22: 1.

Journal article
Published: 15 September 2020 in Remote Sensing
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The knowledge of the area and spatial distribution of paddy rice fields is important for water resource management. However, accurate map of paddy rice is a long-term challenge because of its spatiotemporal discontinuity and short duration. To solve this problem, this study proposed a paddy rice area extraction approach by using the combination of optical vegetation indices and synthetic aperture radar (SAR) data. This method is designed to overcome the data-missing problem due to cloud contamination and spatiotemporal discontinuities of the traditional optical remote sensing method. More specifically, the Sentinel-1A SAR and the Sentinel-2 multispectral imager (MSI) Level-2A imagery are used to identify paddy rice with a high temporal and spatial resolution. Three vegetation indices, namely normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface water index (LSWI), are estimated from optical bands. Two polarization bands (VH (vertical-horizontal) and VV (vertical-vertical)) are used to overcome the cloud contamination problem. This approach was applied with the random forest machine learning algorithm on the Google Earth Engine platform for the Jianghan Plain in China as an experimental area. The results of 39 experiments uncovered the effect of different factors. The results indicated that the combination of VV and VH band showed a better performance compared with other polarization bands; the average producer’s accuracy of paddy rice (PA) is 72.79%, 1.58% higher than the second one VH. Secondly, the combination of three indices also showed a better result than others, with average PA 73.82%, 1.42% higher than using NDVI alone. The classification result presented the best combination is EVI, VV, and VH polarization band. The producer’s accuracy of paddy rice was 76.67%, with the overall accuracy (OA) of 66.07%, and Kappa statistics of 0.45. However, NDVI, EVI, and VH showed better performance in mapping the morphology. The results demonstrated the method developed in this study can be successfully applied to the cloud-prone area for mapping paddy rice to overcome the data missing caused by cloud and rain during the paddy growing season.

ACS Style

Nengcheng Chen; Lixiaona Yu; Xiang Zhang; Yonglin Shen; Linglin Zeng; Qiong Hu; Dev Niyogi. Mapping Paddy Rice Fields by Combining Multi-Temporal Vegetation Index and Synthetic Aperture Radar Remote Sensing Data Using Google Earth Engine Machine Learning Platform. Remote Sensing 2020, 12, 2992 .

AMA Style

Nengcheng Chen, Lixiaona Yu, Xiang Zhang, Yonglin Shen, Linglin Zeng, Qiong Hu, Dev Niyogi. Mapping Paddy Rice Fields by Combining Multi-Temporal Vegetation Index and Synthetic Aperture Radar Remote Sensing Data Using Google Earth Engine Machine Learning Platform. Remote Sensing. 2020; 12 (18):2992.

Chicago/Turabian Style

Nengcheng Chen; Lixiaona Yu; Xiang Zhang; Yonglin Shen; Linglin Zeng; Qiong Hu; Dev Niyogi. 2020. "Mapping Paddy Rice Fields by Combining Multi-Temporal Vegetation Index and Synthetic Aperture Radar Remote Sensing Data Using Google Earth Engine Machine Learning Platform." Remote Sensing 12, no. 18: 2992.

Journal article
Published: 10 September 2020 in Computers, Environment and Urban Systems
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Accurate forecasting of future urban land expansion can provide useful information for policy makers and urban planners to better plan for the impacts of future land use and land cover change (LULCC) on the ecosystem. However, most current studies do not emphasize spatial variations in the influence intensities of the various driving forces, resulting in unreliable predictions of future urban development. This study aimed to enhance the capability of the SLEUTH model, a cellular automaton model that is commonly used to measure and forecast urban growth and LULCC, by embedding an urban suitability surface from geographically weighted logistic regression (GWLR). Moreover, to examine the performance of the loosely-coupled GWLR-SLEUTH model, a layer with only water bodies excluded and a layer combining the former with an urban suitability surface from logistic regression (LR) were also used in SLEUTH in separate model calibrations. This study was applied to the largest metropolitan area in central China, the Wuhan metropolitan area (WMA). Results show that the integrated GWLR-SLEUTH model performed better than either the traditional SLEUTH model or the LR-SLEUTH model. Findings demonstrate that spatial nonstationarity existed in the drivers' impacts on the urban expansion in the study area and that terrain, transportation and socioeconomic factors were the major drivers of urban expansion in the study area. Finally, with the optimal calibrated parameter sets from the GWLR-SLEUTH model, an urban land forecast from 2017 to 2035 was conducted under three scenarios: 1) business as usual; 2) under future planning policy; and 3) ecologically sustainable growth. Findings show that future planning policy may promise a more sustainable urban development if the plan is strictly obeyed. This study recommended that spatial heterogeneity should be taken into account in the process of land change modeling and the integrated model can be applied to other areas for further validation and forecasts.

ACS Style

Dandan Liu; Keith C. Clarke; Nengcheng Chen. Integrating spatial nonstationarity into SLEUTH for urban growth modeling: A case study in the Wuhan metropolitan area. Computers, Environment and Urban Systems 2020, 84, 101545 .

AMA Style

Dandan Liu, Keith C. Clarke, Nengcheng Chen. Integrating spatial nonstationarity into SLEUTH for urban growth modeling: A case study in the Wuhan metropolitan area. Computers, Environment and Urban Systems. 2020; 84 ():101545.

Chicago/Turabian Style

Dandan Liu; Keith C. Clarke; Nengcheng Chen. 2020. "Integrating spatial nonstationarity into SLEUTH for urban growth modeling: A case study in the Wuhan metropolitan area." Computers, Environment and Urban Systems 84, no. : 101545.

Journal article
Published: 04 September 2020 in Remote Sensing
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Urban green space (UGS) is considered a mitigative intervention for urban heat. While increasing the UGS coverage is expected to reduce the urban heat, studies on the effects of UGS configuration have produced inconsistent results. To investigate this inconsistency further, this study conducted a multi-spatial and multi-temporal resolution analysis in the Addis Ababa city metropolitan area for assessing the relationship between UGS patterns and land surface temperature (LST). Landsat images were used to generate land cover and LST maps. Regression models were developed to investigate whether controlling for the proportion of the green area (PGS), fragmentation, shape, complexity, and proximity distance can affect surface temperature. Results indicated that the UGS patches with aggregated, regular and simple shapes and connectivity throughout the urban landscape were more effective in decreasing the LST as compared to the fragmented and complicated spatial patterns. This finding highlighted that in addition to increasing the amount of UGS, optimizing the spatial structure of UGS, could be an effective and useful action to mitigate the urban heat island (UHI) impacts. Changing the spatial size had a significant influence on the interconnection between LST and UGS patterns as well. It also noted that the spatial arrangement of UGS was more sensitive to spatial scales than that of its composition. The relationship between the spatial configuration of UGS and LST could be changed when applying different statistical methods. This result underlined the importance of controlling the effects of the share of green spaces when calculating the impacts of the spatial configuration of UGS on LST. Furthermore, the study highlighted that applying different statistical approaches, spatial scale, and coverage of UGS can help determine the effectiveness of the association between LST and UGS patterns. These outcomes provided new insights regarding the inconsistent findings from earlier studies, which might be a result of the different approaches considered. Indeed, these findings are expected to be of help more broadly for city planning and urban heat mitigation.

ACS Style

Berhanu Keno Terfa; Nengcheng Chen; Xiang Zhang; Dev Niyogi. Spatial Configuration and Extent Explains the Urban Heat Mitigation Potential due to Green Spaces: Analysis over Addis Ababa, Ethiopia. Remote Sensing 2020, 12, 2876 .

AMA Style

Berhanu Keno Terfa, Nengcheng Chen, Xiang Zhang, Dev Niyogi. Spatial Configuration and Extent Explains the Urban Heat Mitigation Potential due to Green Spaces: Analysis over Addis Ababa, Ethiopia. Remote Sensing. 2020; 12 (18):2876.

Chicago/Turabian Style

Berhanu Keno Terfa; Nengcheng Chen; Xiang Zhang; Dev Niyogi. 2020. "Spatial Configuration and Extent Explains the Urban Heat Mitigation Potential due to Green Spaces: Analysis over Addis Ababa, Ethiopia." Remote Sensing 12, no. 18: 2876.

Journal article
Published: 31 August 2020 in Sustainability
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Ecological environment evaluation is of great significance to achieve the Sustainable Development Goals (SDGs) and promote the harmonious development of economy, society, and environment. To evaluate environmental SDGs, single environmental indicators have been analyzed at national or large regional scale in some literature, while the urban integrated environment is ignored. Therefore, it is necessary to systematically and quantically evaluate the sustainability of ecological environment integrating the water, soil, and air environment at the urban scale. This study aims to construct the Integrated Perception Ecological Environment Indicator (IPEEI) based on the Driver-Pressure-State-Impact-Response (DPSIR) framework to solve the above-mentioned problems. The IPEEI model was proposed based on the three-level association mechanism of the Domain-Theme-Element, and the DPSIR framework conforming to the relevant standards for indicator determination. Moreover, the multi-dimensional, multi-thematic, and multi-urban quantitative evaluations were conducted using the entropy weight method, and the comprehensive evaluation grades by the Jenks natural breaks classification method of the geospatial analysis. Nine cities in the Wuhan Metropolitan Area were selected as the experimental areas. The results were consistent with the Ecological Index and local government’s planning and measures, which demonstrated that IPEEI can be effectively verified and applied for the evaluation of urban ecological environment sustainability.

ACS Style

Yingbing Liu; Wenying Du; Nengcheng Chen; Xiaolei Wang. Construction and Evaluation of the Integrated Perception Ecological Environment Indicator (IPEEI) Based on the DPSIR Framework for Smart Sustainable Cities. Sustainability 2020, 12, 7112 .

AMA Style

Yingbing Liu, Wenying Du, Nengcheng Chen, Xiaolei Wang. Construction and Evaluation of the Integrated Perception Ecological Environment Indicator (IPEEI) Based on the DPSIR Framework for Smart Sustainable Cities. Sustainability. 2020; 12 (17):7112.

Chicago/Turabian Style

Yingbing Liu; Wenying Du; Nengcheng Chen; Xiaolei Wang. 2020. "Construction and Evaluation of the Integrated Perception Ecological Environment Indicator (IPEEI) Based on the DPSIR Framework for Smart Sustainable Cities." Sustainability 12, no. 17: 7112.

Research article
Published: 26 August 2020 in International Journal of Climatology
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The Yangtze River Basin has periodically been subject to torrential rains and floods. It is of great significance to characterize the extreme precipitation patterns to learn about frequent flood characteristics in the Yangtze River Basin. Commonly, spatiotemporal characteristics of extreme precipitation was studied by regional frequency analysis method with site data. Spatial sparse site data may cause imprecise divisions of homogeneous regions. In this paper, the spatiotemporal characteristics of extreme precipitation was studied by regional frequency analysis with corrected satellite‐based grid precipitation data (Global Land Data Assimilation System, GLDAS) rather than site data. The results show that: 1) The corrected GLDAS daily precipitation data had greatly improved its ability to capture extreme precipitation events in Yangtze River Basin, as the data average accuracy increased from 0.215 before correction to 0.849 after correction. It is feasible to use satellite‐based grid precipitation data to replace the site data for the regional frequency analysis of extreme precipitation. 2) the Yangtze River Basin was categorized into seven homogeneous regions for the annual maximum 1‐day (RX1DAY) index with an automatic subjective adjustment method. 3) The regional growth curves and quantiles of the Yangtze River Basin were drawn for the return period for 2 to 100 years. 4) Spatial patterns of extreme daily precipitation series with a return period of 100 years indicated that the precipitation amount increases gradually from the upper to the lower Yangtze River Basin, from the “arid zone” to the “wet zone” and then to the “special wet zone”, and the 100‐year return level of RX1DAY varied from 30.3 to 301.8 mm. There were three main precipitation centers, the Sichuan Basin, Dongting Lake Basin and a great triangle area covering the Poyang Lake Basin and the south foot of Dabie Mountain. This article is protected by copyright. All rights reserved.

ACS Style

Zeqiang Chen; Yi Zeng; Gaoyun Shen; Changjiang Xiao; Lei Xu; Nengcheng Chen. Spatiotemporal characteristics and estimates of extreme precipitation in the Yangtze River Basin using GLDAS data. International Journal of Climatology 2020, 41, 1 .

AMA Style

Zeqiang Chen, Yi Zeng, Gaoyun Shen, Changjiang Xiao, Lei Xu, Nengcheng Chen. Spatiotemporal characteristics and estimates of extreme precipitation in the Yangtze River Basin using GLDAS data. International Journal of Climatology. 2020; 41 (S1):1.

Chicago/Turabian Style

Zeqiang Chen; Yi Zeng; Gaoyun Shen; Changjiang Xiao; Lei Xu; Nengcheng Chen. 2020. "Spatiotemporal characteristics and estimates of extreme precipitation in the Yangtze River Basin using GLDAS data." International Journal of Climatology 41, no. S1: 1.

Journal article
Published: 06 June 2020 in Science of The Total Environment
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The widespread occurrence of Cyanobacterial blooms (CABs) in inland waters is a typical and severe challenge for water resources management and environment protection. An accurate and spatially continuous risk assessment of CABs is critical for prediction and preparedness in advance. In this study, a multivariate integrated risk assessment (MIRA) method of CABs in inland waters was proposed. MIRA was simplified with the trophic levels, cyanobacterial and other aquatic plant condition using remote sensing indexes, including the Trophic State Index (TSI), Floating Algae Index (FAI) and Cyanobacteria and Macrophytes Index (CMI). First, the dates of risk assessment were carefully selected based on TSI. Then, we obtained the trophic levels, cyanobacterial, and other aquatic plant condition of water using TSI, CMI and FAI on the selected date, and further scored them pixel by pixel to quantify the risk value. Finally, the risk of CABs in water was accurately assessed based on the pixel risk value. Based on Landsat 8 OLI dataset, MIRA was executed and validated in three different lakes of Wuhan urban agglomeration (WUA) with different trophic states. The results demonstrated that the risk of CABs in Lake LongGan was overall higher than that in Lake LiangZi and Lake FuTou. And the risk of CABs in the east part of Lake LongGan was higher than the other parts. Seasonally, the risk level ranking in Lake LiangZi was the highest in summer, while lowest in winter. However, the seasonal risk ranking was spring, summer, autumn, and winter in Lake LongGan. Based on the comparisons with monthly water quality classification data and results of the existing study, including trophic level, ecology risk, and algal extent, the MIRA method was valuable for accurate and spatially continuous identifying the risk of CABs in inland waters with potential eutrophication trends.

ACS Style

Nengcheng Chen; Siqi Wang; Xiang Zhang; Shangbo Yang. A risk assessment method for remote sensing of cyanobacterial blooms in inland waters. Science of The Total Environment 2020, 740, 140012 .

AMA Style

Nengcheng Chen, Siqi Wang, Xiang Zhang, Shangbo Yang. A risk assessment method for remote sensing of cyanobacterial blooms in inland waters. Science of The Total Environment. 2020; 740 ():140012.

Chicago/Turabian Style

Nengcheng Chen; Siqi Wang; Xiang Zhang; Shangbo Yang. 2020. "A risk assessment method for remote sensing of cyanobacterial blooms in inland waters." Science of The Total Environment 740, no. : 140012.

Journal article
Published: 04 June 2020 in Remote Sensing
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Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE < 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

ACS Style

Phamchimai Phan; Nengcheng Chen; Lei Xu; Zeqiang Chen. Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam. Remote Sensing 2020, 12, 1814 .

AMA Style

Phamchimai Phan, Nengcheng Chen, Lei Xu, Zeqiang Chen. Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam. Remote Sensing. 2020; 12 (11):1814.

Chicago/Turabian Style

Phamchimai Phan; Nengcheng Chen; Lei Xu; Zeqiang Chen. 2020. "Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam." Remote Sensing 12, no. 11: 1814.

Journal article
Published: 04 May 2020 in Journal of Hydrology
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Drought impact is closely related to water deficit phases within the hydrological cycle, and to drought propagation (the complex evolutions from meteorological drought to hydrological drought and to soil moisture drought). Besides the qualitative description, the quantitative analysis of drought propagation is still limited so far. Therefore, in this study, the propagation from meteorological to hydrological and to soil moisture droughts have been quantitatively analyzed. In particular, run theory has been utilized for quantifying key drought features including its duration and magnitude. The two available land assimilation datasets from 1981–2018 were selected for comparison purpose in Northern China Plain. Our results showed that although drought events identified by two datasets were not the same, both datasets revealed that meteorological drought occurred more frequently than hydrological and soil moisture droughts. And both demonstrated meteorological drought, hydrological drought and soil moisture drought were not synchronous. In particular, more than 91.89% of meteorological droughts led to hydrological droughts, and more than 87.10% of hydrological droughts caused soil moisture droughts. Furthermore, linear models showed the best for drought duration and magnitude. We found when meteorological drought was prolonged or shortened by one month, more than 91.89% probability it would lead to the extension or shortening of hydrological drought by 0.992 months with 1.687 unit in magnitude. And if the duration of hydrological drought changed by one month, more than 80.56% probability of the soil moisture drought duration would change by 1.006 months with 0.992 unit in magnitude. The results of duration and magnitude fitted well across the whole study area. Building on the above drought evolution information, practical drought mitigation measures and early warning system could be established.

ACS Style

Nengcheng Chen; Ronghui Li; Xiang Zhang; Chao Yang; Xiaoping Wang; Linglin Zeng; Shengjun Tang; Wei Wang; Deren Li; Dev Niyogi. Drought propagation in Northern China Plain: A comparative analysis of GLDAS and MERRA-2 datasets. Journal of Hydrology 2020, 588, 125026 .

AMA Style

Nengcheng Chen, Ronghui Li, Xiang Zhang, Chao Yang, Xiaoping Wang, Linglin Zeng, Shengjun Tang, Wei Wang, Deren Li, Dev Niyogi. Drought propagation in Northern China Plain: A comparative analysis of GLDAS and MERRA-2 datasets. Journal of Hydrology. 2020; 588 ():125026.

Chicago/Turabian Style

Nengcheng Chen; Ronghui Li; Xiang Zhang; Chao Yang; Xiaoping Wang; Linglin Zeng; Shengjun Tang; Wei Wang; Deren Li; Dev Niyogi. 2020. "Drought propagation in Northern China Plain: A comparative analysis of GLDAS and MERRA-2 datasets." Journal of Hydrology 588, no. : 125026.

Journal article
Published: 27 April 2020 in Remote Sensing
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In the late 1990s, the exotic plant Spartina alterniflora (S. alterniflora), was introduced to the Zhangjiang Estuary of China for tidal zone reclamation and protection. However, it invaded rapidly and has caused serious ecological problems. Accurate information on the seasonal invasion of S. alterniflora is vital to understand invasion pattern and mechanism, especially at a high temporal resolution. This study aimed to explore the S. alterniflora invasion process at a seasonal scale from 2016 to 2018. However, due to the uncertainties caused by periodic inundation of local tides, accurately monitoring the spatial extent of S. alterniflora is challenging. Thus, to achieve the goal and address the challenge, we firstly built a high-quality seasonal Sentinel-2 image collection by developing a new submerged S. alterniflora index (SAI) to reduce the errors caused by high tide fluctuations. Then, an object-based random forest (RF) classification method was applied to the image collection. Finally, seasonal extents of S. alterniflora were captured. Results showed that (1) the red edge bands (bands 5, 6, and 7) of Sentinel-2 imagery played critical roles in delineating submerged S. alterniflora; (2) during March 2016 to November 2018, the extent of S. alterniflora increased from 151.7 to 270.3 ha, with an annual invasion rate of 39.5 ha; (3) S. alterniflora invaded with a rate of 31.5 ha/season during growing season and 12.1 ha/season during dormant season. To our knowledge, this is the first study monitoring S. alterniflora invasion process at a seasonal scale during continuous years, discovering that S. alterniflora also expands during dormant seasons. This discovery is of great significance for understanding the invasion pattern and mechanism of S. alterniflora and will facilitate coastal biodiversity conservation efforts.

ACS Style

Yanlin Tian; Mingming Jia; Zongming Wang; Dehua Mao; Baojia Du; Chao Wang. Monitoring Invasion Process of Spartina alterniflora by Seasonal Sentinel-2 Imagery and an Object-Based Random Forest Classification. Remote Sensing 2020, 12, 1383 .

AMA Style

Yanlin Tian, Mingming Jia, Zongming Wang, Dehua Mao, Baojia Du, Chao Wang. Monitoring Invasion Process of Spartina alterniflora by Seasonal Sentinel-2 Imagery and an Object-Based Random Forest Classification. Remote Sensing. 2020; 12 (9):1383.

Chicago/Turabian Style

Yanlin Tian; Mingming Jia; Zongming Wang; Dehua Mao; Baojia Du; Chao Wang. 2020. "Monitoring Invasion Process of Spartina alterniflora by Seasonal Sentinel-2 Imagery and an Object-Based Random Forest Classification." Remote Sensing 12, no. 9: 1383.

Journal article
Published: 29 March 2020 in Remote Sensing
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Modeling the relationship between precipitation and water level is of great significance in the prevention of flood disaster. In recent years, the use of machine learning algorithms for precipitation–water level prediction has attracted wide attention in flood forecasting and other fields; however, a clear method to model the relationship of precipitation and water level using grid precipitation products with a neural network model is lacking. The issues of the method include how to select a neural network model, as well as how to influence the modeling results with different types and resolutions of remote sensing data. The purpose of this paper is to provide some findings for the issues. We used the back-propagation (BP) neural network and a nonlinear autoregressive exogenous model (NARX) time series network to model the relationship between precipitation and water level, respectively. The water level of Pingshan hydrographic station at a catchment area in the Jinsha River Basin was simulated by the two network models using three different grid precipitation products. The results showed that when the ground station data are missing, the grid precipitation product is a good alternative to construct the precipitation–water level relationship. In addition, using the NARX network as a model fitting network using extra inputs was better than using the BP neural network; the Nash efficiency coefficients of the former were all higher than 97%, while the latter were all lower than 94%. Furthermore, the input of grid products with different spatial resolutions has little significant effect on the modeling results of the model.

ACS Style

Zeqiang Chen; Xin Lin; Chang Xiong; Nengcheng Chen. Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model. Remote Sensing 2020, 12, 1096 .

AMA Style

Zeqiang Chen, Xin Lin, Chang Xiong, Nengcheng Chen. Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model. Remote Sensing. 2020; 12 (7):1096.

Chicago/Turabian Style

Zeqiang Chen; Xin Lin; Chang Xiong; Nengcheng Chen. 2020. "Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model." Remote Sensing 12, no. 7: 1096.

Journal article
Published: 11 March 2020 in Water Resources Research
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ACS Style

Lei Xu; Nengcheng Chen; Hamid Moradkhani; Xiang Zhang; Chuli Hu. Improving Global Monthly and Daily Precipitation Estimation by Fusing Gauge Observations, Remote Sensing, and Reanalysis Data Sets. Water Resources Research 2020, 56, 1 .

AMA Style

Lei Xu, Nengcheng Chen, Hamid Moradkhani, Xiang Zhang, Chuli Hu. Improving Global Monthly and Daily Precipitation Estimation by Fusing Gauge Observations, Remote Sensing, and Reanalysis Data Sets. Water Resources Research. 2020; 56 (3):1.

Chicago/Turabian Style

Lei Xu; Nengcheng Chen; Hamid Moradkhani; Xiang Zhang; Chuli Hu. 2020. "Improving Global Monthly and Daily Precipitation Estimation by Fusing Gauge Observations, Remote Sensing, and Reanalysis Data Sets." Water Resources Research 56, no. 3: 1.

Research article
Published: 09 March 2020 in Advances in Meteorology
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Downscaling considerably alleviates the drawbacks of regional climate simulation by general circulation models (GCMs). However, little information is available regarding the downscaling using machine learning methods, specifically at hydrological basin scale. This study developed multiple machine learning (ML) downscaling models, based on a Bayesian model average (BMA), to downscale the precipitation simulation of 8 Coupled Model Intercomparison Project Phase 5 (CMIP5) models using model output statistics (MOS) for the years 1961–2005 in the upper Han River basin. A series of statistical metrics, including Pearson’s correlation coefficient (PCC), root mean squared error (RMSE), and relative bias (Rbias), were used for evaluation and comparative analyses. Moreover, the BMA and the best ML downscaling model were used to downscale precipitation in the 21st century under Representative Concentration Pathway 4.5 (RCP4.5) and RCP8.5 scenarios. The results show the following: (1) The performance of the BMA ensemble simulation is clearly better than that of the individual models and the simple mean model ensemble (MME). The PCC reaches 0.74, and the RMSE is reduced by 28%–60% for all the GCMs and 33% compared to the MME. (2) The downscaled models greatly improved station simulation performance. Support vector machine for regression (SVR) was superior to multilayer perceptron (MLP) and random forest (RF). The downscaling results based on the BMA ensemble simulation and SVR models were regarded as the best performing overall (PCC, RMSE, and Rbias were 0.82, 35.07, mm and −5.45%, respectively). (3) Based on BMA and SVR models, the projected precipitations show a weak increasing trend on the whole under RCP4.5 and RCP8.5. Specifically, the average rainfall during the mid- (2040–2069) and late (2070–2099) 21st century increased by 3.23% and 1.02%, respectively, compared to the base year (1971–2000) under RCP4.5, while they increased by 4.25% and 8.30% under RCP8.5. Additionally, the magnitude of changes during winter and spring was higher than that during summer and autumn. Furthermore, future work is recommended to study the improvement of downscaling models and the effect of local climate.

ACS Style

Ren Xu; Nengcheng Chen; Yumin Chen; Zeqiang Chen. Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin. Advances in Meteorology 2020, 2020, 1 -17.

AMA Style

Ren Xu, Nengcheng Chen, Yumin Chen, Zeqiang Chen. Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin. Advances in Meteorology. 2020; 2020 ():1-17.

Chicago/Turabian Style

Ren Xu; Nengcheng Chen; Yumin Chen; Zeqiang Chen. 2020. "Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin." Advances in Meteorology 2020, no. : 1-17.

Journal article
Published: 05 March 2020 in Water
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The increasing deterioration of aquatic environments has attracted more attention to water quality monitoring techniques, with most researchers focusing on the acquisition and assessment of water quality data, but seldom on the discovery and tracing of pollution sources. In this study, a semantic-enhanced modeling method for ontology modeling and rules building is proposed, which can be used for river water quality monitoring and relevant data observation processing. The observational process ontology (OPO) method can describe the semantic properties of water resources and observation data. In addition, it can provide the semantic relevance among the different concepts involved in the observational process of water quality monitoring. A pollution alert can be achieved using the reasoning rules for the water quality monitoring stations. In this study, a case is made for the usability testing of the OPO models and reasoning rules by utilizing a water quality monitoring system. The system contributes to the water quality observational monitoring process and traces the source of pollutants using sensors, observation data, process models, and observation products that users can access in a timely manner.

ACS Style

Xiaolei Wang; Haitao Wei; Nengcheng Chen; Xiaohui He; Zhihui Tian. An Observational Process Ontology-Based Modeling Approach for Water Quality Monitoring. Water 2020, 12, 715 .

AMA Style

Xiaolei Wang, Haitao Wei, Nengcheng Chen, Xiaohui He, Zhihui Tian. An Observational Process Ontology-Based Modeling Approach for Water Quality Monitoring. Water. 2020; 12 (3):715.

Chicago/Turabian Style

Xiaolei Wang; Haitao Wei; Nengcheng Chen; Xiaohui He; Zhihui Tian. 2020. "An Observational Process Ontology-Based Modeling Approach for Water Quality Monitoring." Water 12, no. 3: 715.

Journal article
Published: 08 February 2020 in Sustainability
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Fundamental ideas concerning urbanization are primarily based on studies performed in large cities. It is of interest to study whether or not similar phenomena take place in smaller cities. Small cities are an inherent component of urbanization, and in the future, the majority of globalization is expected to occur in small and mid-sized cities. Understanding the effects of small cities on landscape structures is, therefore, an essential component in planning city land expansion. Accordingly, this study focused on six towns of the Oromia Special Zone Surrounding Finfinnee, Ethiopia, which is broadly known to be experiencing dramatic growth. Time-series Landsat images from 1987 to 2019 with an integrated method, landscape metrics, and built-up density analysis were employed to characterize and compare the dynamics of landscape structures, urban expansion patterns, process, and overall growth status in the towns. The results highlight that all the towns experienced accelerated growth in the built-up areas and highly scattered nature in spatial growth. Landscape ecology analysis confirmed a highly fragmented urban landscape, a significant loss of natural land covers, and disconnected and complicated agro-vegetation patches in all towns, suggesting a lack of rigorous implementation of the master plan. Results also indicated that the Oromia Special Zone surrounding Finfinnee has failed to control urban sprawl to surrounding ecological sensitive areas. The study results, more broadly, highlight that the small cities would have a limited physical and demographic footprint and relatively less contribution to the national economic agglomeration; nonetheless, they can have a notable and important impact in terms of their ecological and environmental influence. Hence, the study suggests policies for monitoring such dynamics and protecting agro-environmental connectivity with particular focus on the small cities.

ACS Style

Berhanu Keno Terfa; Nengcheng Chen; Xiang Zhang; Dev Niyogi. Urbanization in Small Cities and Their Significant Implications on Landscape Structures: The Case in Ethiopia. Sustainability 2020, 12, 1235 .

AMA Style

Berhanu Keno Terfa, Nengcheng Chen, Xiang Zhang, Dev Niyogi. Urbanization in Small Cities and Their Significant Implications on Landscape Structures: The Case in Ethiopia. Sustainability. 2020; 12 (3):1235.

Chicago/Turabian Style

Berhanu Keno Terfa; Nengcheng Chen; Xiang Zhang; Dev Niyogi. 2020. "Urbanization in Small Cities and Their Significant Implications on Landscape Structures: The Case in Ethiopia." Sustainability 12, no. 3: 1235.

Erratum
Published: 31 January 2020 in ISPRS Journal of Photogrammetry and Remote Sensing
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ACS Style

Dandan Liu; Nengcheng Chen; Xiang Zhang; Chao Wang; Wenying Du. Corrigendum to “Annual large-scale urban land mapping based on Landsat time series in Google Earth Engine and OpenStreetMap data: A case study in the middle Yangtze River basin” [ISPRS J. Photogram. Rem. Sens. 159 (2020) 337–351]. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 161, 263 .

AMA Style

Dandan Liu, Nengcheng Chen, Xiang Zhang, Chao Wang, Wenying Du. Corrigendum to “Annual large-scale urban land mapping based on Landsat time series in Google Earth Engine and OpenStreetMap data: A case study in the middle Yangtze River basin” [ISPRS J. Photogram. Rem. Sens. 159 (2020) 337–351]. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 161 ():263.

Chicago/Turabian Style

Dandan Liu; Nengcheng Chen; Xiang Zhang; Chao Wang; Wenying Du. 2020. "Corrigendum to “Annual large-scale urban land mapping based on Landsat time series in Google Earth Engine and OpenStreetMap data: A case study in the middle Yangtze River basin” [ISPRS J. Photogram. Rem. Sens. 159 (2020) 337–351]." ISPRS Journal of Photogrammetry and Remote Sensing 161, no. : 263.