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Jianhui Xu
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China

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Journal article
Published: 20 July 2021 in Remote Sensing
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This study examined the impact of different types of building roofs on urban heat islands. This was carried out using building roof data from remotely sensed Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) imagery. The roofs captured included white surface, blue steel, dark metal, other dark material, and residential roofs; these roofs were compared alongside three natural land covers (i.e., forest trees, grassland, and water). We also collected ancillary data including building height, building density, and distance to the city center. The impacts of various building roofs on land surface temperature (LST) were examined by analyzing their correlation and temporal variations. First, we examined the LST characteristics of five building roof types and three natural land covers using boxplots and variance analysis with post hoc tests. Then, multivariate regression analysis was used to explore the impact of building roofs on LST. There were three key findings in the results. First, the mean LSTs for five different building roofs statistically differed from each other; these differences were more significant during the hot season than the cool season. Second, the impact of the five types of roofs on LSTs varied considerably from each other. Lastly, the contribution of the five roof types to LST variance was more substantial during the cool season. These findings unveil specific urban heat retention drivers, in which different types of building roofs are one such driver. The outcomes from this research may help policymakers develop more effective strategies to address the surface urban heat island phenomenon and its related health concerns.

ACS Style

Yingbin Deng; Renrong Chen; Yichun Xie; Jianhui Xu; Ji Yang; Wenyue Liao. Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island. Remote Sensing 2021, 13, 2840 .

AMA Style

Yingbin Deng, Renrong Chen, Yichun Xie, Jianhui Xu, Ji Yang, Wenyue Liao. Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island. Remote Sensing. 2021; 13 (14):2840.

Chicago/Turabian Style

Yingbin Deng; Renrong Chen; Yichun Xie; Jianhui Xu; Ji Yang; Wenyue Liao. 2021. "Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island." Remote Sensing 13, no. 14: 2840.

Journal article
Published: 30 March 2021 in Water
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Accurate waterbody mapping can support water-related environment monitoring and resource management. The Sentinel series satellites provide high-quality Synthetic Aperture Radar (SAR) and optical observations that are commonly used in waterbody mapping. However, owing to the 10-m spatial resolution of Sentinel data, previous studies mostly focused on the mapping of large waterbodies. In this work, we evaluated the performance of small waterbody mapping over urban and mountainous regions with two datasets, the average annual VH backscatter coefficients (VHavg), derived from the Sentinel-1A series, and the Modified Normalized Difference Water Index (MNDWI), derived from cloud-free Sentinel-2. A proven framework of waterbody mapping based on watershed segmentation and noise reduction was employed to assess the performance of the two datasets in waterbody identification. The validation was performed by comparing their results with 1-m spatial resolution reference waterbody data. Assessment metrics, including Precision, Recall, and F-measure, were employed. Results showed that: (1) the MNDWI outperformed the VHavg by 9 percentage points of the F-measure; (2) there was more room for results of VHavg to improve the accuracy through a combination with noise reduction; and (3) the potential smallest identifiable waterbody area (recall rate larger than 0.8) was larger than 104 m2.

ACS Style

Hao Jiang; Mo Wang; Hongda Hu; Jianhui Xu. Evaluating the Performance of Sentinel-1A and Sentinel-2 in Small Waterbody Mapping over Urban and Mountainous Regions. Water 2021, 13, 945 .

AMA Style

Hao Jiang, Mo Wang, Hongda Hu, Jianhui Xu. Evaluating the Performance of Sentinel-1A and Sentinel-2 in Small Waterbody Mapping over Urban and Mountainous Regions. Water. 2021; 13 (7):945.

Chicago/Turabian Style

Hao Jiang; Mo Wang; Hongda Hu; Jianhui Xu. 2021. "Evaluating the Performance of Sentinel-1A and Sentinel-2 in Small Waterbody Mapping over Urban and Mountainous Regions." Water 13, no. 7: 945.

Journal article
Published: 08 March 2021 in Remote Sensing
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This study explored the model of urban impervious surface (IS) density, land surface temperature (LST), and comprehensive ecological evaluation index (CEEI) from urban centers to suburbs. The interrelationships between these parameters in Guangzhou from 1987 to 2019 were analyzed using time-series Landsat-5 TM (Thematic Mapper), Landsat-8 OLI (Operational Land Imager), and TIRS (Thermal Infrared Sensor) images. The urban IS densities were calculated in concentric rings using time-series IS fractions, which were used to construct an inverse S-shaped urban IS density function to depict changes in urban form and the spatio-temporal dynamics of urban expansion from the urban center to the suburbs. The results indicated that Guangzhou experienced expansive urban growth, with the patterns of urban spatial structure changing from a single-center to a multi-center structure over the past 32 years. Next, the normalized LST and CEEI in each concentric ring were calculated, and their variation trends from the urban center to the suburbs were modeled using linear and nonlinear functions, respectively. The results showed that the normalized LST had a gradual decreasing trend from the urban center to the suburbs, while the CEEI showed a significant increasing trend. During the 32-year rapid urban development, the normalized LST difference between the urban center and suburbs increased gradually with time, and the CEEI significantly decreased. This indicated that rapid urbanization significantly expanded the impervious surface areas in Guangzhou, leading to an increase in the LST difference between urban centers and suburbs and a deterioration in ecological quality. Finally, the potential interrelationships among urban IS density, normalized LST, and CEEI were also explored using different models. This study revealed that rapid urbanization has produced geographical convergence between several ISs, which may increase the risk of the urban heat island effect and degradation of ecological quality.

ACS Style

Jianhui Xu; Yi Zhao; Caige Sun; Hanbin Liang; Ji Yang; Kaiwen Zhong; Yong Li; Xulong Liu. Exploring the Variation Trend of Urban Expansion, Land Surface Temperature, and Ecological Quality and Their Interrelationships in Guangzhou, China, from 1987 to 2019. Remote Sensing 2021, 13, 1019 .

AMA Style

Jianhui Xu, Yi Zhao, Caige Sun, Hanbin Liang, Ji Yang, Kaiwen Zhong, Yong Li, Xulong Liu. Exploring the Variation Trend of Urban Expansion, Land Surface Temperature, and Ecological Quality and Their Interrelationships in Guangzhou, China, from 1987 to 2019. Remote Sensing. 2021; 13 (5):1019.

Chicago/Turabian Style

Jianhui Xu; Yi Zhao; Caige Sun; Hanbin Liang; Ji Yang; Kaiwen Zhong; Yong Li; Xulong Liu. 2021. "Exploring the Variation Trend of Urban Expansion, Land Surface Temperature, and Ecological Quality and Their Interrelationships in Guangzhou, China, from 1987 to 2019." Remote Sensing 13, no. 5: 1019.

Journal article
Published: 13 February 2021 in Land
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The interaction between urbanization and the eco-environment is usually viewed as an effect–feedback framework. Its coupling system is composed of urbanization and eco-environment subsystems. In this paper, the coupling degree (CD) and the coupling coordinated degree (CCD) are used to reflect the coupling interaction and coupling coordination between the urbanization subsystem and the eco-environment subsystem. Based on the dynamic relative quantities of urbanization and eco-environment data in the Pearl River Delta, CD and CCD values were calculated, and the spatiotemporal evolution trend of coordination was analyzed. The results show that (1) from 2000 to 2015, the nine cities in the Pearl River Delta had high CD values and CCD values. Though they had different performances in different periods, they were all in a coordinated class, including good coordination (GC), moderate coordination (MC), and bare coordination (BC). (2) In terms of temporal evolution, the coupling coordination between urbanization and the eco-environment in the entire Pearl River Delta greatly improved. (3) From the perspective of spatial distribution, the coupling coordination of the central region was higher than that of the peripheral regions, and that of the west bank of the Pearl River was higher than that of the east bank of the Pearl River. These results can help local policy makers enact appropriate measures for sustainable development.

ACS Style

Caige Sun; Shengyong Zhang; Chuncheng Song; Jianhui Xu; Fenglei Fan. Investigation of Dynamic Coupling Coordination between Urbanization and the Eco-Environment—A Case Study in the Pearl River Delta Area. Land 2021, 10, 190 .

AMA Style

Caige Sun, Shengyong Zhang, Chuncheng Song, Jianhui Xu, Fenglei Fan. Investigation of Dynamic Coupling Coordination between Urbanization and the Eco-Environment—A Case Study in the Pearl River Delta Area. Land. 2021; 10 (2):190.

Chicago/Turabian Style

Caige Sun; Shengyong Zhang; Chuncheng Song; Jianhui Xu; Fenglei Fan. 2021. "Investigation of Dynamic Coupling Coordination between Urbanization and the Eco-Environment—A Case Study in the Pearl River Delta Area." Land 10, no. 2: 190.

Journal article
Published: 28 January 2021 in Remote Sensing
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Impervious surfaces (IS), the most common land cover in urban areas, not only provide convenience to the city, but also exert significant negative environmental impacts, thereby affecting the ecological environment carrying capacity of urban agglomerations. Most of the current research considers IS as a single land-cover type, yet this does not fully reflect the complex physical characteristics of various IS types. Therefore, limited information for urban micro-ecology and urban fine management can be provided through one IS land-cover type. This study proposed a finer IS classification scheme and mapped the detailed IS fraction in Guangzhou City, China using Landsat imagery. The IS type was divided into seven finer classes, including blue steel, cement, asphalt, other impervious surface, and other metal, brick, and plastic. Classification results demonstrate that finer IS can be well extracted from the Landsat imagery as all root mean square errors (RMSE) are less than 15%. Specially, the accuracies of asphalt, plastic, and cement are better than other finer IS types with the RMSEs of 7.99%, 8.48%, and 9.92%, respectively. Quantitative analyses illustrate that asphalt, other impervious surface, and brick are the dominant IS types in the study area with the percentages of 9.68%, 6.27%, and 4.45%, respectively, and they are mainly located in Yuexiu, Liwan, Haizhu, and Panyu districts. These results are valuable for research into urban fine management and can support the detailed analysis of urban micro-ecology.

ACS Style

Wenyue Liao; Yingbin Deng; Miao Li; Meiwei Sun; Ji Yang; Jianhui Xu. Extraction and Analysis of Finer Impervious Surface Classes in Urban Area. Remote Sensing 2021, 13, 459 .

AMA Style

Wenyue Liao, Yingbin Deng, Miao Li, Meiwei Sun, Ji Yang, Jianhui Xu. Extraction and Analysis of Finer Impervious Surface Classes in Urban Area. Remote Sensing. 2021; 13 (3):459.

Chicago/Turabian Style

Wenyue Liao; Yingbin Deng; Miao Li; Meiwei Sun; Ji Yang; Jianhui Xu. 2021. "Extraction and Analysis of Finer Impervious Surface Classes in Urban Area." Remote Sensing 13, no. 3: 459.

Article
Published: 27 October 2020 in Journal of Geovisualization and Spatial Analysis
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Volunteered geographic information (VGI) has been widely explored by researchers for decision support in various application domains because the data are cost-effective to collect and their richness in volume and spatiotemporal coverage is unrivaled against traditional data sources. This study visualizes and analyzes a network of the authors of selected journal articles in GIScience about the first decade of VGI research. It uses the number of citations, one local network centrality measures (i.e., degree), and three global network centrality measures (i.e., closeness centrality, betweenness centrality, and eigenvector centrality) for quantifying the author importance. A new rule-based weighting method has also been developed for taking into account author sequences when computing the global centrality measures. Results show that the connectedness of the European researchers is strong, and Europe and North America have the highest numbers of prominent VGI researchers. Closeness among researchers does not seem to contribute heavily to the increase in citations. Rather, the number of direct connections in the network, the authors’ control over the network, and the quality of research connections is more important. European and North American authors as a whole play a leading role in the VGI research, but on average (per author influence) are only outstanding in terms of the citation numbers and have relatively more control over the network. Lastly, this study has revealed the relatively more diverse VGI research topics investigated over a longer time span in North America and Europe compared with other regions of the globe, highlighting the major problems that have been studied across the VGI research network.

ACS Style

Yingwei Yan; Dawei Ma; Wei Huang; Chen-Chieh Feng; Hongchao Fan; Yingbin Deng; Jianhui Xu. Volunteered Geographic Information Research in the First Decade: Visualizing and Analyzing the Author Connectedness of Selected Journal Articles in GIScience. Journal of Geovisualization and Spatial Analysis 2020, 4, 1 -13.

AMA Style

Yingwei Yan, Dawei Ma, Wei Huang, Chen-Chieh Feng, Hongchao Fan, Yingbin Deng, Jianhui Xu. Volunteered Geographic Information Research in the First Decade: Visualizing and Analyzing the Author Connectedness of Selected Journal Articles in GIScience. Journal of Geovisualization and Spatial Analysis. 2020; 4 (2):1-13.

Chicago/Turabian Style

Yingwei Yan; Dawei Ma; Wei Huang; Chen-Chieh Feng; Hongchao Fan; Yingbin Deng; Jianhui Xu. 2020. "Volunteered Geographic Information Research in the First Decade: Visualizing and Analyzing the Author Connectedness of Selected Journal Articles in GIScience." Journal of Geovisualization and Spatial Analysis 4, no. 2: 1-13.

Journal article
Published: 29 June 2020 in Journal of Hydrology
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The changes of terrestrial water storage (TWS) is critical for drought monitoring, water and food security, global water cycle and climate change studies. Currently, the Gravity Recovery and Climate Experiment (GRACE) twin satellites are unique means of observing large-scale water storage variations, but the short time series (2002-present) limits their applications in long term climatic and hydrologic studies. Although the TWS can be calculated from global land surface models, large uncertainties arise due to uncertainties of inputs and the limitations of the models. This study developed a reconstruction model for GRACE TWS anomalies (TWSA) based on the Global Land Data Assimilation System (GLDAS) model outputs by using a Random Forest (RF) regression approach. A Spatially Moving Window (SMW) structure was introduced when training the RF model to address the spatial variations of TWSA, and a linear regression approach (LR) was also used for comparison purpose. Long-term TWSA over China land area were generated based on the proposed approaches and results were validated through cross-validation and comparisons with reference datasets. As a result, the RF-based model outperforms the LR-based model, and the reconstructed TWSA by using the two models both well reproduce GRACE dataset and outperform the TWSA that are derived directly from GLDAS models. Moreover, the TWSA produced by using the presented models have good agreements with another global GRACE-based reconstructed TWSA dataset and in-situ soil moisture measurements. Importance value for each variable in the RF model was quantified as well as the spatial coefficients for each variable in the LR model. The importance values and regression coefficients present varying spatial patterns. Rather than modifying the land surface model structure and inputs, this study provides alternative ways of improving the TWS estimations of GLDAS and extending time range of GRACE datasets. The experiments are expected to promote and enrich the methodologies and theories of combining physical and statistical models for optimal simulations in geoscientific research.

ACS Style

Wenlong Jing; Pengyan Zhang; Xiaodan Zhao; Yaping Yang; Hao Jiang; Jianhui Xu; Ji Yang; Yong Li. Extending GRACE terrestrial water storage anomalies by combining the random forest regression and a spatially moving window structure. Journal of Hydrology 2020, 590, 125239 .

AMA Style

Wenlong Jing, Pengyan Zhang, Xiaodan Zhao, Yaping Yang, Hao Jiang, Jianhui Xu, Ji Yang, Yong Li. Extending GRACE terrestrial water storage anomalies by combining the random forest regression and a spatially moving window structure. Journal of Hydrology. 2020; 590 ():125239.

Chicago/Turabian Style

Wenlong Jing; Pengyan Zhang; Xiaodan Zhao; Yaping Yang; Hao Jiang; Jianhui Xu; Ji Yang; Yong Li. 2020. "Extending GRACE terrestrial water storage anomalies by combining the random forest regression and a spatially moving window structure." Journal of Hydrology 590, no. : 125239.

Journal article
Published: 02 June 2020 in ISPRS International Journal of Geo-Information
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Walking is one of the most commonly promoted traveling methods and is garnering increasing attention. Many indices/scores have been developed by scholars to measure the walkability in a local community. However, most existing walking indices/scores involve urban planning-oriented, local service-oriented, regional accessibility-oriented, and physical activity-oriented walkability assessments. Since shopping and dining are two major leisure activities in our daily lives, more attention should be given to the shopping or dining-oriented walking environment. Therefore, we developed two additional walking indices that focus on shopping or dining. The point of interest (POI), vegetation coverage, water coverage, distance to bus/subway station, and land surface temperature were employed to construct walking indices based on 50-m street segments. Then, walking index values were categorized into seven recommendation levels. The field verification illustrates that the proposed walking indices can accurately represent the walking environment for shopping and dining. The results in this study could provide references for citizens seeking to engage in activities of shopping and dining with a good walking environment.

ACS Style

Yingbin Deng; Yingwei Yan; Yichun Xie; Jianhui Xu; Hao Jiang; Renrong Chen; Runnan Tan. Developing Shopping and Dining Walking Indices Using POIs and Remote Sensing Data. ISPRS International Journal of Geo-Information 2020, 9, 366 .

AMA Style

Yingbin Deng, Yingwei Yan, Yichun Xie, Jianhui Xu, Hao Jiang, Renrong Chen, Runnan Tan. Developing Shopping and Dining Walking Indices Using POIs and Remote Sensing Data. ISPRS International Journal of Geo-Information. 2020; 9 (6):366.

Chicago/Turabian Style

Yingbin Deng; Yingwei Yan; Yichun Xie; Jianhui Xu; Hao Jiang; Renrong Chen; Runnan Tan. 2020. "Developing Shopping and Dining Walking Indices Using POIs and Remote Sensing Data." ISPRS International Journal of Geo-Information 9, no. 6: 366.

Research article
Published: 08 April 2020 in Hydrological Processes
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ACS Style

Yan Liu; Jianhui Xu; Xinyu Lu; Lei Nie. Assessment of glacier‐ and snowmelt‐driven streamflow in the arid middle Tianshan Mountains of China. Hydrological Processes 2020, 34, 2750 -2762.

AMA Style

Yan Liu, Jianhui Xu, Xinyu Lu, Lei Nie. Assessment of glacier‐ and snowmelt‐driven streamflow in the arid middle Tianshan Mountains of China. Hydrological Processes. 2020; 34 (12):2750-2762.

Chicago/Turabian Style

Yan Liu; Jianhui Xu; Xinyu Lu; Lei Nie. 2020. "Assessment of glacier‐ and snowmelt‐driven streamflow in the arid middle Tianshan Mountains of China." Hydrological Processes 34, no. 12: 2750-2762.

Journal article
Published: 27 March 2020 in Remote Sensing
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Land surface temperature (LST) is a vital physical parameter of earth surface system. Estimating high-resolution LST precisely is essential to understand heat change processes in urban environments. Existing LST products with coarse spatial resolution retrieved from satellite-based thermal infrared imagery have limited use in the detailed study of surface energy balance, evapotranspiration, and climatic change at the urban spatial scale. Downscaling LST is a practicable approach to obtain high accuracy and high-resolution LST. In this study, a machine learning-based geostatistical downscaling method (RFATPK) is proposed for downscaling LST which integrates the advantages of random forests and area-to-point Kriging methods. The RFATPK was performed to downscale the 90 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST 10 m over two representative areas in Guangzhou, China. The 10 m multi-type independent variables derived from the Sentinel-2A imagery on 1 November 2017, were incorporated into the RFATPK, which considered the nonlinear relationship between LST and independent variables and the scale effect of the regression residual LST. The downscaled results were further compared with the results obtained from the normalized difference vegetation index (NDVI) based thermal sharpening method (TsHARP). The experimental results showed that the RFATPK produced 10 m LST with higher accuracy than the TsHARP; the TsHARP showed poor performance when downscaling LST in the built-up and water regions because NDVI is a poor indicator for impervious surfaces and water bodies; the RFATPK captured LST difference over different land coverage patterns and produced the spatial details of downscaled LST on heterogeneous regions. More accurate LST data has wide applications in meteorological, hydrological, and ecological research and urban heat island monitoring.

ACS Style

Jianhui Xu; Feifei Zhang; Hao Jiang; Hongda Hu; Kaiwen Zhong; Wenlong Jing; Ji Yang; Binghao Jia. Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging. Remote Sensing 2020, 12, 1082 .

AMA Style

Jianhui Xu, Feifei Zhang, Hao Jiang, Hongda Hu, Kaiwen Zhong, Wenlong Jing, Ji Yang, Binghao Jia. Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging. Remote Sensing. 2020; 12 (7):1082.

Chicago/Turabian Style

Jianhui Xu; Feifei Zhang; Hao Jiang; Hongda Hu; Kaiwen Zhong; Wenlong Jing; Ji Yang; Binghao Jia. 2020. "Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging." Remote Sensing 12, no. 7: 1082.

Journal article
Published: 21 May 2019 in Water
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Understanding the snow accumulation and melting process is of great significance for the assessment and regulation of water resources and the prevention of meltwater flooding, especially for the semiarid region in the Manas River Basin. However, the lack of long snow measurement time series in this semiarid region prevents a full understanding of the detailed local-scale snow ablation process. Additionally, the modeling of snow accumulation and melting is challenging due to parameter uncertainty. In this study, the snow ablation process in the Manas River Basin was quantitatively explored with long time-series of 3-h measurements of snow depth, snow density and snow water equivalent (SWE) at the Wulanwusu (WLWS), Hanqiazi (HQZ), and Baiyanggou (BYG) sites. This study explored the ability of the Utah energy balance (UEB) snow accumulation and melt model to simulate SWE, energy flux and water loss in the study area. Furthermore, the uncertainty in the ground surface aerodynamic roughness index zos in the UEB model was also analyzed. The results showed that: (1) noticeable variations in snow depth, SWE and snow density occurred on seasonal and interannual time scales, and variations in melting time and melting ratios occurred on short time scales; (2) a rapid decrease in snow depth did not influence the variations in SWE, and snow melting occurred during all time periods, even winter, which is a typical characteristic of snow accumulation in arid environments; (3) the UEB model accurately simulated the snow ablation processes, including SWE, snow surface temperature, and energy flux, at WLWS, HQZ, and BYG sites; (4) the lowest contribution of net radiation to melting occurred in the piedmont clinoplain, followed by the mountain desert grassland belt and mountain forest belt, whereas the contributions of net turbulence exhibited the opposite pattern; (5) the optimal zos in the UEB model was experimentally determined to be 0.01 m, and the UEB model-simulated SWE based on this value was the most consistent with the measured SWE; and (6) the results may provide theoretical and data foundations for research on the snow accumulation process at the watershed scale.

ACS Style

Yan Liu; Pu Zhang; Lei Nie; Jianhui Xu; Xinyu Lu; Shuai Li; Liu; Nie; Xu; Lu; Li. Exploration of the Snow Ablation Process in the Semiarid Region in China by Combining Site-Based Measurements and the Utah Energy Balance Model—A Case Study of the Manas River Basin. Water 2019, 11, 1058 .

AMA Style

Yan Liu, Pu Zhang, Lei Nie, Jianhui Xu, Xinyu Lu, Shuai Li, Liu, Nie, Xu, Lu, Li. Exploration of the Snow Ablation Process in the Semiarid Region in China by Combining Site-Based Measurements and the Utah Energy Balance Model—A Case Study of the Manas River Basin. Water. 2019; 11 (5):1058.

Chicago/Turabian Style

Yan Liu; Pu Zhang; Lei Nie; Jianhui Xu; Xinyu Lu; Shuai Li; Liu; Nie; Xu; Lu; Li. 2019. "Exploration of the Snow Ablation Process in the Semiarid Region in China by Combining Site-Based Measurements and the Utah Energy Balance Model—A Case Study of the Manas River Basin." Water 11, no. 5: 1058.

Journal article
Published: 10 April 2019 in Remote Sensing
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More than 90% of the sugar production in China comes from sugarcane, which is widely grown in South China. Optical image time series have proven to be efficient for sugarcane mapping. There are, however, two limitations associated with previous research: one is that the critical observations during the sugarcane growing season are limited due to frequent cloudy weather in South China; the other is that the classification method requires imagery time series covering the entire growing season, which reduces the time efficiency. The Sentinel-1A (S1A) synthetic aperture radar (SAR) data featuring relatively high spatial-temporal resolution provides an ideal data source for all-weather observations. In this study, we attempted to develop a method for the early season mapping of sugarcane. First, we proposed a framework consisting of two procedures: initial sugarcane mapping using the S1A SAR imagery time series, followed by non-vegetation removal using Sentinel-2 optical imagery. Second, we tested the framework using an incremental classification strategy based on S1A imagery covering the entire 2017–2018 sugarcane season. The study area was in Suixi and Leizhou counties of Zhanjiang city, China. Results indicated that an acceptable accuracy, in terms of Kappa coefficient, can be achieved to a level above 0.902 using time series three months before sugarcane harvest. In general, sugarcane mapping utilizing the combination of VH + VV as well as VH polarization alone outperformed mapping using VV alone. Although the XGBoost classifier with VH + VV polarization achieved a maximum accuracy that was slightly lower than the random forest (RF) classifier, the XGBoost shows promising performance in that it was more robust to overfitting with noisy VV time series and the computation speed was 7.7 times faster than RF classifier. The total sugarcane areas in Suixi and Leizhou for the 2017–2018 harvest year estimated by this study were approximately 598.95 km2 and 497.65 km2, respectively. The relative accuracy of the total sugarcane mapping area was approximately 86.3%.

ACS Style

Hao Jiang; Dan Li; Wenlong Jing; Jianhui Xu; Jianxi Huang; Ji Yang; Shuisen Chen. Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China. Remote Sensing 2019, 11, 861 .

AMA Style

Hao Jiang, Dan Li, Wenlong Jing, Jianhui Xu, Jianxi Huang, Ji Yang, Shuisen Chen. Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China. Remote Sensing. 2019; 11 (7):861.

Chicago/Turabian Style

Hao Jiang; Dan Li; Wenlong Jing; Jianhui Xu; Jianxi Huang; Ji Yang; Shuisen Chen. 2019. "Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China." Remote Sensing 11, no. 7: 861.

Journal article
Published: 02 April 2019 in Science of The Total Environment
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Statistical modeling using ground-based PM2.5 observations and satellite-derived aerosol optical depth (AOD) data is a promising means of obtaining spatially and temporally continuous PM2.5 estimations to assess population exposure to PM2.5. However, the vast amount of AOD data that is missing due to retrieval incapability above bright reflecting surfaces such as cloud/snow cover and urban areas challenge this application. Furthermore, most previous studies cannot directly account for the spatiotemporal autocorrelations in PM2.5 distribution, impacting the associated estimation accuracy. In this study, fixed rank smoothing was adopted to fill the data gaps in a semifinished 3 km AOD dataset, which was a combination of the Moderate Resolution Imaging Spectroradiometer (MODIS) 3 km Dark Target AOD data and MODIS 10 km Deep Blue AOD data from the Terra and Aqua satellites. By matching the gap-filled 3 km AOD data, ground-based PM2.5 observations, and auxiliary variable data, sufficient samples were screened to develop a spatiotemporal regression kriging (STRK) model for PM2.5 estimation. The STRK model achieved notable performance in a cross-validation experiment, with a R square of 0.87 and root-mean-square error of 16.55 μg/m3 when applied to estimate daily ground-level PM2.5 concentrations over East China from March 1, 2015 to February 29, 2016. Using the STRK model, daily PM2.5 concentrations with full spatial coverage at a resolution of 3 km were generated. The PM2.5 distribution pattern over East China can be identified at a relatively fine spatiotemporal scale. Thus, the STRK model with gap-filled high-resolution AOD data can provide reliable full-coverage PM2.5 estimations over large areas for long-term exposure assessment in epidemiological studies.

ACS Style

Hongda Hu; Zhiyong Hu; Kaiwen Zhong; Jianhui Xu; Feifei Zhang; Yi Zhao; Pinghao Wu. Satellite-based high-resolution mapping of ground-level PM2.5 concentrations over East China using a spatiotemporal regression kriging model. Science of The Total Environment 2019, 672, 479 -490.

AMA Style

Hongda Hu, Zhiyong Hu, Kaiwen Zhong, Jianhui Xu, Feifei Zhang, Yi Zhao, Pinghao Wu. Satellite-based high-resolution mapping of ground-level PM2.5 concentrations over East China using a spatiotemporal regression kriging model. Science of The Total Environment. 2019; 672 ():479-490.

Chicago/Turabian Style

Hongda Hu; Zhiyong Hu; Kaiwen Zhong; Jianhui Xu; Feifei Zhang; Yi Zhao; Pinghao Wu. 2019. "Satellite-based high-resolution mapping of ground-level PM2.5 concentrations over East China using a spatiotemporal regression kriging model." Science of The Total Environment 672, no. : 479-490.

Journal article
Published: 31 August 2018 in Sensors
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This study explores the performance of Sentinel-2A Multispectral Instrument (MSI) imagery for extracting urban impervious surface using a modified linear spectral mixture analysis (MLSMA) method. Sentinel-2A MSI provided 10 m red, green, blue, and near-infrared spectral bands, and 20 m shortwave infrared spectral bands, which were used to extract impervious surfaces. We aimed to extract urban impervious surfaces at a spatial resolution of 10 m in the main urban area of Guangzhou, China. In MLSMA, a built-up image was first extracted from the normalized difference built-up index (NDBI) using the Otsu’s method; the high-albedo, low-albedo, vegetation, and soil fractions were then estimated using conventional linear spectral mixture analysis (LSMA). The LSMA results were post-processed to extract high-precision impervious surface, vegetation, and soil fractions by integrating the built-up image and the normalized difference vegetation index (NDVI). The performance of MLSMA was evaluated using Landsat 8 Operational Land Imager (OLI) imagery. Experimental results revealed that MLSMA can extract the high-precision impervious surface fraction at 10 m with Sentinel-2A imagery. The 10 m impervious surface map of Sentinel-2A is capable of recovering more detail than the 30 m map of Landsat 8. In the Sentinel-2A impervious surface map, continuous roads and the boundaries of buildings in urban environments were clearly identified.

ACS Style

Rudong Xu; Jin Liu; Jianhui Xu. Extraction of High-Precision Urban Impervious Surfaces from Sentinel-2 Multispectral Imagery via Modified Linear Spectral Mixture Analysis. Sensors 2018, 18, 2873 .

AMA Style

Rudong Xu, Jin Liu, Jianhui Xu. Extraction of High-Precision Urban Impervious Surfaces from Sentinel-2 Multispectral Imagery via Modified Linear Spectral Mixture Analysis. Sensors. 2018; 18 (9):2873.

Chicago/Turabian Style

Rudong Xu; Jin Liu; Jianhui Xu. 2018. "Extraction of High-Precision Urban Impervious Surfaces from Sentinel-2 Multispectral Imagery via Modified Linear Spectral Mixture Analysis." Sensors 18, no. 9: 2873.

Journal article
Published: 01 June 2018 in Science of The Total Environment
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This study evaluated the spatio-temporal change characteristics of urban development at different scales with time-series impervious surface fractions. Landsat-5 Thematic Mapper (TM) and Landsat-8 Operational Land Imager (OLI) images were used to extract impervious surface fractions using a modified linear spectral mixture analysis method in Guangzhou from 1988 to 2015. The results indicated that the impervious surface area has substantially increased, from 70.3 km in 1988 to 580.5 km in 2015. In 2015, the impervious surfaces were distributed almost throughout the whole region of the study area, except in the forest region. Next, impervious surface weighted mean centre (ISWMC) and the standard deviational ellipse (SDE) methods were used to systematically analyse the principle orientation, direction, spatio-temporal expansion trends, and the distribution differences of impervious surfaces at the whole and local region scales from 1988 to 2015. The spatio-temporal dynamics of ISWMC exhibited different expansion directions and intensities of impervious surfaces at the whole and local region scales. On a whole region scale, the principle expansion direction of impervious surfaces was northward. However, the expansion trend of impervious surfaces in the different districts was significantly different from other trends at the local region scale. The parameters of SDE were used to investigate the orientation and the clustering or dispersion degree of impervious surface at different scales. The results from SDE analysis indicated that the impervious surfaces exhibited uncertainty in the expansion direction at the whole region scale; in contrast, they had a distinct preferred orientation and expansion direction at the local region scale. The analysis revealed that urban expansion exhibited different change characteristics in various directions at the local region scale. In summary, the results at the local region scale can better reflect the change trajectory of spatio-temporal dynamics of urban development and its fine spatial structure than at the whole region scale.

ACS Style

Jianhui Xu; Yi Zhao; Kaiwen Zhong; Feifei Zhang; Xulong Liu; Caige Sun. Measuring spatio-temporal dynamics of impervious surface in Guangzhou, China, from 1988 to 2015, using time-series Landsat imagery. Science of The Total Environment 2018, 627, 264 -281.

AMA Style

Jianhui Xu, Yi Zhao, Kaiwen Zhong, Feifei Zhang, Xulong Liu, Caige Sun. Measuring spatio-temporal dynamics of impervious surface in Guangzhou, China, from 1988 to 2015, using time-series Landsat imagery. Science of The Total Environment. 2018; 627 ():264-281.

Chicago/Turabian Style

Jianhui Xu; Yi Zhao; Kaiwen Zhong; Feifei Zhang; Xulong Liu; Caige Sun. 2018. "Measuring spatio-temporal dynamics of impervious surface in Guangzhou, China, from 1988 to 2015, using time-series Landsat imagery." Science of The Total Environment 627, no. : 264-281.

Journal article
Published: 01 September 2017 in Water
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High-resolution water mapping with remotely sensed data is essential to monitoring of rainstorm waterlogging and flood disasters. In this study, a modified linear spectral mixture analysis (LSMA) method is proposed to extract high-precision water fraction maps. In the modified LSMA, the pure water and mixed water-land pixels, which are extracted by the Otsu method and a morphological dilation operation, are used to improve the accuracy of water fractions. The modified LSMA is applied to the 18 October 2015 Landsat 8 OLI image of the Pearl River Delta for the extraction of water fractions. Based on the water fraction maps, a modified subpixel mapping method (MSWM) based on a pixel-swapping algorithm is proposed for obtaining the spatial distribution information of water at subpixel scale. The MSWM includes two steps in subpixel water mapping. The MSWM considers the inter-subpixel/pixel and intra-subpixel/subpixel spatial attractions. Subpixel water mapping is first implemented with the inter-subpixel/pixel spatial attractions, which are estimated using the distance between a given subpixel and its surrounding pixels and the water fractions of the surrounding pixels. Based on the initialized subpixel water mapping results, the final subpixel water maps are determined by a modified pixel-swapping algorithm, in which the intra-subpixel/subpixel spatial attractions are estimated using the initialized subpixel water maps and an inverse-distance weighted function of the current subpixel at the centre of a moving window with its surrounding subpixels within the window. The subpixel water mapping performance of the MSWM is compared with that of subpixel mapping for linear objects (SPML) and that of the subpixel/pixel spatial attraction model (SPSAM) using the GF-1 reference image from 20 October 2015. The experimental results show that the MSWM shows better subpixel water mapping performance and obtains more details than SPML and SPSAM, as it has the largest overall accuracy values and Kappa coefficients. Furthermore, the MSWM can significantly eliminate the phenomenon of jagged edges and has smooth continuous edges.

ACS Style

Xulong Liu; Ruru Deng; Jianhui Xu; Feifei Zhang. Coupling the Modified Linear Spectral Mixture Analysis and Pixel-Swapping Methods for Improving Subpixel Water Mapping: Application to the Pearl River Delta, China. Water 2017, 9, 658 .

AMA Style

Xulong Liu, Ruru Deng, Jianhui Xu, Feifei Zhang. Coupling the Modified Linear Spectral Mixture Analysis and Pixel-Swapping Methods for Improving Subpixel Water Mapping: Application to the Pearl River Delta, China. Water. 2017; 9 (9):658.

Chicago/Turabian Style

Xulong Liu; Ruru Deng; Jianhui Xu; Feifei Zhang. 2017. "Coupling the Modified Linear Spectral Mixture Analysis and Pixel-Swapping Methods for Improving Subpixel Water Mapping: Application to the Pearl River Delta, China." Water 9, no. 9: 658.

Journal article
Published: 01 January 2017 in Journal of Applied Remote Sensing
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. HJ-1A hyperspectral data were used to distinguish topsoil salt components and estimate soil salinity, and the relationship between soil salt chemical components and sensitive bands of soil reflectance spectra was analyzed. The correlation between the soil salt content and the soil spectra obtained from the hyperspectral data was analyzed, proving that topsoil salinity has a very significant correlation with soil reflectance spectra. The relationship between soil reflectance spectra and salt chemical ions was investigated. The soil spectral reflectance at wavelength 510.975 nm and a difference vegetation index were selected to estimate soil salinity and the dominant salt chemical ion concentrations at a depth of 0 to 10 cm using a partial least squares regression model. It was found that the bands sensitive to various levels of chemical components of soil salt were shown to differ, controlled by the dominant component of the soil salt. The sensitive bands in the soil salinity estimation will change with differences in salt components. Estimating the dominant salt in the soil using soil reflectance spectra will lead to greater prediction accuracy. This study provided a possible method for the estimation of salinity and chemical component levels in topsoil, using the hyperspectral data to estimate topsoil salt components.

ACS Style

Hongnan Jiang; Hong Shu; Lei Lei; Jianhui Xu. Estimating soil salt components and salinity using hyperspectral remote sensing data in an arid area of China. Journal of Applied Remote Sensing 2017, 11, 16043 .

AMA Style

Hongnan Jiang, Hong Shu, Lei Lei, Jianhui Xu. Estimating soil salt components and salinity using hyperspectral remote sensing data in an arid area of China. Journal of Applied Remote Sensing. 2017; 11 (1):16043.

Chicago/Turabian Style

Hongnan Jiang; Hong Shu; Lei Lei; Jianhui Xu. 2017. "Estimating soil salt components and salinity using hyperspectral remote sensing data in an arid area of China." Journal of Applied Remote Sensing 11, no. 1: 16043.

Journal article
Published: 01 January 2017 in Journal of Hydrometeorology
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During snow cover fraction (SCF) data assimilation (DA), the simplified observation operator and presence of cloud cover cause large errors in the assimilation results. To reduce these errors, a new snow cover depletion curve (SDC), known as an observation operator in the DA system, is statistically fitted to in situ snow depth (SD) observations and Moderate Resolution Imaging Spectroradiometer (MODIS) SCF data from January 2004 to October 2008. Using this new SDC, a two-dimensional deterministic ensemble–variational hybrid DA (2DEnVar) method of integrating the deterministic ensemble Kalman filter (DEnKF) and a two-dimensional variational DA (2DVar) is proposed. The proposed 2DEnVar is then used to assimilate the MODIS SCF into the Common Land Model (CoLM) at five sites in the Altay region of China for data from November 2008 to March 2009. The analysis performance of the 2DEnVar is compared with that of the DEnKF. The results show that the 2DEnVar outperforms the DEnKF as it effectively reduces the bias and root-mean-square error during the snow accumulation and ablation periods at all sites except for the Qinghe site. In addition, the 2DEnVar, with more assimilated MODIS SCF observations, produces more innovations (observation minus forecast) than the DEnKF, with only one assimilated MODIS SCF observation. The problems of cloud cover and overestimation are addressed by the 2DEnVar.

ACS Style

Jianhui Xu; Feifei Zhang; Hong Shu; Kaiwen Zhong. Improvement of the Snow Depth in the Common Land Model by Coupling a Two-Dimensional Deterministic Ensemble Model with a Variational Hybrid Snow Cover Fraction Data Assimilation Scheme and a New Observation Operator. Journal of Hydrometeorology 2017, 18, 119 -138.

AMA Style

Jianhui Xu, Feifei Zhang, Hong Shu, Kaiwen Zhong. Improvement of the Snow Depth in the Common Land Model by Coupling a Two-Dimensional Deterministic Ensemble Model with a Variational Hybrid Snow Cover Fraction Data Assimilation Scheme and a New Observation Operator. Journal of Hydrometeorology. 2017; 18 (1):119-138.

Chicago/Turabian Style

Jianhui Xu; Feifei Zhang; Hong Shu; Kaiwen Zhong. 2017. "Improvement of the Snow Depth in the Common Land Model by Coupling a Two-Dimensional Deterministic Ensemble Model with a Variational Hybrid Snow Cover Fraction Data Assimilation Scheme and a New Observation Operator." Journal of Hydrometeorology 18, no. 1: 119-138.

Journal article
Published: 24 November 2016 in Water
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Land surface characteristics, including soil type, terrain slope, and antecedent soil moisture, have significant impacts on surface runoff during heavy precipitation in highly urbanized areas. In this study, a Linear Spectral Mixture Analysis (LSMA) method is modified to extract high-precision impervious surface, vegetation, and soil fractions. In the modified LSMA method, the representative endmembers are first selected by combining a high-resolution image from Google Earth; the unmixing results of the LSMA are then post-processed to reduce errors of misclassification with Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). The modified LSMA is applied to the Landsat 8 Operational Land Imager (OLI) image from 18 October 2015 of the main urban area of Guangzhou city. The experimental result indicates that the modified LSMA shows improved extraction performance compared with the conventional LSMA, as it can significantly reduce the bias and root-mean-square error (RMSE). The improved impervious surface, vegetation, and soil fractions are used to calculate the composite curve number (CN) for each pixel according to the Soil Conservation Service curve number (SCS-CN) model. The composite CN is then adjusted with regional data of the terrain slope and total 5-day antecedent precipitation. Finally, the surface runoff is simulated with the SCS-CN model by combining the adjusted CN and real precipitation data at 1 p.m., 4 May 2015.

ACS Style

Jianhui Xu; Yi Zhao; Kaiwen Zhong; Huihua Ruan; Xulong Liu. Coupling Modified Linear Spectral Mixture Analysis and Soil Conservation Service Curve Number (SCS-CN) Models to Simulate Surface Runoff: Application to the Main Urban Area of Guangzhou, China. Water 2016, 8, 550 .

AMA Style

Jianhui Xu, Yi Zhao, Kaiwen Zhong, Huihua Ruan, Xulong Liu. Coupling Modified Linear Spectral Mixture Analysis and Soil Conservation Service Curve Number (SCS-CN) Models to Simulate Surface Runoff: Application to the Main Urban Area of Guangzhou, China. Water. 2016; 8 (12):550.

Chicago/Turabian Style

Jianhui Xu; Yi Zhao; Kaiwen Zhong; Huihua Ruan; Xulong Liu. 2016. "Coupling Modified Linear Spectral Mixture Analysis and Soil Conservation Service Curve Number (SCS-CN) Models to Simulate Surface Runoff: Application to the Main Urban Area of Guangzhou, China." Water 8, no. 12: 550.

Journal article
Published: 01 July 2016 in Journal of Applied Remote Sensing
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For the large-area snow depth (SD) data sets with high spatial resolution in the Altay region of Northern Xinjiang, China, we present a deterministic ensemble Kalman filter (DEnKF)-albedo assimilation scheme that considers the common land model (CoLM) subgrid heterogeneity. In the albedo assimilation of DEnKF-albedo, the assimilated albedos over each subgrid tile are estimated with the MCD43C1 bidirectional reflectance distribution function (BRDF) parameters product and CoLM calculated solar zenith angle. The BRDF parameters are hypothesized to be consistent over all subgrid tiles within a specified grid. In the SCF assimilation of DEnKF-albedo, a DEnKF combining a snow density-based observation operator considers the effects of the CoLM subgrid heterogeneity and is employed to assimilate MODIS SCF to update SD states over all subgrid tiles. The MODIS SCF over a grid is compared with the area-weighted sum of model predicted SCF over all the subgrid tiles within the grid. The results are validated with in situ SD measurements and AMSR-E product. Compared with the simulations, the DEnKF-albedo scheme can reduce errors of SD simulations and accurately simulate the seasonal variability of SD. Furthermore, it can improve simulations of SD spatiotemporal distribution in the Altay region, which is more accurate and shows more detail than the AMSR-E product.

ACS Style

Jianhui Xu; Feifei Zhang; Yi Zhao; Hong Shu; Kaiwen Zhong. Joint DEnKF-albedo assimilation scheme that considers the common land model subgrid heterogeneity and a snow density-based observation operator for improving snow depth simulations. Journal of Applied Remote Sensing 2016, 10, 036001 -036001.

AMA Style

Jianhui Xu, Feifei Zhang, Yi Zhao, Hong Shu, Kaiwen Zhong. Joint DEnKF-albedo assimilation scheme that considers the common land model subgrid heterogeneity and a snow density-based observation operator for improving snow depth simulations. Journal of Applied Remote Sensing. 2016; 10 (3):036001-036001.

Chicago/Turabian Style

Jianhui Xu; Feifei Zhang; Yi Zhao; Hong Shu; Kaiwen Zhong. 2016. "Joint DEnKF-albedo assimilation scheme that considers the common land model subgrid heterogeneity and a snow density-based observation operator for improving snow depth simulations." Journal of Applied Remote Sensing 10, no. 3: 036001-036001.