Xiaohe Gu

1.5k total citations
117 papers, 1.0k citations indexed

About

Xiaohe Gu is a scholar working on Ecology, Plant Science and Atmospheric Science. According to data from OpenAlex, Xiaohe Gu has authored 117 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Ecology, 41 papers in Plant Science and 38 papers in Atmospheric Science. Recurrent topics in Xiaohe Gu's work include Remote Sensing in Agriculture (62 papers), Remote Sensing and Land Use (36 papers) and Spectroscopy and Chemometric Analyses (27 papers). Xiaohe Gu is often cited by papers focused on Remote Sensing in Agriculture (62 papers), Remote Sensing and Land Use (36 papers) and Spectroscopy and Chemometric Analyses (27 papers). Xiaohe Gu collaborates with scholars based in China, United States and Fiji. Xiaohe Gu's co-authors include Qian Sun, Guijun Yang, Meiyan Shu, Yuchun Pan, Shu Cheng, Liping Chen, Yuntao Ma, Lin Sun, Tianen Chen and Chao Zhang and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Science of The Total Environment and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Xiaohe Gu

108 papers receiving 1.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Xiaohe Gu China 18 617 455 318 203 178 117 1.0k
Meichen Feng China 17 507 0.8× 464 1.0× 235 0.7× 310 1.5× 110 0.6× 93 1.0k
Chenwei Nie China 18 673 1.1× 641 1.4× 295 0.9× 219 1.1× 155 0.9× 60 1.1k
Luwei Feng China 15 510 0.8× 403 0.9× 238 0.7× 143 0.7× 231 1.3× 23 1.1k
Chengquan Zhou China 23 992 1.6× 818 1.8× 550 1.7× 311 1.5× 191 1.1× 39 1.5k
Jichong Han China 13 617 1.0× 507 1.1× 171 0.5× 98 0.5× 180 1.0× 29 990
Shanyu Huang China 17 1.1k 1.7× 762 1.7× 616 1.9× 197 1.0× 132 0.7× 26 1.4k
Reddy Pullanagari New Zealand 19 474 0.8× 304 0.7× 238 0.7× 330 1.6× 81 0.5× 44 1.0k
Yaping Cai United States 11 932 1.5× 624 1.4× 415 1.3× 111 0.5× 223 1.3× 15 1.5k
Anna Chlingaryan Australia 10 481 0.8× 682 1.5× 176 0.6× 209 1.0× 127 0.7× 22 1.2k
Philippe Vigneault Canada 7 600 1.0× 441 1.0× 247 0.8× 159 0.8× 69 0.4× 13 749

Countries citing papers authored by Xiaohe Gu

Since Specialization
Citations

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

Fields of papers citing papers by Xiaohe Gu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaohe Gu

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaohe Gu. A scholar is included among the top collaborators of Xiaohe Gu 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 Xiaohe Gu. Xiaohe Gu 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
1.
Shu, Meiyan, Li Yang, Jibo Yue, et al.. (2025). A novel canopy water indicator for UAV imaging to monitor winter wheat water status. Smart Agricultural Technology. 12. 101160–101160. 1 indexed citations
2.
Li, Xiaolan, et al.. (2025). Detecting tasseling rate of breeding maize using UAV-based RGB images and STB-YOLO model. Smart Agricultural Technology. 11. 100893–100893. 3 indexed citations
3.
Liu, Xingyu, Xiaohe Gu, Xuqing Li, et al.. (2025). Dynamic mapping of dissolved oxygen in freshwater aquaculture ponds using UAV multispectral imagery. Ecological Informatics. 91. 103388–103388.
4.
Zhang, Mingzheng, et al.. (2025). A Nitrogen Application Decision-Making Scheme for Tobacco Growth Based on UAV Multispectral Imagery. SHILAP Revista de lepidopterología. 6.
5.
Gu, Xiaohe, et al.. (2024). Intelligent classification of maize straw types from UAV remote sensing images using DenseNet201 deep transfer learning algorithm. Ecological Indicators. 166. 112331–112331. 5 indexed citations
6.
Sun, Qian, et al.. (2024). A spectral index for estimating grain filling rate of winter wheat using UAV-based hyperspectral images. Computers and Electronics in Agriculture. 223. 109059–109059. 5 indexed citations
7.
Gu, Limin, Qian Sun, Mingzheng Zhang, et al.. (2024). Estimation of grain filling rate and thousand-grain weight of winter wheat (Triticum aestivum L.) using UAV-based multispectral images. European Journal of Agronomy. 159. 127258–127258. 6 indexed citations
8.
Chen, Tianen, et al.. (2024). A Novel Feature Construction Method for Tobacco Chlorophyll Estimation Based on Integral of UAV-Borne Hyperspectral Reflectance Curve. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–10.
9.
Chen, Tianen, et al.. (2023). UAV-borne hyperspectral estimation of nitrogen content in tobacco leaves based on ensemble learning methods. Computers and Electronics in Agriculture. 211. 108008–108008. 33 indexed citations
10.
Sun, Qian, Liping Chen, Xiaohe Gu, et al.. (2023). Estimation of canopy nitrogen nutrient status in lodging maize using unmanned aerial vehicles hyperspectral data. Ecological Informatics. 78. 102315–102315. 7 indexed citations
11.
Shu, Meiyan, et al.. (2023). A spectral decomposition method for estimating the leaf nitrogen status of maize by UAV-based hyperspectral imaging. Computers and Electronics in Agriculture. 212. 108100–108100. 36 indexed citations
12.
Sun, Qian, et al.. (2023). Hyperspectral estimation of maize (Zea mays L.) yield loss under lodging stress. Field Crops Research. 302. 109042–109042. 15 indexed citations
13.
Fu, Yuanyuan, Guijun Yang, Dandan Duan, et al.. (2020). Comparison analysis of spatial and spectral feature in vegetation classification based on AVIRIS hyperspectral image. SHILAP Revista de lepidopterología. 1 indexed citations
14.
Li, Xiaofang, et al.. (2020). The Study on Thickness Detection Technology of Reflective Thermal Insulation Coatings for Buildings Based on Hyperspectral Technology. Guangpuxue yu guangpu fenxi. 40(8). 2552–2557. 1 indexed citations
15.
Shu, Meiyan, et al.. (2018). High spectral inversion of winter wheat LAI based on new vegetation index.. Zhongguo nongye Kexue. 51(18). 3486–3496. 3 indexed citations
16.
Su, Zhongbin, et al.. (2015). Design and Implementation of Soil Nutrient Monitoring System Based on "3S" Technology. International Journal of Smart Home. 9(5). 153–164. 2 indexed citations
17.
Yang, Hao, Guijun Yang, Xiaohe Gu, et al.. (2014). Radar polarimetric response features and remote sensing monitoring of wheat lodging.. Nongye gongcheng xuebao. 30(7). 1–8. 12 indexed citations
18.
Gu, Xiaohe. (2011). Monitoring the Pattern of Crop Rotation Through Remote Sensing. Zhongguo tudi kexue. 2 indexed citations
19.
Wang, Jihua, et al.. (2009). Estimation of crop yield based on weight optimization combination and multi-temporal remote sensing data.. Nongye gongcheng xuebao. 25(9). 137–142. 5 indexed citations
20.
Li, Ma, et al.. (2009). Remote sensing measurement of corn planting area based on field-data.. Nongye gongcheng xuebao. 25(8). 147–151. 7 indexed citations

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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