World's Best Scientists 2026 revealed!

D-Index & Metrics

Environmental Sciences

D-Index
66
Citations
16487
World Ranking
2084
National Ranking
854

A-Xing Zhu publication distribution in Environmental Sciences in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Environmental Sciences in 2026. The highlighted bar marks where A-Xing Zhu sits on this spectrum.

41–50 publications: 21 scientists 51–60 publications: 62 scientists 61–70 publications: 133 scientists 71–80 publications: 257 scientists 81–90 publications: 361 scientists 91–100 publications: 440 scientists 101–110 publications: 492 scientists 111–120 publications: 541 scientists 121–130 publications: 617 scientists 131–140 publications: 544 scientists 141–150 publications: 541 scientists 151–160 publications: 539 scientists 161–170 publications: 444 scientists 171–180 publications: 444 scientists 181–190 publications: 400 scientists 191–200 publications: 377 scientists 201–210 publications: 318 scientists 211–220 publications: 283 scientists 221–230 publications: 263 scientists 231–240 publications: 220 scientists 241–250 publications: 217 scientists 251–260 publications: 180 scientists 261–270 publications: 181 scientists 271–280 publications: 155 scientists 281–290 publications: 130 scientists 291–300 publications: 127 scientists 301–310 publications: 130 scientists 311–320 publications: 85 scientists 321–330 publications: 106 scientists 331–340 publications: 80 scientists 341–350 publications: 83 scientists 351–360 publications: 75 scientists 361–370 publications: 69 scientists 371–380 publications: 52 scientists 381–390 publications: 54 scientists 391–400 publications: 56 scientists 401–410 publications: 44 scientists 411–420 publications: 40 scientists 421–430 publications: 36 scientists 431–440 publications: 25 scientists 441–450 publications: 25 scientists 451–460 publications: 32 scientists 461–470 publications: 29 scientists 471–480 publications: 21 scientists 481–490 publications: 26 scientists 491–500 publications: 25 scientists 501–510 publications: 17 scientists 511–520 publications: 18 scientists 521–530 publications: 15 scientists 531–540 publications: 22 scientists 541–550 publications: 12 scientists 551–560 publications: 15 scientists 561–570 publications: 11 scientists 571–580 publications: 19 scientists 581–590 publications: 9 scientists 591–600 publications: 9 scientists 601–610 publications: 7 scientists 611–620 publications: 11 scientists 621–630 publications: 5 scientists 631–640 publications: 5 scientists 641–650 publications: 6 scientists 651–660 publications: 3 scientists 661–670 publications: 3 scientists 671–680 publications: 4 scientists 681–686 publications: 3 scientists 687+ publications: 100 scientists
41 publications 687+

This scientist: 271 publications — 82nd percentile

82% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 687 publications or more.

A-Xing Zhu D-index placement in Environmental Sciences in 2026

The chart shows the D-index (discipline H-index) distribution of Environmental Sciences scientists ranked by Research.com in 2026. The highlighted bar marks where A-Xing Zhu sits on this spectrum.

30 D-Index: 12 scientists 31 D-Index: 26 scientists 32 D-Index: 51 scientists 33 D-Index: 88 scientists 34 D-Index: 123 scientists 35 D-Index: 163 scientists 36 D-Index: 206 scientists 37 D-Index: 267 scientists 38 D-Index: 265 scientists 39 D-Index: 275 scientists 40 D-Index: 321 scientists 41 D-Index: 343 scientists 42 D-Index: 305 scientists 43 D-Index: 336 scientists 44 D-Index: 330 scientists 45 D-Index: 348 scientists 46 D-Index: 291 scientists 47 D-Index: 275 scientists 48 D-Index: 272 scientists 49 D-Index: 273 scientists 50 D-Index: 263 scientists 51 D-Index: 232 scientists 52 D-Index: 266 scientists 53 D-Index: 217 scientists 54 D-Index: 199 scientists 55 D-Index: 177 scientists 56 D-Index: 202 scientists 57 D-Index: 204 scientists 58 D-Index: 166 scientists 59 D-Index: 177 scientists 60 D-Index: 166 scientists 61 D-Index: 152 scientists 62 D-Index: 143 scientists 63 D-Index: 150 scientists 64 D-Index: 124 scientists 65 D-Index: 119 scientists 66 D-Index: 120 scientists 67 D-Index: 118 scientists 68 D-Index: 82 scientists 69 D-Index: 98 scientists 70 D-Index: 94 scientists 71 D-Index: 105 scientists 72 D-Index: 74 scientists 73 D-Index: 84 scientists 74 D-Index: 70 scientists 75 D-Index: 67 scientists 76 D-Index: 78 scientists 77 D-Index: 60 scientists 78 D-Index: 59 scientists 79 D-Index: 52 scientists 80 D-Index: 47 scientists 81 D-Index: 38 scientists 82 D-Index: 48 scientists 83 D-Index: 42 scientists 84 D-Index: 42 scientists 85 D-Index: 43 scientists 86 D-Index: 29 scientists 87 D-Index: 37 scientists 88 D-Index: 29 scientists 89 D-Index: 30 scientists 90 D-Index: 34 scientists 91 D-Index: 20 scientists 92 D-Index: 22 scientists 93 D-Index: 17 scientists 94 D-Index: 19 scientists 95 D-Index: 24 scientists 96 D-Index: 21 scientists 97 D-Index: 20 scientists 98 D-Index: 24 scientists 99 D-Index: 17 scientists 100 D-Index: 17 scientists 101 D-Index: 21 scientists 102 D-Index: 25 scientists 103 D-Index: 18 scientists 104 D-Index: 26 scientists 105 D-Index: 19 scientists 106 D-Index: 15 scientists 107 D-Index: 10 scientists 108 D-Index: 13 scientists 109 D-Index: 15 scientists 110 D-Index: 12 scientists 111 D-Index: 8 scientists 112 D-Index: 7 scientists 113 D-Index: 9 scientists 114 D-Index: 6 scientists 115 D-Index: 12 scientists 116 D-Index: 7 scientists 117 D-Index: 8 scientists 118 D-Index: 3 scientists 119 D-Index: 5 scientists 120 D-Index: 7 scientists 121 D-Index: 2 scientists 122 D-Index: 4 scientists 123 D-Index: 8 scientists 124 D-Index: 7 scientists 125+ D-Index: 99 scientists
30 D-Index 125+

This scientist: 66 D-Index — 79th percentile

79% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 125 D-Index or more.

Overview

A-Xing Zhu is affiliated with the University of Wisconsin-Madison in the United States. Their research primarily spans the field of Environmental Science with a particular focus on subfields such as Environmental Engineering, Global and Planetary Change, Ecology, Soil Science, and Atmospheric Science.

Their main topics of scientific inquiry include:

  • Soil Geostatistics and Mapping
  • Hydrology and Watershed Management Studies
  • Soil erosion and sediment transport
  • Remote Sensing in Agriculture
  • Land Use and Ecosystem Services
  • Soil and Unsaturated Flow
  • Remote Sensing and LiDAR Applications

Among their recent published papers are:

  • "Mapping high resolution National Soil Information Grids of China," 2021, Science Bulletin
  • "Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble," 2020, The Science of The Total Environment
  • "CN-China: Revised runoff curve number by using rainfall-runoff events data in China," 2020, Water Research
  • "Low rank and collaborative representation for hyperspectral anomaly detection via robust dictionary construction," 2020, ISPRS Journal of Photogrammetry and Remote Sensing
  • "How is the Third Law of Geography different?," 2022, Annals of GIS

The scientist has frequently published in the following venues:

  • Annals of GIS
  • Geoderma
  • International Journal of Geographical Information Systems
  • Land
  • The Science of The Total Environment

Frequent co-authors collaborating with A-Xing Zhu include:

  • Cheng-Zhi Qin
  • Fang-He Zhao
  • Liang-Jun Zhu
  • Peng Liang
  • Lin Yang

Best Publications

  • Soil Mapping Using GIS, Expert Knowledge, and Fuzzy Logic

    A. X. Zhu;B. Hudson;J. Burt;K. Lubich

  • A China data set of soil properties for land surface modeling

    Wei Shangguan;Yongjiu Dai;Baoyuan Liu;Axing Zhu

  • Mapping high resolution National Soil Information Grids of China

    Feng Liu;Huayong Wu;Yuguo Zhao;Decheng Li

  • Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)

    Haoyuan Hong;Haoyuan Hong;Junzhi Liu;Junzhi Liu;Dieu Tien Bui;Biswajeet Pradhan;Biswajeet Pradhan

  • Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China.

    Wei Chen;Jianbing Peng;Haoyuan Hong;Haoyuan Hong;Himan Shahabi

  • Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China.

    Haoyuan Hong;Paraskevas Tsangaratos;Ioanna Ilia;Junzhi Liu;Junzhi Liu

  • Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution.

    Haoyuan Hong;Haoyuan Hong;Mahdi Panahi;Ataollah Shirzadi;Tianwu Ma;Tianwu Ma

  • Multi-scale digital terrain analysis and feature selection for digital soil mapping

    Thorsten Behrens;A-Xing Zhu;A-Xing Zhu;Karsten Schmidt;Thomas Scholten

  • Derivation of Soil Properties Using a Soil Land Inference Model (SoLIM)

    A-Xing Zhu;Lawrence Band;Robert Vertessy;Barry Dutton

  • A similarity model for representing soil spatial information

    A-Xing Zhu

  • GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method

    Wei Chen;Xiaoshen Xie;Jianbing Peng;Himan Shahabi

  • Reflections and speculations on the progress in Geographic Information Systems (GIS): a geographic perspective

    Guonian Lü;Michael Batty;Josef Strobl;Hui Lin

  • Spatial prediction based on Third Law of Geography

    A‐Xing Zhu;Guonian Lu;Guonian Lu;Jing Liu;Cheng‐Zhi Qin

  • Developing a continental-scale measure of gross primary production by combining MODIS and AmeriFlux data through Support Vector Machine approach

    Feihua Yang;Feihua Yang;Kazuhito Ichii;Kazuhito Ichii;Michael A. White;Hirofumi Hashimoto;Hirofumi Hashimoto

  • An approach to computing topographic wetness index based on maximum downslope gradient

    Cheng-Zhi Qin;A-Xing Zhu;Tao Pei;Bao-Lin Li

  • An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic

    A-Xing Zhu;A-Xing Zhu;A-Xing Zhu;Rongxun Wang;Jianping Qiao;Cheng-Zhi Qin

  • Automated soil inference under fuzzy logic

    A. X. Zhu;L. E. Band;B. Dutton;T. J. Nimlos

  • The effects of DEM resolution and neighborhood size on digital soil survey

    Michael P. Smith;A-Xing Zhu;A-Xing Zhu;James E. Burt;Cynthia Stiles

  • Mapping soil organic matter using the topographic wetness index: A comparative study based on different flow-direction algorithms and kriging methods

    Tao Pei;Cheng-Zhi Qin;A-Xing Zhu;Lin Yang

  • Prediction of Continental-Scale Evapotranspiration by Combining MODIS and AmeriFlux Data Through Support Vector Machine

    Feihua Yang;M.A. White;A.R. Michaelis;K. Ichii

  • Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble.

    Haoyuan Hong;Junzhi Liu;Junzhi Liu;A-Xing Zhu

Frequent Co-Authors

Haoyuan Hong
Haoyuan Hong Nanjing University of Information Science and Technology
Chenghu Zhou
Chenghu Zhou Chinese Academy of Sciences
Biswajeet Pradhan
Biswajeet Pradhan University of Technology Sydney
Lawrence E. Band
Lawrence E. Band University of Virginia
Thomas Scholten
Thomas Scholten University of Tübingen
Thorsten Behrens
Thorsten Behrens University of Tübingen
Kazuhito Ichii
Kazuhito Ichii Chiba University
Karsten Schmidt
Karsten Schmidt University of Tübingen
Himan Shahabi
Himan Shahabi University of Kurdistan
Xinyue Ye
Xinyue Ye Texas A&M University

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