World's Best Scientists 2026 revealed!

D-Index & Metrics

Environmental Sciences

D-Index
48
Citations
9705
World Ranking
5496
National Ranking
561

Bailang Yu 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 Bailang Yu 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: 282 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: 26 scientists 501–510 publications: 17 scientists 511–520 publications: 19 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 681–689 publications: 4 scientists 690+ publications: 100 scientists
41 publications 690+

This scientist: 134 publications — 32nd percentile

32% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 690 publications or more.

Bailang Yu 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 Bailang Yu 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: 198 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: 20 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: 9 scientists 126+ D-Index: 92 scientists
30 D-Index 126+

This scientist: 48 D-Index — 44th percentile

44% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 126 D-Index or more.

Overview

Bailang Yu is affiliated with East China Normal University in China. Their research primarily focuses on environmental science, with substantial work in subfields such as global and planetary change, atmospheric science, environmental engineering, transportation, and ecology.

The scientist has contributed extensively to topics including the impact of light on environment and health, land use and ecosystem services, urban heat island mitigation, urban transport and accessibility, remote sensing in agriculture, cryospheric studies and observations, and urban green space and health.

Frequent publication venues for Bailang Yu include:

  • Harvard Dataverse
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • Remote Sensing
  • IEEE Transactions on Geoscience and Remote Sensing
  • Remote Sensing of Environment

The scientist has collaborated repeatedly with several co-authors, reflecting a broad and consistent network within their research community. Frequent co-authors include:

  • Zuoqi Chen
  • Bin Wu
  • Jianping Wu
  • Chengshu Yang
  • Yuyu Zhou

Selected recent publications by Bailang Yu illustrate the scope of their research interests:

  • "An extended time series (2000-2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration", 2021, Earth System Science Data
  • "Exploring the relationship between 2D/3D landscape pattern and land surface temperature based on explainable eXtreme Gradient Boosting tree: A case study of Shanghai, China", 2020, The Science of The Total Environment
  • "Identifying and evaluating poverty using multisource remote sensing and point of interest (POI) data: A case study of Chongqing, China", 2020, Journal of Cleaner Production
  • "Effects of urban forms on CO2 emissions in China from a multi-perspective analysis", 2020, Journal of Environmental Management
  • "The potential of nighttime light remote sensing data to evaluate the development of digital economy: A case study of China at the city level", 2021, Computers Environment and Urban Systems

Best Publications

  • Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data

    Kaifang Shi;Bailang Yu;Yixiu Huang;Yingjie Hu

  • An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration

    Zuoqi Chen;Bailang Yu;Chengshu Yang;Yuyu Zhou

  • Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas

    Kaifang Shi;Chang Huang;Bailang Yu;Bing Yin

  • Extracting and understanding urban areas of interest using geotagged photos

    Yingjie Hu;Song Gao;Krzysztof Janowicz;Bailang Yu

  • Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives

    Min Zhao;Yuyu Zhou;Xuecao Li;Wenting Cao

  • Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis

    Kaifang Shi;Kaifang Shi;Yun Chen;Bailang Yu;Tingbao Xu

  • Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data

    Kaifang Shi;Kaifang Shi;Yun Chen;Bailang Yu;Tingbao Xu

  • Poverty Evaluation Using NPP-VIIRS Nighttime Light Composite Data at the County Level in China

    Bailang Yu;Kaifang Shi;Yingjie Hu;Chang Huang

  • Automated derivation of urban building density information using airborne LiDAR data and object-based method

    Bailang Yu;Hongxing Liu;Jianping Wu;Yingjie Hu

  • A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data

    Bin Wu;Bailang Yu;Wenhui Yue;Song Shu

  • A New Approach for Detecting Urban Centers and Their Spatial Structure With Nighttime Light Remote Sensing

    Zuoqi Chen;Bailang Yu;Wei Song;Hongxing Liu

  • Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China

    Bailang Yu;Song Shu;Hongxing Liu;Wei Song

  • Exploring the relationship between 2D/3D landscape pattern and land surface temperature based on explainable eXtreme Gradient Boosting tree: A case study of Shanghai, China.

    Siyi Yu;Zuoqi Chen;Bailang Yu;Lei Wang

  • Spatiotemporal variations of CO2 emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels

    Kaifang Shi;Bailang Yu;Yuyu Zhou;Yun Chen

  • Estimation of Poverty Using Random Forest Regression with Multi-Source Data: A Case Study in Bangladesh

    Xizhi Zhao;Bailang Yu;Yan Liu;Zuoqi Chen

  • View-based greenery: A three-dimensional assessment of city buildings' green visibility using Floor Green View Index

    Siyi Yu;Bailang Yu;Wei Song;Bin Wu

  • Mapping annual urban dynamics (1985–2015) using time series of Landsat data

    Xuecao Li;Yuyu Zhou;Zhengyuan Zhu;Lu Liang

  • Estimating House Vacancy Rate in Metropolitan Areas Using NPP-VIIRS Nighttime Light Composite Data

    Zuoqi Chen;Bailang Yu;Yingjie Hu;Chang Huang

  • Urban Built-Up Area Extraction From Log- Transformed NPP-VIIRS Nighttime Light Composite Data

    Bailang Yu;Min Tang;Qiusheng Wu;Chengshu Yang

  • Normalization of time series DMSP-OLS nighttime light images for urban growth analysis with Pseudo Invariant Features

    Ye Wei;Hongxing Liu;Wei Song;Bailang Yu

Frequent Co-Authors

Jianping Wu
Jianping Wu East China Normal University
Hongxing Liu
Hongxing Liu University of Alabama
Yuyu Zhou
Yuyu Zhou Iowa State University
Kenneth M. Hinkel
Kenneth M. Hinkel Michigan Technological University
Krzysztof Janowicz
Krzysztof Janowicz University of California, Santa Barbara
Frédéric Frappart
Frédéric Frappart Bordeaux Sciences Agro
Xia Li
Xia Li East China Normal University
Xuecao Li
Xuecao Li China Agricultural University
Christopher Small
Christopher Small Lamont-Doherty Earth Observatory
Christopher D. Elvidge
Christopher D. Elvidge Colorado School of Mines

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