Bimal K. Bhattacharya

640 total citations
31 papers, 249 citations indexed

About

Bimal K. Bhattacharya is a scholar working on Ecology, Atmospheric Science and Environmental Engineering. According to data from OpenAlex, Bimal K. Bhattacharya has authored 31 papers receiving a total of 249 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Ecology, 9 papers in Atmospheric Science and 9 papers in Environmental Engineering. Recurrent topics in Bimal K. Bhattacharya's work include Remote Sensing in Agriculture (9 papers), Spectroscopy and Chemometric Analyses (5 papers) and Remote-Sensing Image Classification (5 papers). Bimal K. Bhattacharya is often cited by papers focused on Remote Sensing in Agriculture (9 papers), Spectroscopy and Chemometric Analyses (5 papers) and Remote-Sensing Image Classification (5 papers). Bimal K. Bhattacharya collaborates with scholars based in India, United States and United Arab Emirates. Bimal K. Bhattacharya's co-authors include Robert O. Green, P. Srinivasulu, Sadasiva M. Rao, Soumya Bandyopadhyay, Raj Kumar, Mehul R. Pandya, Debajyoti Dhar, Chandra Prakash Singh, Prashant Kumar and Sujay Kumar Dutta and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Agricultural and Forest Meteorology.

In The Last Decade

Bimal K. Bhattacharya

26 papers receiving 241 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bimal K. Bhattacharya India 10 94 80 68 56 51 31 249
Zama Eric Mashimbye South Africa 8 155 1.6× 128 1.6× 39 0.6× 56 1.0× 49 1.0× 14 310
David Montero Germany 7 117 1.2× 51 0.6× 43 0.6× 30 0.5× 29 0.6× 18 233
Hugo do Nascimento Bendini Brazil 10 184 2.0× 86 1.1× 80 1.2× 38 0.7× 15 0.3× 33 310
Xiguang Yang China 12 155 1.6× 163 2.0× 134 2.0× 114 2.0× 38 0.7× 36 441
Jacob Shermeyer United States 7 182 1.9× 133 1.7× 101 1.5× 53 0.9× 45 0.9× 10 395
Wenjiang Huang China 7 222 2.4× 98 1.2× 42 0.6× 76 1.4× 33 0.6× 13 361
Lorenzo Fusilli Italy 9 177 1.9× 82 1.0× 120 1.8× 69 1.2× 46 0.9× 26 375
Salman Ashraf New Zealand 11 97 1.0× 65 0.8× 87 1.3× 52 0.9× 44 0.9× 21 340
Yizhi Huang United States 6 139 1.5× 149 1.9× 62 0.9× 42 0.8× 84 1.6× 9 299
Svetlana Illarionova Russia 13 130 1.4× 133 1.7× 59 0.9× 17 0.3× 17 0.3× 31 304

Countries citing papers authored by Bimal K. Bhattacharya

Since Specialization
Citations

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

Fields of papers citing papers by Bimal K. Bhattacharya

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bimal K. Bhattacharya. 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 Bimal K. Bhattacharya. The network helps show where Bimal K. Bhattacharya may publish in the future.

Co-authorship network of co-authors of Bimal K. Bhattacharya

This figure shows the co-authorship network connecting the top 25 collaborators of Bimal K. Bhattacharya. A scholar is included among the top collaborators of Bimal K. Bhattacharya 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 Bimal K. Bhattacharya. Bimal K. Bhattacharya 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
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Das, Bappa, Pooja Singh, Priyabrata Santra, et al.. (2025). Assessing the accuracy of multi-model approaches for downscaling land surface temperature across diverse agroclimatic zones. Scientific Reports. 15(1). 10824–10824. 1 indexed citations
3.
Kumar, K.Ch.V. Naga, Nikhil Lele, Kakani Nageswara Rao, et al.. (2024). Assessing the significance of leaf chlorophyll content for measuring the vegetation health in mangrove species along the Kerala Coast, India – A multi-proxy approach. Regional Studies in Marine Science. 77. 103702–103702. 1 indexed citations
4.
5.
Bhattacharya, Bimal K., et al.. (2024). Machine learning based plot level rice lodging assessment using multi-spectral UAV remote sensing. Computers and Electronics in Agriculture. 219. 108754–108754. 14 indexed citations
6.
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Pandey, Dharmendra Kumar, Prashant K. Srivastava, Raj Setia, et al.. (2024). Operational 500 m surface soil moisture product using EOS-04 C-band SAR over Indian agricultural croplands. Current Science. 126(9). 1061–1061. 1 indexed citations
8.
Kayet, Narayan, et al.. (2023). Detection and mapping of vegetation stress using AVIRIS-NG hyperspectral imagery in coal mining sites. Advances in Space Research. 73(2). 1368–1378. 3 indexed citations
9.
Singh, Chandra Prakash, et al.. (2023). Role of LiDAR remote sensing in identifying physiognomic traits of alpine treeline: a global review. Tropical Ecology. 65(3). 341–355. 2 indexed citations
10.
Singh, Chandra Prakash, H. H. Joshi, Rajesh Bajpai, et al.. (2023). Mapping lichen abundance in ice-free areas of Larsemann Hills, East Antarctica using remote sensing and lichen spectra. Polar Science. 38. 100976–100976. 6 indexed citations
11.
Das, Bappa, Debashis Chakraborty, Bimal K. Bhattacharya, et al.. (2023). Ensemble surface soil moisture estimates at farm-scale combining satellite-based optical-thermal-microwave remote sensing observations. Agricultural and Forest Meteorology. 339. 109567–109567. 22 indexed citations
12.
Behera, Mukunda Dev, et al.. (2023). Species-level classification of mangrove forest using AVIRIS-NG hyperspectral imagery. Remote Sensing Letters. 14(5). 522–533. 3 indexed citations
13.
Birah, Ajanta, et al.. (2023). Selection of sensitive bands for assessing Alernaria blight diseased severity grades in mustard crops using hyperspectral reflectance. Journal of Agrometeorology. 25(2). 274–279. 1 indexed citations
14.
Nigam, Rahul, et al.. (2023). Detection of Aphid-Infested Mustard Crop Using Ground Spectroscopy. Remote Sensing. 16(1). 47–47. 1 indexed citations
15.
Singh, Ankit, Chandra Prakash Singh, Mehul R. Pandya, et al.. (2022). Phenocam observed flowering anomaly of Rhododendron arboreum Sm. in Himalaya: a climate change impact perspective. Environmental Monitoring and Assessment. 194(12). 877–877. 13 indexed citations
16.
Ghosh, Rajib, et al.. (2019). NON-LINEAR AUTOENCODER BASED ALGORITHM FOR DIMENSIONALITY REDUCTION OF AIRBORNE HYPERSPECTRAL DATA. SHILAP Revista de lepidopterología. XLII-3/W6. 593–598. 10 indexed citations
17.
Nigam, Rahul, Rojalin Tripathy, Sujay Kumar Dutta, et al.. (2019). Crop Type Discrimination and Health Assessment using Hyperspectral Imaging. Current Science. 116(7). 1108–1108. 23 indexed citations
18.
Kumar, Amrender, et al.. (2016). Epidemiology and forecasting of insect-pests and diseases for value-added agro-advisory. MAUSAM. 67(1). 267–276. 5 indexed citations
19.
Bhattacharya, Bimal K., et al.. (2013). Cross calibration of INSAT 3A CCD channel radiances with IRS P6 AWiFS sensor. Journal of Earth System Science. 122(4). 957–966. 5 indexed citations
20.
Dutta, Sujay Kumar, Bimal K. Bhattacharya, D. Ram Rajak, et al.. (2007). Modelling regional level spatial distribution of aphid (Lipaphis erysimi) growth in Indian mustard using satellite-based remote sensing data. International Journal of Pest Management. 54(1). 51–62. 8 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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