Murali Krishna Gumma

6.5k total citations · 3 hit papers
98 papers, 4.8k citations indexed

About

Murali Krishna Gumma is a scholar working on Ecology, Global and Planetary Change and Plant Science. According to data from OpenAlex, Murali Krishna Gumma has authored 98 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Ecology, 48 papers in Global and Planetary Change and 23 papers in Plant Science. Recurrent topics in Murali Krishna Gumma's work include Remote Sensing in Agriculture (51 papers), Land Use and Ecosystem Services (30 papers) and Climate change impacts on agriculture (16 papers). Murali Krishna Gumma is often cited by papers focused on Remote Sensing in Agriculture (51 papers), Land Use and Ecosystem Services (30 papers) and Climate change impacts on agriculture (16 papers). Murali Krishna Gumma collaborates with scholars based in India, United States and Philippines. Murali Krishna Gumma's co-authors include Prasad S. Thenkabail, Pardhasaradhi Teluguntla, Jun Xiong, Kamini Yadav, Russell G. Congalton, Adam Oliphant, Venkateswarlu Dheeravath, Andrew Nelson, Anthony Whitbread and Çhandrashekhar Biradar and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Science of The Total Environment and Journal of Environmental Management.

In The Last Decade

Murali Krishna Gumma

93 papers receiving 4.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

2018 424 citations Pardhasaradhi Teluguntla, Prasad S. Thenkabail et al. ISPRS Journal of Photogrammetry and Remote Sensing profile →
Automated cropland mapping of continental Africa using Google Earth Engine cloud computing

2017 382 citations Jun Xiong, Prasad S. Thenkabail et al. ISPRS Journal of Photogrammetry and Remote Sensing profile →
Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

2017 312 citations Jun Xiong, Prasad S. Thenkabail et al. Remote Sensing profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Murali Krishna Gumma India	36	2.8k	2.4k	1.1k	960	949	98	4.8k
Mutlu Özdoğan United States	39	2.5k 0.9×	2.6k 1.1×	1.2k 1.1×	928 1.0×	939 1.0×	66	4.9k
Çhandrashekhar Biradar United States	36	2.9k 1.0×	2.5k 1.0×	1.1k 1.0×	764 0.8×	975 1.0×	97	4.7k
Christopher Conrad Germany	36	2.5k 0.9×	2.1k 0.9×	1.0k 0.9×	736 0.8×	810 0.9×	168	4.5k
Geli Zhang China	36	4.0k 1.4×	3.9k 1.6×	1.4k 1.3×	894 0.9×	1.6k 1.7×	86	6.3k
Grégory Duveiller Italy	36	2.8k 1.0×	3.3k 1.4×	1.7k 1.6×	1.2k 1.3×	1.2k 1.3×	97	5.9k
Jianxi Huang China	41	3.6k 1.3×	2.6k 1.1×	1.5k 1.4×	1.9k 2.0×	1.2k 1.3×	181	6.1k
Yaozhong Pan China	34	2.3k 0.8×	3.3k 1.3×	964 0.9×	559 0.6×	1.0k 1.1×	128	4.8k
Bin Zhao China	40	3.4k 1.2×	2.7k 1.1×	1.2k 1.1×	681 0.7×	849 0.9×	143	5.9k
Adam Sibley United States	9	1.8k 0.6×	2.5k 1.0×	868 0.8×	810 0.8×	1.0k 1.1×	12	4.1k
V. K. Dadhwal India	43	2.7k 1.0×	4.0k 1.7×	1.9k 1.7×	702 0.7×	1.7k 1.7×	341	7.5k

Countries citing papers authored by Murali Krishna Gumma

Since Specialization

Citations

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

Fields of papers citing papers by Murali Krishna Gumma

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Murali Krishna Gumma. 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 Murali Krishna Gumma. The network helps show where Murali Krishna Gumma may publish in the future.

Co-authorship network of co-authors of Murali Krishna Gumma

This figure shows the co-authorship network connecting the top 25 collaborators of Murali Krishna Gumma. A scholar is included among the top collaborators of Murali Krishna Gumma 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 Murali Krishna Gumma. Murali Krishna Gumma 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

Jain, Atul K., Naveen Chandra, Prabir K. Patra, et al.. (2025). South Asia's Ecosystems Are a Net Carbon Sink, But the Region Is a Major Net GHG Source to the Atmosphere. Global Biogeochemical Cycles. 39(4). 1 indexed citations

Gumma, Murali Krishna, S. K. Gupta, Vinay Kumar Sehgal, et al.. (2024). Geospatial analysis to identify millet suitable areas in the upland rice ecosystem of Odisha. Journal of Agriculture and Food Research. 19. 101593–101593. 1 indexed citations

Gumma, Murali Krishna, S. K. Dubey, D. K. Ray, et al.. (2024). Spatial Distribution of Cropping Systems in South Asia Using Time-Series Satellite Data Enriched with Ground Data. Remote Sensing. 16(15). 2733–2733. 4 indexed citations

Gumma, Murali Krishna, et al.. (2023). Crop Yield Assessment Using Field-Based Data and Crop Models at the Village Level: A Case Study on a Homogeneous Rice Area in Telangana, India. AgriEngineering. 5(4). 1909–1924. 2 indexed citations

Gumma, Murali Krishna, et al.. (2023). Impacts of irrigation tank restoration on water bodies and croplands in Telangana State of India using Landsat time series data and machine learning algorithms. Geocarto International. 38(1). 5 indexed citations

Gumma, Murali Krishna, et al.. (2023). Mapping Shrimp Pond Dynamics: A Spatiotemporal Study Using Remote Sensing Data and Machine Learning. AgriEngineering. 5(3). 1432–1447. 2 indexed citations

Kagone, Stefanie, Mansoor Leh, Naga Manohar Velpuri, et al.. (2023). A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale. International Journal of Applied Earth Observation and Geoinformation. 126. 103607–103607. 4 indexed citations

Amare, Tadele, et al.. (2022). Remediation of acid soils and soil property amelioration via Acacia decurrens-based agroforestry system. Agroforestry Systems. 96(2). 329–342. 22 indexed citations

Gumma, Murali Krishna, et al.. (2022). Assessment of Cropland Changes Due to New Canals in Vientiane Prefecture of Laos using Earth Observation Data. SHILAP Revista de lepidopterología. 4. 100149–100149. 3 indexed citations

10.

Gumma, Murali Krishna, et al.. (2022). Identifying Suitable Watersheds across Nigeria Using Biophysical Parameters and Machine Learning Algorithms for Agri–Planning. ISPRS International Journal of Geo-Information. 11(8). 416–416. 1 indexed citations

11.

Gumma, Murali Krishna, M. D. M. Kadiyala, S. S. Ray, et al.. (2021). Assimilation of Remote Sensing Data into Crop Growth Model for Yield Estimation: A Case Study from India. Journal of the Indian Society of Remote Sensing. 50(2). 257–270. 33 indexed citations

12.

Sharma, Shivali, Rajan Sharma, Mahalingam Govindaraj, et al.. (2020). Harnessing wild relatives of pearl millet for germplasm enhancement: Challenges and opportunities. Crop Science. 61(1). 177–200. 22 indexed citations

13.

Gumma, Murali Krishna, et al.. (2020). Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information. Geocarto International. 37(7). 1833–1849. 43 indexed citations

14.

Vadez, Vincent, Afshin Soltani, Pooran M. Gaur, et al.. (2018). Characterization of the main chickpea cropping systems in India using a yield gap analysis approach. Field Crops Research. 223. 93–104. 35 indexed citations

15.

Teluguntla, Pardhasaradhi, Prasad S. Thenkabail, Jun Xiong, et al.. (2016). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security Support Analysis Data (GFSAD) Crop Dominance 2010 Global 1 km V001. 17 indexed citations

16.

Upadhyaya, Hari D., et al.. (2016). Geographical distribution of traits and diversity in the world collection of pearl millet [Pennisetum glaucum (L.) R. Br., synonym: Cenchrus americanus (L.) Morrone] landraces conserved at the ICRISAT genebank. Genetic Resources and Crop Evolution. 64(6). 1365–1381. 17 indexed citations

17.

Gumma, Murali Krishna, et al.. (2015). Mapping Direct Seeded Rice in Raichur District of Karnataka, India. Photogrammetric Engineering & Remote Sensing. 81(11). 873–880. 8 indexed citations

18.

Thenkabail, Prasad S., Jerry Knox, Mutlu Özdoğan, et al.. (2012). Assessing future risks to agricultural productivity, water resources and food security: How can remote sensing help?. Photogrammetric Engineering & Remote Sensing. 78(8). 773–782. 96 indexed citations

19.

Dheeravath, Venkateswarlu, Prasad S. Thenkabail, Praveen Noojipady, et al.. (2009). Irrigated areas of India derived using MODIS 500 m time series for the years 2001–2003. ISPRS Journal of Photogrammetry and Remote Sensing. 65(1). 42–59. 78 indexed citations

20.

Biggs, Trent, Anju Gaur, Christopher A. Scott, et al.. (2007). Closing of the Krishna Basin: irrigation, streamflow depletion and macroscale hydrology. CGSPace A Repository of Agricultural Research Outputs (Consultative Group for International Agricultural Research). 11 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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