Jing M Chen

1.6k total citations
18 papers, 661 citations indexed

About

Jing M Chen is a scholar working on Ecology, Global and Planetary Change and Environmental Engineering. According to data from OpenAlex, Jing M Chen has authored 18 papers receiving a total of 661 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Ecology, 13 papers in Global and Planetary Change and 9 papers in Environmental Engineering. Recurrent topics in Jing M Chen's work include Remote Sensing in Agriculture (12 papers), Plant Water Relations and Carbon Dynamics (7 papers) and Remote Sensing and LiDAR Applications (7 papers). Jing M Chen is often cited by papers focused on Remote Sensing in Agriculture (12 papers), Plant Water Relations and Carbon Dynamics (7 papers) and Remote Sensing and LiDAR Applications (7 papers). Jing M Chen collaborates with scholars based in Canada, China and United States. Jing M Chen's co-authors include B. D. Amiro, John R. Miller, J. Cihlar, Yongqin Zhang, Wenjun Chen, David T. Price, Sean C. Thomas, Jane Liu, J. I. MacPherson and Richard Fernandes and has published in prestigious journals such as Remote Sensing of Environment, Agricultural and Forest Meteorology and ISPRS Journal of Photogrammetry and Remote Sensing.

In The Last Decade

Jing M Chen

17 papers receiving 607 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jing M Chen Canada 12 467 427 239 133 112 18 661
Titta Majasalmi Finland 12 324 0.7× 418 1.0× 278 1.2× 113 0.8× 120 1.1× 22 564
Manuela Balzarolo Belgium 15 532 1.1× 423 1.0× 166 0.7× 144 1.1× 69 0.6× 35 728
Pekka Voipio Finland 10 314 0.7× 530 1.2× 431 1.8× 204 1.5× 93 0.8× 16 707
Daniel Krofcheck United States 13 406 0.9× 348 0.8× 134 0.6× 103 0.8× 187 1.7× 27 601
D. E. Wickland United States 5 417 0.9× 284 0.7× 296 1.2× 61 0.5× 138 1.2× 14 684
Ritika Srinet India 12 274 0.6× 393 0.9× 352 1.5× 86 0.6× 180 1.6× 23 596
Veronique V. Cheret France 10 265 0.6× 300 0.7× 123 0.5× 88 0.7× 56 0.5× 19 448
Carl H. Menges Australia 8 278 0.6× 386 0.9× 295 1.2× 87 0.7× 47 0.4× 23 547
Sofia Cerasoli Portugal 14 386 0.8× 204 0.5× 112 0.5× 271 2.0× 135 1.2× 19 598
Blandine Caquet France 7 436 0.9× 468 1.1× 214 0.9× 97 0.7× 92 0.8× 7 670

Countries citing papers authored by Jing M Chen

Since Specialization
Citations

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

Fields of papers citing papers by Jing M Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jing M Chen. 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 Jing M Chen. The network helps show where Jing M Chen may publish in the future.

Co-authorship network of co-authors of Jing M Chen

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

All Works

18 of 18 papers shown
1.
Fan, Bin, Hankui K. Zhang, Jingfeng Xiao, et al.. (2025). Estimating carbon fluxes over North America using a physics-constrained deep learning model. ISPRS Journal of Photogrammetry and Remote Sensing. 227. 551–569.
2.
Dong, Taifeng, Jane Liu, Jiangui Liu, et al.. (2023). Assessing the consistency of crop leaf area index derived from seasonal Sentinel-2 and Landsat 8 imagery over Manitoba, Canada. Agricultural and Forest Meteorology. 332. 109357–109357. 9 indexed citations
3.
Geng, Jun, Jing M Chen, Weiliang Fan, et al.. (2022). Application of a Hypergeometric Model in Simulating Canopy Gap Fraction and BRF for Forest Plantations on Sloping Terrains. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15. 2901–2913. 8 indexed citations
4.
Chen, Jing M, Louis Legendre, & Ronald Benner. (2017). A recent project shows that the microbial carbon pump is a primary mechanism driving ocean carbon uptake. National Science Review. 5(4). 458–458. 4 indexed citations
5.
Jiao, Ziti, Crystal Schaaf, Yadong Dong, et al.. (2016). A method for improving hotspot directional signatures in BRDF models used for MODIS. Remote Sensing of Environment. 186. 135–151. 95 indexed citations
6.
7.
Zhang, Yongqin, Jing M Chen, John R. Miller, & Thomas L. Noland. (2008). Retrieving chlorophyll content in conifer needles from hyperspectral measurements. Canadian Journal of Remote Sensing. 34(3). 296–310. 32 indexed citations
8.
Písek, Jan, Jing M Chen, & Feng Deng. (2007). Assessment of a global leaf area index product from SPOT-4 VEGETATION data over selected sites in Canada. Canadian Journal of Remote Sensing. 33(4). 341–356. 19 indexed citations
9.
Peddle, Derek R., Ryan Johnson, J. Cihlar, et al.. (2007). Physically based inversion modeling for unsupervised cluster labeling, independent forest classification, and LAI estimation using MFM-5-Scale. Canadian Journal of Remote Sensing. 33(3). 214–225. 20 indexed citations
10.
Zhang, Yongqin, Jing M Chen, & Sean C. Thomas. (2007). Retrieving seasonal variation in chlorophyll content of overstory and understory sugar maple leaves from leaf-level hyperspectral data. Canadian Journal of Remote Sensing. 33(5). 406–415. 53 indexed citations
11.
Coursolle, Carole, Hank A. Margolis, Alan Barr, et al.. (2006). Late-summer carbon fluxes from Canadian forests and peatlands along an eastwest continental transect. Canadian Journal of Forest Research. 36(3). 783–800. 86 indexed citations
12.
Leblanc, Sylvain G., Jing M Chen, H. Peter White, et al.. (2005). Canada-wide foliage clumping index mapping from multiangular POLDER measurements. Canadian Journal of Remote Sensing. 31(5). 364–376. 53 indexed citations
13.
Chen, Jing M, et al.. (2004). Effects of subpixel water area fraction on mapping leaf area index and modeling net primary productivity in Canada. Canadian Journal of Remote Sensing. 30(5). 797–804. 5 indexed citations
14.
Hu, Baoxin, John R. Miller, Jing M Chen, & A. Hollinger. (2003). Retrieval of the canopy leaf area index in the BOREAS flux tower sites using linear spectral mixture analysis. Remote Sensing of Environment. 89(2). 176–188. 39 indexed citations
15.
Amiro, B. D. & Jing M Chen. (2003). Forest-fire-scar aging using SPOT-VEGETATION for Canadian ecoregions. Canadian Journal of Forest Research. 33(6). 1116–1125. 23 indexed citations
16.
Amiro, B. D., et al.. (2003). Post-fire carbon dioxide fluxes in the western Canadian boreal forest: evidence from towers, aircraft and remote sensing. Agricultural and Forest Meteorology. 115(1-2). 91–107. 71 indexed citations
17.
Fernandes, Richard, John R. Miller, Jing M Chen, & Irene Rubinstein. (2003). Evaluating image-based estimates of leaf area index in boreal conifer stands over a range of scales using high-resolution CASI imagery. Remote Sensing of Environment. 89(2). 200–216. 44 indexed citations
18.
Chen, Wenjun, Jing M Chen, David T. Price, & J. Cihlar. (2002). Effects of stand age on net primary productivity of boreal black spruce forests in Ontario, Canada. Canadian Journal of Forest Research. 32(5). 833–842. 95 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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