Jay Gao

137 papers receiving 6.5k citations

Hit Papers

A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023 2024 · 56 citations
56200320262010201850010001.5k

Peers

Jay Gao
Comparison fields: 5 of 145
  • Global and Planetary Change 3.0k
  • Environmental Engineering 2.0k
  • Ecology 2.4k
  • Atmospheric Science 1.6k
  • Media Technology 700
Replace Dafang Zhuang with:
Dafang Zhuang China
V. K. Dadhwal India
Xiao‐Peng Song United States
Heiko Balzter United Kingdom
Zongming Wang China
Yuanwei Qin United States
Kaishan Song China
Jiyuan Liu China
Nicholas Clinton United States
Claudia Kuenzer Germany
Jay Gao relative to Dafang Zhuang China Dafang Zhuang's profile →
Citations per field
00.5×1.5×2.3×
Dafang Zhuang · 1×
Citations per year

Countries citing papers authored by Jay Gao

Since Specialization
Citations

This map shows the geographic impact of Jay Gao'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 Jay Gao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jay Gao more than expected).

Fields of papers citing papers by Jay Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jay Gao. 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 Jay Gao. The network helps show where Jay Gao may publish in the future.

Co-authors

The 25 scholars most cited alongside Jay Gao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jay Gao Line = papers co-authored together Jay Gao links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 141 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Use of normalized difference built-up index in automatically mapping urban areas from TM imagery
Hit paper breakdown →
20031959
2 2011320
3 2009288
4 2017231
5 2015219
6 2016196
7 2009171
8 2014159
9 1997150
10 2003134
11 2010105
12 2004101
13 199798
14 199998
15 200397
16 201996
17 200677
18 200571
19 202170
20 200167

About Jay Gao

Jay Gao is a scholar working on Global and Planetary Change, Atmospheric Science, Ecology, Environmental Engineering and Management, Monitoring, Policy and Law, having authored 141 papers that have together received 6.8k indexed citations. Recurring topics across this work include Land Use and Ecosystem Services (39 papers), Remote Sensing in Agriculture (29 papers), Coastal wetland ecosystem dynamics (21 papers), Remote Sensing and Land Use (20 papers), Remote Sensing and LiDAR Applications (16 papers), Rangeland Management and Livestock Ecology (15 papers), Atmospheric chemistry and aerosols (11 papers) and Air Quality and Health Impacts (10 papers). The work is most often cited by research in Global and Planetary Change (3.0k citations), Environmental Engineering (2.0k citations), Ecology (2.4k citations), Atmospheric Science (1.6k citations) and Media Technology (700 citations). Jay Gao has collaborated with scholars based in New Zealand, China and Australia. Frequent co-authors include Yong Zha, Shaoxiang Ni, Yansui Liu, Fenghe Wang, Xilai Li, Gary Brierley, Tingting Xu, Yan Qiao, Yun Yang and Giovanni Coco. Their work appears in journals such as International Journal of Remote Sensing, Progress in Physical Geography Earth and Environment, Photogrammetric Engineering & Remote Sensing, CATENA and Applied Geography.

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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