World's Best Scientists 2026 revealed!

D-Index & Metrics

Environmental Sciences

D-Index
38
Citations
7542
World Ranking
8544
National Ranking
22

Moses Azong Cho 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 Moses Azong Cho 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: 132 publications — 31st percentile

31% of scientists in this discipline score the same or lower.

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

Moses Azong Cho 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 Moses Azong Cho 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: 38 D-Index — 12th percentile

12% of scientists in this discipline score the same or lower.

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

Overview

Moses Azong Cho is affiliated with the Council for Scientific and Industrial Research in South Africa. Their research primarily focuses on Environmental Science, with a concentration on various subfields including Ecology, Environmental Engineering, Global and Planetary Change, Plant Science, and Ecological Modeling.

Their scientific contributions extensively cover topics related to Remote Sensing in Agriculture, Land Use and Ecosystem Services, Species Distribution and Climate Change, Leaf Properties and Growth Measurement, Urban Heat Island Mitigation, Remote Sensing and Land Use, as well as Remote Sensing and LiDAR Applications.

Key frequent co-authors collaborating with Moses Azong Cho include Abel Ramoelo, Cecilia Masemola, Laven Naidoo, Sabelo Madonsela, and Russell Main.

The scientist has published in a variety of academic venues, with notable frequent publication outlets being the SSRN Electronic Journal, International Journal of Remote Sensing, Geocarto International, International Journal of Applied Earth Observation and Geoinformation, and the Journal of Spatial Science.

The following notable research papers represent a selection of Moses Azong Cho's published works:

  • Sentinel-2 time series based optimal features and time window for mapping invasive Australian native Acacia species in KwaZulu Natal, South Africa, 2020, International Journal of Applied Earth Observation and Geoinformation
  • Towards a semi-automated mapping of Australia native invasive alien Acacia trees using Sentinel-2 and radiative transfer models in South Africa, 2020, ISPRS Journal of Photogrammetry and Remote Sensing
  • Exploring the utility of Sentinel-2 for estimating maize chlorophyll content and leaf area index across different growth stages, 2021, Journal of Spatial Science
  • Comparison between Sentinel-2 and WorldView-3 sensors in mapping wetland vegetation communities of the Grassland Biome of South Africa, for monitoring under climate change, 2022, Remote Sensing Applications Society and Environment
  • Theoretical Study on the Degree of CO2 Activation in CO2-Coordinated Ni(0) Complexes, 2021, ACS Omega

Best Publications

  • High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm

    Onisimo Mutanga;Elhadi Adam;Moses Azong Cho

  • A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method

    Moses Azong Cho;Andrew K. Skidmore

  • LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements

    Roshanak Darvishzadeh;Andrew Skidmore;Martin Schlerf;Clement Atzberger

  • Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression

    Moses Azong Cho;Moses Azong Cho;Andrew K. Skidmore;Andrew K. Skidmore;Fabio Corsi;Sipke E. van Wieren

  • An investigation into robust spectral indices for leaf chlorophyll estimation

    Russell Main;Moses Azong Cho;Renaud Mathieu;Martha M. O’Kennedy

  • Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment

    L Naidoo;Moses A Cho;Renaud Sa Mathieu;G Asner

  • Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions

    Nathalie Pettorelli;Martin Wegmann;Martin Wegmann;Andrew Skidmore;Sander Mucher

  • Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system

    Moses Azong Cho;Renaud Mathieu;Gregory P. Asner;Laven Naidoo

  • Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor

    Abel Ramoelo;Abel Ramoelo;Andrew K. Skidmore;Moses Azong Cho;Martin Schlerf

  • Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data

    Abel Ramoelo;Abel Ramoelo;Moses Azong Cho;Renaud Mathieu;S. Madonsela

  • Improving Discrimination of Savanna Tree Species Through a Multiple-Endmember Spectral Angle Mapper Approach: Canopy-Level Analysis

    M A Cho;P Debba;R Mathieu;L Naidoo

  • Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data

    Abel Ramoelo;Abel Ramoelo;AK Skidmore;Moses A Cho;Renaud Sa Mathieu

  • Potential of Sentinel-2 spectral configuration to assess rangeland quality

    Abel Ramoelo;Abel Ramoelo;Moses Cho;Moses Cho;Renaud Mathieu;Renaud Mathieu;Andrew K. Skidmore

  • Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image

    Samuel Adelabu;Onisimo Mutanga;Elhadi E. Adam;Moses Azong Cho

  • Model-Based Integrated Methods for Quantitative Estimation of Soil Salinity from Hyperspectral Remote Sensing Data: A Case Study of Selected South African Soils

    Unknown

  • Multi-phenology WorldView-2 imagery improves remote sensing of savannah tree species

    Sabelo Madonsela;Sabelo Madonsela;Moses Azong Cho;Moses Azong Cho;Renaud Mathieu;Renaud Mathieu;Onisimo Mutanga

  • Remote sensing of species diversity using Landsat 8 spectral variables

    Sabelo Madonsela;Sabelo Madonsela;Moses Azong Cho;Moses Azong Cho;Moses Azong Cho;Abel Ramoelo;Abel Ramoelo;Abel Ramoelo;Onisimo Mutanga

  • Towards red-edge positions less sensitive to canopy biophysical parameters for leaf chlorophyll estimation using properties optique spectrales des feuilles (PROSPECT) and scattering by arbitrarily inclined leaves (SAILH) simulated data

    M. A. Cho;A. K. Skidmore;C. Atzberger

  • Assessing the utility WorldView-2 imagery for tree species mapping in South African subtropical humid forest and the conservation implications: Dukuduku forest patch as case study

    Moses Azong Cho;Moses Azong Cho;Oupa Malahlela;Abel Ramoelo

  • Toward structural assessment of semi-arid African savannahs and woodlands: The potential of multitemporal polarimetric RADARSAT-2 fine beam images

    Renaud Mathieu;Laven Naidoo;Moses A. Cho;Brigitte Leblon

  • Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella National Park, Italy

    M. A. Cho;A. K. Skidmore

Frequent Co-Authors

Renaud Mathieu
Renaud Mathieu International Rice Research Institute
Onisimo Mutanga
Onisimo Mutanga University of KwaZulu-Natal
Andrew K. Skidmore
Andrew K. Skidmore University of Twente
Gregory P. Asner
Gregory P. Asner Arizona State University
Martin Schlerf
Martin Schlerf Luxembourg Institute of Science and Technology
Konrad J Wessels
Konrad J Wessels George Mason University
Clement Atzberger
Clement Atzberger BOKU University
Roshanak Darvishzadeh
Roshanak Darvishzadeh University of Twente
Eren Turak
Eren Turak Office of Environment and Heritage
Martin Wegmann
Martin Wegmann University of Würzburg

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