World's Best Scientists 2026 revealed!

D-Index & Metrics

Environmental Sciences

D-Index
44
Citations
6253
World Ranking
6852
National Ranking
2440

Lin Li 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 Lin Li 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: 113 publications — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Lin Li 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 Lin Li 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: 44 D-Index — 32nd percentile

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

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

Overview

Lin Li is a researcher affiliated with Indiana University - Purdue University Indianapolis in the United States. Their work predominantly focuses on Environmental Science, with a specialization in subfields such as Ecology, Global and Planetary Change, Oceanography, Industrial and Manufacturing Engineering, and Environmental Chemistry.

Their research outputs span various topics including:

  • Marine and coastal ecosystems
  • Water Quality Monitoring and Analysis
  • Land Use and Ecosystem Services
  • Aquatic Ecosystems and Phytoplankton Dynamics
  • Remote Sensing in Agriculture
  • Coastal wetland ecosystem dynamics
  • Water Quality Monitoring Technologies

Lin Li's frequent publication venues reflect a focus on environmental and remote sensing sciences. These include:

  • Remote Sensing of Environment
  • Environmental Science & Technology
  • Ecological Indicators
  • IEEE Transactions on Geoscience and Remote Sensing
  • Water

Prominent recent papers authored or co-authored by Lin Li include:

  • "National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images," 2020, published in ISPRS Journal of Photogrammetry and Remote Sensing
  • "GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality," 2023, published in Scientific Data
  • "Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters," 2020, published in Remote Sensing of Environment
  • "Advancing cyanobacteria biomass estimation from hyperspectral observations: Demonstrations with HICO and PRISMA imagery," 2021, published in Remote Sensing of Environment
  • "Water Table Fluctuations Regulate Hydrogen Peroxide Production and Distribution in Unconfined Aquifers," 2020, published in Environmental Science & Technology

Lin Li collaborates frequently with a network of co-authors, including:

  • Nima Pahlevan
  • Daniela Gurlin
  • Caren Binding
  • Stefan Simis
  • Kaishan Song

Best Publications

  • National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images

    Dehua Mao;Zongming Wang;Baojia Du;Lin Li

  • Predicting water content using Gaussian model on soil spectra

    Michael L Whiting;Lin Li;Susan L Ustin

  • Optical types of inland and coastal waters

    Evangelos Spyrakos;Ruth O'Donnell;Peter D. Hunter;Claire Miller

  • Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin

    Kaylan Randolph;Jeff Wilson;Lenore Tedesco;Lin Li

  • Rapid Invasion of Spartina alterniflora in the Coastal Zone of Mainland China: New Observations from Landsat OLI Images

    Mingyue Liu;Dehua Mao;Zongming Wang;Lin Li

  • Quantifying changes in multiple ecosystem services during 1992–2012 in the Sanjiang Plain of China

    Zongming Wang;Dehua Mao;Lin Li;Mingming Jia

  • Rapid Invasion of Spartina Alterniflora in the Coastal Zone of Mainland China: Spatiotemporal Patterns and Human Prevention

    Dehua Mao;Mingyue Liu;Mingyue Liu;Zongming Wang;Lin Li

  • Application of AVIRIS data in detection of oil-induced vegetation stress and cover change at Jornada, New Mexico

    Lin Li;Susan L. Ustin;Mui Lay

  • Remote estimation of chlorophyll-a in turbid inland waters: Three-band model versus GA-PLS model

    Kaishan Song;Lin Li;L.P. Tedesco;Shuai Li

  • Estimation of heavy-metal contamination in soil using reflectance spectroscopy and partial least-squares regression

    Caaminee M. Pandit;Gabriel M. Filippelli;Lin Li

  • Hyperspectral Remote Sensing of Total Phosphorus (TP) in Three Central Indiana Water Supply Reservoirs

    Kaishan Song;Lin Li;Shuai Li;Lenore Tedesco

  • Retrieval of Fresh Leaf Fuel Moisture Content Using Genetic Algorithm Partial Least Squares (GA-PLS) Modeling

    Lin Li;S.L. Ustin;D. Riano

  • Remote sensing of freshwater cyanobacteria: An extended IOP Inversion Model of Inland Waters (IIMIW) for partitioning absorption coefficient and estimating phycocyanin

    Linhai Li;Lin Li;Kaishan Song;Kaishan Song

  • Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters

    Nima Pahlevan;Brandon Smith;Caren Binding;Daniela Gurlin

  • An inversion model for deriving inherent optical properties of inland waters: Establishment, validation and application

    Linhai Li;Lin Li;Kaishan Song;Yunmei Li

  • Soil organic carbon in the Sanjiang Plain of China: storage, distribution and controlling factors

    D. H. Mao;Z. M. Wang;L. Li;Z. H. Miao

  • Mapping China's mangroves based on an object-oriented classification of Landsat imagery

    Mingming Jia;Zongming Wang;Lin Li;Kaishan Song

  • An OLCI-based algorithm for semi-empirically partitioning absorption coefficient and estimating chlorophyll a concentration in various turbid case-2 waters

    Ge Liu;Ge Liu;Lin Li;Kaishan Song;Yunmei Li

  • Assessment of habitat suitability for waterbirds in the West Songnen Plain, China, using remote sensing and GIS

    Zhangyu Dong;Zongming Wang;Dianwei Liu;Lin Li

  • Wetlands shrinkage, fragmentation and their links to agriculture in the Muleng-Xingkai Plain, China.

    Kaishan Song;Zongming Wang;Lin Li;Lenore Tedesco

  • Mapping Wetland Areas Using Landsat-Derived NDVI and LSWI: A Case Study of West Songnen Plain, Northeast China

    Zhangyu Dong;Zongming Wang;Dianwei Liu;Kaishan Song

  • Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources.

    Kaishan Song;Kaishan Song;Lin Li;Zongming Wang;Dianwei Liu

Frequent Co-Authors

Kaishan Song
Kaishan Song Liaocheng University
Zongming Wang
Zongming Wang Chinese Academy of Sciences
Dehua Mao
Dehua Mao Chinese Academy of Sciences
Susan L. Ustin
Susan L. Ustin University of California, Davis
Yunlin Zhang
Yunlin Zhang Chinese Academy of Sciences
Kun Shi
Kun Shi Chinese Academy of Sciences
Zhidan Wen
Zhidan Wen Northeast Institute of Geography and Agroecology
Ying Zhao
Ying Zhao Ludong University
Claudia Giardino
Claudia Giardino National Research Council (CNR)
Mariano Bresciani
Mariano Bresciani Istituto per il Rilevamento Elettromagnetico dell'Ambiente

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