Maosong Li

646 total citations
20 papers, 536 citations indexed

About

Maosong Li is a scholar working on Plant Science, Global and Planetary Change and Ecology. According to data from OpenAlex, Maosong Li has authored 20 papers receiving a total of 536 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Plant Science, 6 papers in Global and Planetary Change and 5 papers in Ecology. Recurrent topics in Maosong Li's work include Smart Agriculture and AI (6 papers), Remote Sensing in Agriculture (5 papers) and Environmental and Agricultural Sciences (5 papers). Maosong Li is often cited by papers focused on Smart Agriculture and AI (6 papers), Remote Sensing in Agriculture (5 papers) and Environmental and Agricultural Sciences (5 papers). Maosong Li collaborates with scholars based in China, Japan and United States. Maosong Li's co-authors include Ping Wang, Shuo Zhuang, Boran Jiang, Jun Wang, Guoqi He, Zhao Min, Jun Gao, Xiao Xuchang, Yao-Qing Tang and Zhihong Gong and has published in prestigious journals such as Tectonophysics, Environmental Science and Pollution Research and Computers and Electronics in Agriculture.

In The Last Decade

Maosong Li

18 papers receiving 520 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maosong Li China 11 247 165 139 82 74 20 536
Kurt Heil Germany 10 163 0.7× 65 0.4× 165 1.2× 41 0.5× 42 0.6× 15 485
Alessandra Pellegrino Italy 12 307 1.2× 74 0.4× 83 0.6× 9 0.1× 83 1.1× 28 497
R. Quarto Italy 12 93 0.4× 152 0.9× 90 0.6× 43 0.5× 17 0.2× 21 400
Peter Dokukin Russia 11 134 0.5× 38 0.2× 53 0.4× 28 0.3× 37 0.5× 32 310
Cinthia K. Johnson United States 9 128 0.5× 39 0.2× 119 0.9× 37 0.5× 28 0.4× 11 460
Shattri Mansor Malaysia 11 96 0.4× 19 0.1× 96 0.7× 13 0.2× 78 1.1× 23 298
Aiym Orynbaikyzy Germany 6 123 0.5× 13 0.1× 230 1.7× 19 0.2× 82 1.1× 7 353
Sergio Ruggieri Italy 12 135 0.5× 8 0.0× 151 1.1× 20 0.2× 58 0.8× 34 344
M. L. Adams Australia 13 245 1.0× 6 0.0× 253 1.8× 38 0.5× 91 1.2× 21 512
N. M. Milton United States 8 109 0.4× 9 0.1× 190 1.4× 75 0.9× 76 1.0× 16 348

Countries citing papers authored by Maosong Li

Since Specialization
Citations

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

Fields of papers citing papers by Maosong Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maosong Li

This figure shows the co-authorship network connecting the top 25 collaborators of Maosong Li. A scholar is included among the top collaborators of Maosong Li 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 Maosong Li. Maosong Li 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.
2.
Zhuang, Shuo, Ping Wang, Boran Jiang, & Maosong Li. (2020). Learned features of leaf phenotype to monitor maize water status in the fields. Computers and Electronics in Agriculture. 172. 105347–105347. 50 indexed citations
3.
Li, Wanyi, et al.. (2019). Identification and Classification of Maize Drought Stress Using Deep Convolutional Neural Network. Symmetry. 11(2). 256–256. 93 indexed citations
4.
Jiang, Boran, et al.. (2019). Leaf Counting with Multi-Scale Convolutional Neural Network Features and Fisher Vector Coding. Symmetry. 11(4). 516–516. 11 indexed citations
5.
Jiang, Boran, Ping Wang, Shuo Zhuang, Maosong Li, & Zhihong Gong. (2019). Drought Stress Detection in the Middle Growth Stage Of Maize Based On Gabor Filter and Deep Learning. 7751–7756. 12 indexed citations
6.
Jiang, Boran, et al.. (2018). Detection of maize drought based on texture and morphological features. Computers and Electronics in Agriculture. 151. 50–60. 35 indexed citations
7.
Wang, Chunyan, Darioush Alidoust, Akihiro Isoda, & Maosong Li. (2017). Suppressive effects of thermal-treated oyster shells on cadmium and copper translocation in maize plants. Environmental Science and Pollution Research. 24(23). 19347–19356. 6 indexed citations
8.
Zhuang, Shuo, Ping Wang, Boran Jiang, Maosong Li, & Zhihong Gong. (2017). Early detection of water stress in maize based on digital images. Computers and Electronics in Agriculture. 140. 461–468. 53 indexed citations
9.
Sui, Yue, et al.. (2013). [Characteristics and adaptation of seasonal drought in southern China under the background of climate change. V. Seasonal drought characteristics division and assessment in southern China].. PubMed. 24(10). 2917–25. 5 indexed citations
10.
Sui, Yue, et al.. (2013). [Characteristics and adaptation of seasonal drought in southern China under the background of climate change. III. Spatiotemporal characteristics of seasonal drought in southern China based on the percentage of precipitation anomalies].. PubMed. 24(2). 397–406. 9 indexed citations
11.
Sui, Yue, et al.. (2013). [Characteristics and adaptation of seasonal drought in southern China under the background of global climate change. IV. Spatiotemporal characteristics of drought for maize based on crop water deficit index].. PubMed. 24(9). 2590–8.
12.
Sui, Yue, et al.. (2012). [Characteristics and adaption of seasonal drought in southern China under the background of global climate change. II. Spatiotemporal characteristics of drought for wintering grain- and oil crops based on crop water deficit index].. PubMed. 23(9). 2467–76. 6 indexed citations
13.
Sui, Yue, et al.. (2012). [Characteristics and adaption of seasonal drought in southern China under the background of global climate change. I. Change characteristics of precipitation resource].. PubMed. 23(7). 1875–82. 5 indexed citations
14.
Liu, Buchun, et al.. (2010). Analysis of the Demand for Weather Index Agricultural Insurance on Household level in Anhui, China. Agriculture and Agricultural Science Procedia. 1. 179–186. 22 indexed citations
15.
16.
Zhou, Wenlong, et al.. (2009). The Effects of 1,2,3,4-Butanetetracarboxylic Acid (BTCA) Finishing on the Color of Naturally Colored Cotton Fabrics. Research Journal of Textile and Apparel. 13(4). 26–33. 7 indexed citations
17.
Wang, Chunyan, Akihiro Isoda, Maosong Li, & Daolong Wang. (2007). Growth and Eco-Physiological Performance of Cotton Under Water Stress Conditions. Agricultural Sciences in China. 6(8). 949–955. 21 indexed citations
18.
Yan, Feng, Zhihao Qin, Maosong Li, & Wenjuan Li. (2006). Progress in soil moisture estimation from remote sensing data for for agricultural drought monitoring. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6366. 636601–636601. 14 indexed citations
19.
Li, Maosong, et al.. (2005). Cause analysis of frost damage to winter wheat in Huang-Huai-Hai plain during 2004-2005. 14(4). 51–55. 8 indexed citations
20.
Gao, Jun, Guoqi He, Maosong Li, et al.. (1995). The mineralogy, petrology, metamorphic PTDt trajectory and exhumation mechanism of blueschists, south Tianshan, northwestern China. Tectonophysics. 250(1-3). 151–168. 169 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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