World's Best Scientists 2026 revealed!

D-Index & Metrics

Environmental Sciences

D-Index
45
Citations
8246
World Ranking
6372
National Ranking
643

Liangyun Liu 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 Liangyun Liu 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: 238 publications — 75th percentile

75% of scientists in this discipline score the same or lower.

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

Liangyun Liu 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 Liangyun Liu 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: 45 D-Index — 36th percentile

36% of scientists in this discipline score the same or lower.

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

Overview

Liangyun Liu is affiliated with the Chinese Academy of Sciences in China and has made substantial contributions to the field of environmental science, with a focus on remote sensing and land-use studies. Their research explores various aspects of global environmental changes, monitoring, and mapping using advanced remote sensing technologies and time-series satellite imagery.

Their recent noteworthy publications include:

  • "GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method," 2024, Earth system science data
  • "GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery," 2021, Earth system science data
  • "Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform," 2020, Earth system science data
  • "Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects," 2021, Journal of Remote Sensing
  • "GISD30: global 30 m impervious-surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform," 2022, Earth system science data

The primary research topics covered by Liangyun Liu include:

  • Remote Sensing in Agriculture
  • Plant Water Relations and Carbon Dynamics
  • Atmospheric and Environmental Gas Dynamics
  • Land Use and Ecosystem Services
  • Remote Sensing and Land Use
  • Remote Sensing and LiDAR Applications
  • Environmental Changes in China

Their publications predominantly appear in journals such as:

  • Zenodo (CERN European Organization for Nuclear Research)
  • Remote Sensing
  • Earth system science data
  • Journal of Remote Sensing
  • Remote Sensing of Environment

Liangyun Liu has frequently collaborated with several coauthors, including:

  • Xinjie Liu
  • Shanshan Du
  • Xidong Chen
  • Jidai Chen
  • Yuan Gao

Their work spans key subfields within environmental science, such as:

  • Global and Planetary Change
  • Ecology
  • Atmospheric Science
  • Environmental Engineering
  • Plant Science

Through the use of dense time-series satellite data and platforms like Google Earth Engine, Liangyun Liu's research addresses the dynamics of global land cover and impervious surfaces on a fine spatial scale. Their focus includes both methodological development and applied monitoring, providing datasets relevant for environmental assessment and planning.

Best Publications

  • GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery

    Xiao Zhang;Liangyun Liu;Xidong Chen;Yuan Gao;Yuan Gao

  • Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging

    Wenjiang Huang;Wenjiang Huang;David W. Lamb;Zheng Niu;Yongjiang Zhang

  • Development of a global 30-m impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform

    Xiao Zhang;Liangyun Liu;Changshan Wu;Xidong Chen

  • GISD30: global 30 m impervious-surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform

    Unknown

  • Directly estimating diurnal changes in GPP for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence

    Liangyun Liu;Linlin Guan;Xinjie Liu

  • Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

    Liangyun Liu;Xiao Zhang;Yuan Gao;Xidong Chen

  • Downscaling of solar-induced chlorophyll fluorescence from canopy level to photosystem level using a random forest model

    Xinjie Liu;Luis Guanter;Liangyun Liu;Alexander Damm;Alexander Damm

  • Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress

    Chunjiang Zhao;Liangyun Liu;Jihua Wang;Wenjiang Huang

  • Retrieval of global terrestrial solar-induced chlorophyll fluorescence from TanSat satellite

    Shanshan Du;Liangyun Liu;Xinjie Liu;Xiao Zhang

  • Prediction of grain protein content in winter wheat (Triticum aestivum L.) using plant pigment ratio (PPR).

    Z.J. Wang;Z.J. Wang;J.H. Wang;L.Y. Liu;W.J. Huang

  • Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine

    Shuai Xie;Liangyun Liu;Xiao Zhang;Jiangning Yang

  • Predicting winter wheat condition, grain yield and protein content using multi‐temporal EnviSat‐ASAR and Landsat TM satellite images

    Liangyun Liu;Jihua Wang;Yansong Bao;Wenjiang Huang

  • Estimating winter wheat plant water content using red edge parameters

    Liangyun Liu;Jihua Wang;Wenjiang Huang;Chunjiang Zhao

  • The delineation of agricultural management zones with high resolution remotely sensed data

    Xiaoyu J. Song;Xiaoyu J. Song;Jihua Wang;Wenjiang Huang;Liangyun Liu

  • Detecting solar-induced chlorophyll fluorescence from field radiance spectra based on the Fraunhofer line principle

    Liangyun Liu;Yongjiang Zhang;Jihua Wang;Chunjiang Zhao

  • Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset

    Yuan Gao;Liangyun Liu;Xiao Zhang;Xidong Chen

  • Response of Canopy Solar-Induced Chlorophyll Fluorescence to the Absorbed Photosynthetically Active Radiation Absorbed by Chlorophyll

    Shanshan Du;Liangyun Liu;Xinjie Liu;Jiaochan Hu

  • Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach

    Xiao Zhang;Liangyun Liu;Xidong Chen;Shuai Xie

  • Scaling effects on spring phenology detections from MODIS data at multiple spatial resolutions over the contiguous United States

    Dailiang Peng;Xiaoyang Zhang;Bing Zhang;Liangyun Liu

  • Measurement and Analysis of Bidirectional SIF Emissions in Wheat Canopies

    Liangyun Liu;Xinjie Liu;Zhihui Wang;Bing Zhang

  • Comparison of two methods of the fusion of remote sensing images with fidelity of spectral information

    Cunjun Li;Liangyun Liu;Jihua Wang;Chunjiang Zhao

  • Modelling paddy rice yield using MODIS data.

    Dailiang Peng;Jingfeng Huang;Cunjun Li;Liangyun Liu

  • Evaluating the potential of MODIS satellite data to track temporal dynamics of autumn phenology in a temperate mixed forest

    Lingling Liu;Lingling Liu;Liang Liang;Mark D. Schwartz;Alison Donnelly

  • Monitoring the seasonal bare soil areas in Beijing using multitemporal TM images

    Wanhui Chen;Liangyun Liu;Chao Zhang;Jihua Wang

Frequent Co-Authors

Wenjiang Huang
Wenjiang Huang Chinese Academy of Sciences
Bing Zhang
Bing Zhang Chinese Academy of Sciences
Yuan Gao
Yuan Gao Northeastern University
Alfredo Huete
Alfredo Huete University of Technology Sydney
Le Yu
Le Yu Tsinghua University
Xiaoyang Zhang
Xiaoyang Zhang South Dakota State University
Zheng Niu
Zheng Niu Chinese Academy of Sciences
Weimin Ju
Weimin Ju Nanjing University
Holly Croft
Holly Croft University of Sheffield

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