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Guangxing Wang

D-Index & Metrics

Computer Science

D-Index
31
Citations
4353
World Ranking
13618
National Ranking
5436

Guangxing Wang publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Guangxing Wang sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 103 publications — 9th percentile

9% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Guangxing Wang D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Guangxing Wang sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 31 D-Index — 6th percentile

6% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Guangxing Wang is affiliated with Southern Illinois University Carbondale in the United States. Their research primarily spans the field of Environmental Science, with a focus on several specialized subfields including Environmental Engineering, Ecology, Global and Planetary Change, Nature and Landscape Conservation, and Atmospheric Science.

The scientist's work covers multiple research topics that reflect both their broad and specific interests. These topics include:

  • Remote Sensing and LiDAR Applications
  • Remote Sensing in Agriculture
  • Forest ecology and management
  • Land Use and Ecosystem Services
  • Physical Activity and Health
  • Hydrology and Sediment Transport Processes
  • Hydrology and Watershed Management Studies

Guangxing Wang frequently publishes in several scientific journals. The main publication venues are:

  • Remote Sensing
  • Sensors
  • International Journal of Remote Sensing
  • Forests
  • SSRN Electronic Journal

The scientist has been involved in multiple research papers with varied themes. Some recent publications include:

  • "Deep Learning Segmentation and Classification for Urban Village Using a Worldview Satellite Image Based on U-Net," 2020, Remote Sensing
  • "Associations of Daily Steps and Step Intensity With Incident Diabetes in a Prospective Cohort Study of Older Women: The OPACH Study," 2022, Diabetes Care
  • "Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method," 2020, Sensors
  • "Feasibility of a behavioral intervention using mobile health applications to reduce cardiovascular risk factors in cancer survivors: a pilot randomized controlled trial," 2020, Journal of Cancer Survivorship
  • "Identification of the critical accident causative factors in the urban rail transit system by complex network theory," 2022, Physica A Statistical Mechanics and its Applications

Collaborations form a significant part of Guangxing Wang's research output. Frequent co-authors include:

  • Chongzhi Di
  • Kelly R. Evenson
  • Ruopu Li
  • Andrea Z. LaCroix
  • John Bellettiere

Best Publications

  • A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

    Dengsheng Lu;Qi Chen;Guangxing Wang;Lijuan Liu

  • Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates

    Dengsheng Lu;Qi Chen;Guangxing Wang;Emilio Moran

  • Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

    Panpan Zhao;Dengsheng Lu;Guangxing Wang;Chuping Wu

  • Mapping Multiple Variables for Predicting Soil Loss by Geostatistical Methods with TM Images and a Slope Map

    Guangxing Wang;George Gertner;Shoufan Fang;Alan B. Anderson

  • Deep Learning Segmentation and Classification for Urban Village Using a Worldview Satellite Image Based on U-Net

    Zhuokun Pan;Jiashu Xu;Yubin Guo;Yueming Hu

  • Improvement in mapping vegetation cover factor for the universal soil loss equation by geostatistical methods with Landsat Thematic Mapper images

    G. Wang;S. Wente;G. Z. Gertner;A. Anderson

  • Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region

    Yukun Gao;Dengsheng Lu;Guiying Li;Guangxing Wang

  • Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

    Meng Zhang;Hui Lin;Guangxing Wang;Hua Sun

  • Multipath analysis of code measurements for BeiDou geostationary satellites

    Guangxing Wang;Kees Jong;Qile Zhao;Zhigang Hu

  • Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data

    Panpan Zhao;Dengsheng Lu;Guangxing Wang;Lijuan Liu

  • Phenology-based classification of vegetation cover types in Northeast China using MODIS NDVI and EVI time series

    Enping Yan;Guangxing Wang;Hui Lin;Chaozong Xia

  • Mapping and spatial uncertainty analysis of forest vegetation carbon by combining national forest inventory data and satellite images

    Guangxing Wang;Tonny Oyana;Maozhen Zhang;Samuel Adu-Prah

  • The geography of ecosystem service value: The case of the Des Plaines and Cache River wetlands, Illinois

    Justin Kozak;Christopher L. Lant;Sabina Shaikh;Guangxing Wang

  • The calibration of digitized aerial photographs for forest stratification

    Markus Holopainen;Guangxing Wang

  • Remote sensing of natural resources

    Guangxing Wang;Qihao Weng

  • Estimation of Soil Heavy Metal Content Using Hyperspectral Data

    Zhenhua Liu;Ying Lu;Yiping Peng;Li Zhao

  • Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing

    Li Zhao;Yue-Ming Hu;Wu Zhou;Zhen-Hua Liu

  • Diagnosis and Prediction of Traffic Congestion on Urban Road Networks Using Bayesian Networks

    Unknown

  • Mapping and uncertainty of predictions based on multiple primary variables from joint co-simulation with Landsat TM image and polynomial regression

    George Gertner;Guangxing Wang;Shoufan Fang;Alan B. Anderson

  • A Methodology for Spatial Uncertainty Analysis Of Remote Sensing and GIS Products

    Guangxing Wang;George Z. Gertner;Shoufan Fang;Alan B. Anderson

  • Effect and uncertainty of digital elevation model spatial resolutions on predicting the topographical factor for soil loss estimation

    George Gertner;Guangxing Wang;Shoufan Fang;Alan B. Anderson

  • Integrating a Hybrid Back Propagation Neural Network and Particle Swarm Optimization for Estimating Soil Heavy Metal Contents Using Hyperspectral Data

    Piao Liu;Zhenhua Liu;Yueming Hu;Zhou Shi

  • Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods

    Jia Zhu;Zhihong Huang;Hua Sun;Guangxing Wang

  • Improving Aboveground Biomass Estimation of Pinus densata Forests in Yunnan Using Landsat 8 Imagery by Incorporating Age Dummy Variable and Method Comparison

    Guanglong Ou;Chao Li;Yanyu Lv;Anchao Wei

  • Determination of earthquake magnitude using GPS displacement waveforms from real-time precise point positioning

    Rongxin Fang;Chuang Shi;Weiwei Song;Guangxing Wang

  • Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method.

    Fugen Jiang;Mykola Kutia;Arbi J Sarkissian;Hui Lin

  • Prediction of soil properties using a hyperspectral remote sensing method

    Huan Yu;Huan Yu;Bo Kong;Guangxing Wang;Rongxiang Du

Frequent Co-Authors

Hui Lin
Hui Lin Jiangxi Normal University
Qile Zhao
Qile Zhao Wuhan University
Dengsheng Lu
Dengsheng Lu Fujian Normal University
Zhou Shi
Zhou Shi Zhejiang University
Chuang Shi
Chuang Shi Wuhan University
Qi Chen
Qi Chen University of Hawaii at Manoa
Ronald E. McRoberts
Ronald E. McRoberts University of Minnesota
Emilio F. Moran
Emilio F. Moran Michigan State University
Qihao Weng
Qihao Weng Hong Kong Polytechnic University
Markus Holopainen
Markus Holopainen University of Helsinki

External Links

Personal website of Guangxing Wang

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