Shilong Ren

1.1k total citations
36 papers, 893 citations indexed

About

Shilong Ren is a scholar working on Global and Planetary Change, Ecology and Ecological Modeling. According to data from OpenAlex, Shilong Ren has authored 36 papers receiving a total of 893 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Global and Planetary Change, 24 papers in Ecology and 8 papers in Ecological Modeling. Recurrent topics in Shilong Ren's work include Remote Sensing in Agriculture (24 papers), Plant Water Relations and Carbon Dynamics (15 papers) and Land Use and Ecosystem Services (10 papers). Shilong Ren is often cited by papers focused on Remote Sensing in Agriculture (24 papers), Plant Water Relations and Carbon Dynamics (15 papers) and Land Use and Ecosystem Services (10 papers). Shilong Ren collaborates with scholars based in China, United States and Switzerland. Shilong Ren's co-authors include Shuhua Yi, Matthias Peichl, Xiaoqiu Chen, Shuai An, Qiming Qin, Qin Yu, Huazhong Ren, Jianjun Chen, Xiaoyun Wang and Yating Li and has published in prestigious journals such as The Science of The Total Environment, Agricultural and Forest Meteorology and Remote Sensing.

In The Last Decade

Shilong Ren

33 papers receiving 884 citations

Peers

Shilong Ren

ECOLO

GPC

Zhoutao Zheng China

Wenping Kang China

Zhengfang Wu China

Shengwei Zong China

Torben Riis‐Nielsen Denmark

Hyeon‐Ju Gim South Korea

Aaron B. Berdanier United States

Dawei Xu China

Zhengguo Sun China

Xuanran Li China

Zhoutao Zheng China

Shilong Ren

480 ×0.9

ECOLO

665 ×1.3

GPC

250 ×1.0

142 ×0.8

98 ×0.7

Citations per year, relative to Shilong Ren Shilong Ren (= 1×) peers Zhoutao Zheng

Countries citing papers authored by Shilong Ren

Since Specialization

Citations

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

Fields of papers citing papers by Shilong Ren

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shilong Ren

This figure shows the co-authorship network connecting the top 25 collaborators of Shilong Ren. A scholar is included among the top collaborators of Shilong Ren 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 Shilong Ren. Shilong Ren is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Ren, Shilong, et al.. (2025). Improved CASA-Based Net Ecosystem Productivity Estimation in China by Incorporating Developmental Factors into Autumn Phenology Model. Remote Sensing. 17(3). 487–487. 3 indexed citations

Bai, Xiaohui, Changzhi Yang, Lei Fang, et al.. (2025). Identification of Salt Marsh Vegetation in the Yellow River Delta Using UAV Multispectral Imagery and Deep Learning. Drones. 9(4). 235–235.

Fang, Lei, et al.. (2025). Drivers of Structural and Functional Resilience Following Extreme Fires in Boreal Forests of Northeast China. Fire. 8(3). 108–108. 1 indexed citations

Ren, Shilong, Xiaoyang Zhang, Zhaoliang Gao, et al.. (2025). The role of developmental and climate factors in driving autumn phenology across the Northern Hemisphere. Agricultural and Forest Meteorology. 368. 110548–110548.

Chen, Jinyue, Chongyang Wang, Shuisen Chen, et al.. (2025). Hyperspectral modeling of colored dissolved organic matter (CDOM): Insights from machine learning and band ratio models. Estuarine Coastal and Shelf Science. 327. 109567–109567.

Wang, Wanting, Guoqiang Wang, Jie Li, et al.. (2025). Remote sensing identification and model-based prediction of harmful algal blooms in inland waters: Current insights and future perspectives. Water Research X. 28. 100369–100369. 2 indexed citations

An, Shuai, Xiaoqiu Chen, Xuhui Wang, et al.. (2024). Long-term species-level observations indicate the critical role of soil moisture in regulating China's grassland productivity relative to phenological and climatic factors. The Science of The Total Environment. 929. 172553–172553. 7 indexed citations

Xie, Gang, Xiaohui Bai, Yanbo Peng, et al.. (2024). Aquaculture Ponds Identification Based on Multi-Feature Combination Strategy and Machine Learning from Landsat-5/8 in a Typical Inland Lake of China. Remote Sensing. 16(12). 2168–2168. 4 indexed citations

Li, Xuan, Lei Fang, Yanbo Peng, et al.. (2024). Disentangling the Influential Factors Driving NPP Decrease in Shandong Province: An Analysis from Time Series Evaluation Using MODIS and CASA Model. Remote Sensing. 16(11). 1966–1966. 7 indexed citations

10.

Guan, Xu, Qingzhu Zhang, Lei Li, et al.. (2023). A machine learning-based study on the impact of COVID-19 on three kinds of pollution in Beijing-Tianjin-Hebei region. The Science of The Total Environment. 884. 163190–163190. 5 indexed citations

11.

Ren, Shilong & Matthias Peichl. (2021). Enhanced spatiotemporal heterogeneity and the climatic and biotic controls of autumn phenology in northern grasslands. The Science of The Total Environment. 788. 147806–147806. 33 indexed citations

12.

Ji, Shuping, et al.. (2021). The response of net primary productivity to climate change and its impact on hydrology in a water-limited agricultural basin. Environmental Science and Pollution Research. 29(7). 10277–10290. 8 indexed citations

13.

Ji, Shuping, Shilong Ren, Junyu Dong, et al.. (2020). Diverse responses of spring phenology to preseason drought and warming under different biomes in the North China Plain. The Science of The Total Environment. 766. 144437–144437. 55 indexed citations

14.

Ren, Shilong, Qiming Qin, Huazhong Ren, Juan Sui, & Yao Zhang. (2019). Heat and Drought Stress Advanced Global Wheat Harvest Timing from 1981–2014. Remote Sensing. 11(8). 971–971. 12 indexed citations

15.

Ren, Shilong, Qiming Qin, Huazhong Ren, Juan Sui, & Yao Zhang. (2019). New model for simulating autumn phenology of herbaceous plants in the Inner Mongolian Grassland. Agricultural and Forest Meteorology. 275. 136–145. 23 indexed citations

16.

Ren, Shilong, Qiming Qin, & Huazhong Ren. (2019). Contrasting wheat phenological responses to climate change in global scale. The Science of The Total Environment. 665. 620–631. 56 indexed citations

17.

Zhang, Yao, Qiming Qin, Huazhong Ren, et al.. (2018). Optimal Hyperspectral Characteristics Determination for Winter Wheat Yield Prediction. Remote Sensing. 10(12). 2015–2015. 35 indexed citations

18.

Chen, Xiaoqiu, et al.. (2017). Temporal coherence of phenological and climatic rhythmicity in Beijing. International Journal of Biometeorology. 61(10). 1733–1748. 7 indexed citations

19.

Ren, Shilong, Xiaoqiu Chen, & Shuai An. (2016). Assessing plant senescence reflectance index-retrieved vegetation phenology and its spatiotemporal response to climate change in the Inner Mongolian Grassland. International Journal of Biometeorology. 61(4). 601–612. 80 indexed citations

20.

Qin, Yu, Shuhua Yi, Shilong Ren, Nan Li, & Jianjun Chen. (2013). Responses of typical grasslands in a semi-arid basin on the Qinghai-Tibetan Plateau to climate change and disturbances. Environmental Earth Sciences. 71(3). 1421–1431. 64 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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