World's Best Scientists 2026 revealed!

D-Index & Metrics

Environmental Sciences

D-Index
53
Citations
10115
World Ranking
4264
National Ranking
1600

Kenneth J. Ranson 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 Kenneth J. Ranson 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: 194 publications — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Kenneth J. Ranson 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 Kenneth J. Ranson 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: 53 D-Index — 57th percentile

57% of scientists in this discipline score the same or lower.

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

Overview

What is he best known for?

The fields of study he is best known for:

  • Ecology
  • Remote sensing
  • Meteorology

His scientific interests lie mostly in Remote sensing, Lidar, Synthetic aperture radar, Canopy and Backscatter. Kenneth J. Ranson studies Radiance which is a part of Remote sensing. His biological study spans a wide range of topics, including Tree canopy, Altimeter, Shuttle Radar Topography Mission and Imaging spectrometer.

His biological study spans a wide range of topics, including Radar imaging and Digital elevation model. As part of the same scientific family, Kenneth J. Ranson usually focuses on Canopy, concentrating on Taiga and intersecting with Ecotone and Tundra. His study in Backscatter is interdisciplinary in nature, drawing from both Productivity, Atmospheric sciences and C band.

His most cited work include:

  • Forest vertical structure from GLAS : An evaluation using LVIS and SRTM data (229 citations)
  • Mapping biomass of a northern forest using multifrequency SAR data (192 citations)
  • NASA Goddards LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager (191 citations)

What are the main themes of his work throughout his whole career to date?

The scientist’s investigation covers issues in Remote sensing, Taiga, Synthetic aperture radar, Ecotone and Vegetation. His Remote sensing research includes themes of Canopy and Meteorology. His Taiga study integrates concerns from other disciplines, such as Elevation, Boreal and Physical geography.

His Synthetic aperture radar research incorporates themes from Biomass, Radar imaging, Atmospheric sciences and Hydrology. His Ecotone research is multidisciplinary, relying on both Crown closure, Tundra and Tree line. His Vegetation research integrates issues from Nadir, Satellite imagery, Deciduous and Radiance.

He most often published in these fields:

  • Remote sensing (60.48%)
  • Taiga (28.23%)
  • Synthetic aperture radar (24.19%)

What were the highlights of his more recent work (between 2010-2021)?

  • Remote sensing (60.48%)
  • Taiga (28.23%)
  • Ecotone (17.74%)

In recent papers he was focusing on the following fields of study:

Kenneth J. Ranson mostly deals with Remote sensing, Taiga, Ecotone, Lidar and Larch. He is interested in Synthetic aperture radar, which is a field of Remote sensing. Kenneth J. Ranson has included themes like Boreal, Tundra and Satellite imagery in his Taiga study.

His studies in Ecotone integrate themes in fields like Tree line and Physical geography. His study explores the link between Lidar and topics such as Interferometric synthetic aperture radar that cross with problems in Remote sensing and Stereo imagery. Kenneth J. Ranson interconnects Permafrost and Transect in the investigation of issues within Larch.

Between 2010 and 2021, his most popular works were:

  • NASA Goddards LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager (191 citations)
  • Wildfires Dynamics in Siberian Larch Forests (54 citations)
  • Achieving accuracy requirements for forest biomass mapping: A spaceborne data fusion method for estimating forest biomass and LiDAR sampling error (44 citations)

In his most recent research, the most cited papers focused on:

  • Ecology
  • Remote sensing
  • Meteorology

Kenneth J. Ranson mainly focuses on Remote sensing, Lidar, Taiga, Vegetation and Ecotone. Remote sensing and Thematic map are commonly linked in his work. The concepts of his Lidar study are interwoven with issues in Synthetic aperture radar and Meteorology.

His research in Taiga tackles topics such as Boreal which are related to areas like Tundra, Polygon and Tree canopy. Kenneth J. Ranson works mostly in the field of Vegetation, limiting it down to concerns involving Elevation and, occasionally, Interferometric synthetic aperture radar, Deciduous and Photogrammetry. The study incorporates disciplines such as Physical geography and Climate change, Tree line in addition to Ecotone.

Best Publications

  • The Boreal Ecosystem-Atmosphere Study (BOREAS) : an overview and early results from the 1994 field year

    Piers Sellers;Forrest Hall;K. Jon Ranson;Hank Margolis

  • BOREAS in 1997: Experiment overview, scientific results, and future directions

    Piers J. Sellers;Forrest G. Hall;Robert D. Kelly;Andrew Black

  • Forest vertical structure from GLAS : An evaluation using LVIS and SRTM data

    G. Sun;K.J. Ranson;D.S. Kimes;J.B. Blair

  • NASA Goddards LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager

    Bruce D. Cook;Lawrence A. Corp;Ross F. Nelson;Elizabeth M. Middleton

  • Validation of surface height from shuttle radar topography mission using shuttle laser altimeter

    G Sun;K.J Ranson;V.I Kharuk;K Kovacs

  • Mapping biomass of a northern forest using multifrequency SAR data

    K.J. Ranson;Guoqing Sun

  • Modeling lidar returns from forest canopies

    G. Sun;K.J. Ranson

  • Radar modeling of a boreal forest

    N.S. Chauhan;R.H. Lang;K.J. Ranson

  • A new technique to measure the spectral properties of conifer needles

    C.S.T. Daughtry;L.L. Biehl;K.J. Ranson

  • Forest biomass mapping from lidar and radar synergies

    Guoqing Sun;K. Jon Ranson;Z. Guo;Z. Zhang

  • Estimating Siberian timber volume using MODIS and ICESat/GLAS.

    R. Nelson;K.J. Ranson;G. Sun;D.S. Kimes

  • Imaging radar for ecosystem studies

    Richard H. Waring;JoBea Way;E. Raymond J. Hunt;Leslie Morrissey

  • Wildfires Dynamics in Siberian Larch Forests

    Evgenii I. Ponomarev;Viacheslav I. Kharuk;Kenneth J. Ranson

  • An off-nadir-pointing imaging spectroradiometer for terrestrial ecosystem studies

    J.R. Irons;K.J. Ranson;D.L. Williams;R.R. Irish

  • Sun-View Angle Effects on Reflectance Factors of Corn Canopies

    K.J. Ranson;C.S.T. Daughtry;L.L. Biehl;M.E. Bauer

  • Earth Observing System AM1 mission to Earth

    Y.J. Kaufman;D.D. Herring;K.J. Ranson;G.J. Collatz

  • Fusion of imaging spectrometer and LIDAR data over combined radiative transfer models for forest canopy characterization

    Benjamin Koetz;Guoqing Sun;Felix Morsdorf;K.J. Ranson

  • A three-dimensional radar backscatter model of forest canopies

    Guoqing Sun;K. Jon Ranson

  • Radiometric slope correction for forest biomass estimation from SAR data in the Western Sayani Mountains, Siberia

    G Sun;K.J Ranson;V.I Kharuk

  • Boreal forest ecosystem characterization with SIR-C/XSAR

    K.J. Ranson;S. Saatchi;Guoqing Sun

Frequent Co-Authors

Guoqing Sun
Guoqing Sun University of Maryland, College Park
Ross Nelson
Ross Nelson Goddard Space Flight Center
Daniel S. Kimes
Daniel S. Kimes Goddard Space Flight Center
Ralph Dubayah
Ralph Dubayah University of Maryland, College Park
Bruce D. Cook
Bruce D. Cook Goddard Space Flight Center
Jeffrey G. Masek
Jeffrey G. Masek Goddard Space Flight Center
Craig S. T. Daughtry
Craig S. T. Daughtry Agricultural Research Service
John F. Weishampel
John F. Weishampel University of Central Florida
David J. Harding
David J. Harding Goddard Space Flight Center
Felix Morsdorf
Felix Morsdorf University of Zurich

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