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D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Environmental Sciences 48 5496 5269 561 557 134 9705

Bailang Yu publications per year

The chart shows the history of publications by Bailang Yu between 2006 and 2026, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Bailang Yu published across 21 years, from 2006 to 2026, averaging 8.3 papers a year. Output peaked at 20 publications in 2024. 21 of the 174 publications appeared in the last two years.

No. of publications
5 10 15 20
Bar chart. Horizontal axis: year, 2006 to 2026. Vertical axis: number of publications, 0 to 20. Peak 20 publications in 2024. 2006: 1 publication 2007: 0 publications 2008: 1 publication 2009: 2 publications 2010: 7 publications 2011: 5 publications 2012: 3 publications 2013: 6 publications 2014: 6 publications 2015: 11 publications 2016: 9 publications 2017: 5 publications 2018: 9 publications 2019: 12 publications 2020: 11 publications 2021: 12 publications 2022: 15 publications 2023: 18 publications 2024: 20 publications 2025: 20 publications 2026: 1 publication
2006 2026

174 publications in total across all disciplines

View publications per year as a table
Bailang Yu: publications per year, 2006 to 2026
Year Publications
2006 1
2007 0
2008 1
2009 2
2010 7
2011 5
2012 3
2013 6
2014 6
2015 11
2016 9
2017 5
2018 9
2019 12
2020 11
2021 12
2022 15
2023 18
2024 20
2025 20
2026 1
Total 174
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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.

No. of scientists
200 400 600
Bar chart with 66 bars. Horizontal axis: publications, 41–50 to 690+. Vertical axis: number of scientists, 0 to 617. Most scientists, 617, have 121–130 publications. The last bar groups every scientist with 690 publications or more. The highlighted bar, 131–140 publications, is where this scientist sits. 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
41–50 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.

View publications distribution as a table
Number of Environmental Sciences scientists by publication count, Research.com 2026 ranking edition. Based on 9,676 ranked scientists.
Publications Scientists This scientist
41–50 21
51–60 62
61–70 133
71–80 257
81–90 361
91–100 440
101–110 492
111–120 541
121–130 617
131–140 544 134
141–150 541
151–160 539
161–170 444
171–180 444
181–190 400
191–200 377
201–210 318
211–220 282
221–230 263
231–240 220
241–250 217
251–260 180
261–270 181
271–280 155
281–290 130
291–300 127
301–310 130
311–320 85
321–330 106
331–340 80
341–350 83
351–360 75
361–370 69
371–380 52
381–390 54
391–400 56
401–410 44
411–420 40
421–430 36
431–440 25
441–450 25
451–460 32
461–470 29
471–480 21
481–490 26
491–500 26
501–510 17
511–520 19
521–530 15
531–540 22
541–550 12
551–560 15
561–570 11
571–580 19
581–590 9
591–600 9
601–610 7
611–620 11
621–630 5
631–640 5
641–650 6
651–660 3
661–670 3
671–680 4
681–689 4
690+ 100
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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.

No. of scientists
100 200 300
Bar chart with 97 bars. Horizontal axis: D-Index, 30 to 126+. Vertical axis: number of scientists, 0 to 348. Most scientists, 348, have 45 D-Index. The last bar groups every scientist with 126 D-Index or more. The highlighted bar, 48 D-Index, is where this scientist sits. 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.

View D-Index distribution as a table
Number of Environmental Sciences scientists by D-index, Research.com 2026 ranking edition. Based on 9,676 ranked scientists.
D-Index Scientists This scientist
30 12
31 26
32 51
33 88
34 123
35 163
36 206
37 267
38 265
39 275
40 321
41 343
42 305
43 336
44 330
45 348
46 291
47 275
48 272 48
49 273
50 263
51 232
52 266
53 217
54 198
55 177
56 202
57 204
58 166
59 177
60 166
61 152
62 143
63 150
64 124
65 119
66 120
67 118
68 82
69 98
70 94
71 105
72 74
73 84
74 70
75 67
76 78
77 60
78 59
79 52
80 47
81 38
82 48
83 42
84 42
85 43
86 29
87 37
88 29
89 30
90 34
91 20
92 22
93 17
94 19
95 24
96 21
97 20
98 24
99 17
100 17
101 21
102 25
103 18
104 26
105 20
106 15
107 10
108 13
109 15
110 12
111 8
112 7
113 9
114 6
115 12
116 7
117 8
118 3
119 5
120 7
121 2
122 4
123 8
124 7
125 9
126+ 92
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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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