M. Daigo

1.1k citations
25 papers · 266 indexed · h-index 10
Topics
Remote Sensing and Land Use (7 papers)Remote Sensing in Agriculture (6 papers)Remote-Sensing Image Classification (6 papers)
Partner nations
JapanChina

In The Last Decade

M. Daigo

24 papers receiving 244 citations

Peers

M. Daigo
Comparison fields: 5 of 57
  • Ecology 115
  • Atmospheric Science 80
  • Global and Planetary Change 75
  • Nuclear and High Energy Physics 65
  • Media Technology 64
Replace Manab Chakraborty with:
Manab Chakraborty India
G. S. Pankiewicz United Kingdom
J. Roehrig United States
E. O’Mongáin Ireland
K. T. Wiedemann Brazil
H. F. Lü China
Benoît Tremblay United States
M. E. Murphy United States
G. Chalon France
Lesley De Cruz Belgium
M. Daigo relative to Manab Chakraborty India Manab Chakraborty's profile →
Citations per field
00.5×3.6×
Manab Chakraborty · 1×
Citations per year

Countries citing papers authored by M. Daigo

Since Specialization
Citations

This map shows the geographic impact of M. Daigo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by M. Daigo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Daigo more than expected).

Fields of papers citing papers by M. Daigo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. Daigo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by M. Daigo. The network helps show where M. Daigo may publish in the future.

Co-authorship network of co-authors of M. Daigo

This figure shows the co-authorship network connecting the top 25 collaborators of M. Daigo. A scholar is included among the top collaborators of M. Daigo based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with M. Daigo. M. Daigo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1
Improvement of terrestrial GPP estimation algorithms using satellite and flux data
2
2
DEVELOPMENT OF VALIDATION DATA SETS FOR GLOBAL LAND COVER CLASSIFICATION USING ALOS/AVNIR-2 DATA
1
3 17
4 4
5 16
6 44
7 26
8
Land cover classification based on the universal pattern decomposition method
1
9 16
10 0
11 2
12 38
13 2
14 5
15
Craniometrical estimation of the native Japanese Mishima cattle, using multivariate analysis.
6
16 2
17 4
18 4
19 7
20 8

About M. Daigo

M. Daigo is a scholar working on Nuclear and High Energy Physics, Media Technology and Radiation, having authored 25 papers that have together received 266 indexed citations. Recurring topics across this work include Remote Sensing and Land Use (7 papers), Remote Sensing in Agriculture (6 papers) and Remote-Sensing Image Classification (6 papers). The work is most often cited by research in Media Technology (64 citations), Nuclear and High Energy Physics (65 citations) and Atmospheric Science (80 citations). M. Daigo has collaborated with scholars based in Japan and China. Frequent co-authors include Keiichi Muramatsu, N. Fujiwara, Lifu Zhang, Liangpei Zhang, F. Ochiai, Akemi Hayashi, Liangpei Zhang, Akira Ono, Pingxiang Li and K. Maeda. Their work appears in journals such as Nuclear Physics B, International Journal of Remote Sensing and Physics Letters A.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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