Bibliometric mapping of remote sensing-based crop climate vulnerability: from spectral monitoring to predictive geospatial intelligence
Article Sidebar
Main Article Content
Abstract
The intensification of droughts, heatwaves and floods is increasing crop vulnerability and compromising food security, which requires a robust scientific basis to guide agricultural adaptation supported by remote sensing. This study aimed to describe and quantitatively analyse the evolution of research on remote sensing-based crop climate vulnerability, identifying temporal, geographical and thematic patterns. A quantitative, exploratory, descriptive, longitudinal and retrospective bibliometric design was applied to 2,343 Scopus records published between 1985 and 2026, processed in Bibliometrix 5.1.1 and VOSviewer 1.6.20 through productivity, impact and collaboration indicators, together with RPYS. The results show an annual growth rate of 4%, 627 sources, 10,408 authors, 5.17 co-authors per document, 35.3% international collaboration and 19.93 citations per document, with leadership by China, the United States and India. Remote Sensing and Science of the Total Environment concentrated dissemination, while machine learning and Google Earth Engine shaped the recent agenda. The study concludes that the field has reached scientific and technological maturity, consolidated remote sensing as a strategic axis for assessing, modelling and anticipating agricultural vulnerability, and advanced towards predictive approaches with high relevance for climate change adaptation.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish in this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see "The Effect of Open Access").
How to Cite
References
Abdulwahid, W. M., Feizizadeh, B., Blaschke, T., & Karimzadeh, S. (2025). A Geoinformation Approach for Spatiotemporal Mapping of Climate Change Environmental Impacts on Sustainable Food Production in Iraq. International Journal of Environmental Research, 19(5). Scopus. https://doi.org/10.1007/s41742-025-00821-8
Aduko, J., Osumanu, I. K., & Yahaya, A.-K. (2025). Impacts of climate change and mobile pastoralism on land use in semi-arid Ghana. Discover Environment, 3(1). Scopus. https://doi.org/10.1007/s44274-025-00211-z
Ahmed, S. M., Biro Turk, K. G., Ahmed, A. E., Elbushra, A. A., Aldhafeeri, A. A., & Darrag, H. M. (2025). Evapotranspiration Estimation in the Arab Region: Methodological Advances and Multi-Sensor Integration Framework. Water (Switzerland), 17(18). Scopus. https://doi.org/10.3390/w17182702
Ali, E. M., Zanaty, N., Khairy, N., & Abou El-Magd, I. (2025). Harnessing EO data for short-term surveillance and projections of the Nile Delta coastline and sea-level dynamics. Environmental and Sustainability Indicators, 28. Scopus. https://doi.org/10.1016/j.indic.2025.100961
Aria, M., & Cuccurullo, C. (2024). bibliometrix: Comprehensive Science Mapping Analysis (Versión 4.2.3) [Software]. https://cran.r-project.org/web/packages/bibliometrix/index.html
Attia, A., Woli, P., Long, C. R., Rouquette Jr, F. M., Smith, G. R., & Ibrahim, A. M. H. (2025). Mapping spatial zones of climate vulnerability and adaptive potential for major crops in the Texas high plains. Modeling Earth Systems and Environment, 11(6). https://doi.org/10.1007/s40808-025-02605-7
Bellido-Valdiviezo, O., Cardoza-Sernaqué, M. A., Cardoza-Sernaqué, L. S., Gamarra-Mendoza, S., Estrada-Espinoza, J. A., Torres-Solano, C. G., Bolaño García, M., & Zavala Palacios, A. (2023). Digital Citizenship: A bibliographic Review of the Publications in Scopus from 2017 to 2022. 2023-July. Scopus.
Bhattacharjee, S., Basavareddy, Rajput, V. D., Majumder, D., Panja, A., Kashyap, S., & Kumar, R. (2025). Assessing land degradation and drought vulnerability in the Trans-Gangetic Plains using Google Earth Engine and remote sensing for SDG 15.3.1 monitoring. Environmental Monitoring and Assessment, 197(8). https://doi.org/10.1007/s10661-025-14371-1
Chaiyana, A., Lima, J. M. E., Barnes, E. M., & Jagadish, S. V. K. (2025). Spatiotemporal prediction of drought-driven phenology-based cotton yield impacts in Texas dryland systems. Smart Agricultural Technology, 12. Scopus. https://doi.org/10.1016/j.atech.2025.101453
Cisse, A., De Janvry, A., Ferguson, J., Gonzalez-Navarro, M., Mbaye, S., Sadoulet, E., & Syll, M. M. A. (2026). Irrigation infrastructure and satellite-measured land cultivation impacts: Evidence from the Senegal river valley. Journal of Development Economics, 179. Scopus. https://doi.org/10.1016/j.jdeveco.2025.103615
Dhillon, M. S., Kübert-Flock, C., Dahms, T., Rummler, T., Arnault, J., Steffan-Dewenter, I., & Ullmann, T. (2023). Evaluation of MODIS, Landsat 8 and Sentinel-2 Data for Accurate Crop Yield Predictions: A Case Study Using STARFM NDVI in Bavaria, Germany. Remote Sensing, 15(7). https://doi.org/10.3390/rs15071830
FAO [Food and Agriculture Organization]. (2023). Cultivated cropland area extent in Sudan Estimates for July 2023 in comparison to July average during 2018-2022. https://openknowledge.fao.org/handle/20.500.14283/cc8787en
García, L. K. O., Alayo, W. M. H., Taboada, S. L. V., & Benites, N. I. P. (2025). BIBLIOMETRIC ANALYSIS ON BRIDGING THE DIGITAL DIVIDE AMONG UNIVERSITY STUDENTS: TRENDS AND PROSPECTS. Revista Conhecimento Online, 1, 193-220. Scopus. https://doi.org/10.25112/rco.v1.3963
Goroee, F., & Bazrafshan, O. (2025). Comparative performance analysis of Google Earth Engine and SNAP platforms for estimating water quality parameters in Minab dam reservoir using multi-spectral resolution satellite imagery. Water and Soil Management and Modeling, 5(3), 104-122. Scopus. https://doi.org/10.22098/mmws.2025.16743.1557
Hamer, E. A., Oyedotun, T. D. T., & Nedd, G. A. (2025). Evaluating coastal agroecological dynamics using Landsat-derived vegetation and environmental indices embedded in Decision Support System and Monitoring Tools: Insights from Guyana towards achieving SDGs. Discover Sustainability, 6(1). Scopus. https://doi.org/10.1007/s43621-025-01203-y
Hammond, M. L., Foti, G., Gommenginger, C., & Srokosz, M. (2020). Temporal variability of GNSS-Reflectometry ocean wind speed retrieval performance during the UK TechDemoSat-1 mission. Remote Sensing of Environment, 242. https://doi.org/10.1016/j.rse.2020.111744
Hao, Z., Hao, F., Xia, Y., Singh, V. P., & Zhang, X. (2019). A monitoring and prediction system for compound dry and hot events. Environmental Research Letters, 14(11). https://doi.org/10.1088/1748-9326/ab4df5
Hoyos-Alayo, W. M. (2025). Global knowledge structure of eco-anxiety and climate action: A multidimensional bibliometric analysis. Aibi, Revista de Investigacion Administracion e Ingenierias, 13(3). https://doi.org/10.15649/2346030X.5606
Hoyos-Alayo, W. M. (2026a). Bibliometric analysis of energy poverty and thermal comfort in the residential sector and social housing. Revista Brasileira de Meio Ambiente, 14(2), 2-28. https://doi.org/10.66205/rvbma.v14i2.2056
Hoyos-Alayo, W. M. (2026b). Bibliometric analysis of urbanisation, land-use change and ecosystem services in urban and regional planning. Revista Brasileira de Meio Ambiente, 14(1), 209-236. https://doi.org/10.66205/rvbma.v14i1.2065
Hoyos-Alayo, W. M. (2026c). Construction project management with a circular economy and waste minimisation focus: A bibliometric analysis of its evolution and research trends. Revista Ingenieria de Construccion, 41(1). https://doi.org/10.7764/RIC.00173.21Estaobraest
Hultgren, A., Carleton, T., Delgado, M., Gergel, D. R., Greenstone, M., Houser, T., Hsiang, S., Jina, A., Kopp, R. E., Malevich, S. B., McCusker, K. E., Mayer, T., Nath, I., Rising, J., Rode, A., & Yuan, J. (2025). Impacts of climate change on global agriculture accounting for adaptation. Nature, 642(8068), 644-652. https://doi.org/10.1038/s41586-025-09085-w
IPCC [Intergovernmental Panel on Climate Change]. (2022). Chapter 5: Food, Fibre and Other Ecosystem Products. https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-5/
Javari, M. (2025). A hybrid machine learning approach to analyzing the impacts of urban development on land surface temperature and the urban heat island effect in Isfahan. Theoretical and Applied Climatology, 156(10). Scopus. https://doi.org/10.1007/s00704-025-05691-w
Khan, S. A., Zandler, H., & Sass, O. (2025). Climate change impacts on livelihoods in the Eastern Hindu Kush: Integrating local perceptions and biophysical data. Regional Environmental Change, 25(3). Scopus. https://doi.org/10.1007/s10113-025-02414-9
Kim, S.-J., Kim, H., Son, H., & Jang, M.-W. (2025). Assessment and correction of Sentinel-2 and Landsat-8/9 NDVI using in-situ measurements across rice growth stages in southern South Korea. Field Crops Research, 334. Scopus. https://doi.org/10.1016/j.fcr.2025.110149
Kongo, V. O., & Arreyndip, N. A. (2025). The 2023 drought in West Africa and associated vulnerability to food insecurity. Scientific Reports, 15(1), 34959. Scopus. https://doi.org/10.1038/s41598-025-18940-9
Lakshmi, V., Kir, E. G., Kir, A., & Fang, B. (2025). Remote Sensing-Based Monitoring of Agricultural Drought and Irrigation Adaptation Strategies in the Antalya Basin, Türkiye. Hydrology, 12(11). https://doi.org/10.3390/hydrology12110288
Liou, Y.-A., & Thai, M.-T. (2025). Relative surface evapotranspiration index (RSETI): A novel approach for drought characterization in Australia. Remote Sensing of Environment, 329. https://doi.org/10.1016/j.rse.2025.114948
Luna-Morales, M. E., Pérez-Angón, M. Á., & Luna-Morales, E. (2023). Strengthen of a Scientific Field in Latin America: Evolutionary Computation. Journal of Scientometric Research, 12(2), 264-274. Scopus. https://doi.org/10.5530/jscires.12.2.025
Manuel Hoyos Alayo, W. (2025). Bioadsorption of arsenic by fruit residues: A systematic review. Proc. LACCEI int. multi-conf. eng. educ. technol., (2025). https://doi.org/10.18687/LACCEI2025.1.1.589
Manuel Hoyos Alayo, W., & Lourdes Vidal Taboada, S. (2025). Adsorption as a predominant technology for industrial wastewater treatment: A systematic review. Proc. LACCEI int. multi-conf. eng. educ. technol., (2025). https://doi.org/10.18687/LACCEI2025.1.1.281
Mao, X., Zheng, J., Lu, B., Wang, R., Han, W., & Harris, P. (2025). Spatiotemporal drought forecasting in Xinjiang’s irrigated agriculture: Model comparison and multi-source data integration. Journal of Hydrology, 660. Scopus. https://doi.org/10.1016/j.jhydrol.2025.133483
Monteleone, B., Borzí, I., Bonaccorso, B., & Martina, M. (2023). Quantifying crop vulnerability to weather-related extreme events and climate change through vulnerability curves. Natural Hazards, 116(3), 2761-2796. https://doi.org/10.1007/s11069-022-05791-0
Mukherjee, S., & Mishra, A. K. (2021). Increase in Compound Drought and Heatwaves in a Warming World. Geophysical Research Letters, 48(1). https://doi.org/10.1029/2020GL090617
Osemwegie, O. O., Olaniran, A. F., Folorunsho, J. O., Nwonuma, C. O., Ojo, O. A., Adetunde, L. A., Alejolowo, O. O., Oluba, O. M., & Daramola, F. Y. (2023). PRELIMINARY BIBLIOMETRICS OF PLANT-DERIVED HEALTH FOODS OVER THE LAST DECADE IN THE SCOPUS DATABASE. African Journal of Food, Agriculture, Nutrition and Development, 23(8), 24363-24382. Scopus. https://doi.org/10.18697/ajfand.123.22765
Sarah, C., Rochelle, S., Johanna, N., Michelle, H., Sofia, F., Ying, W., Michael, R., Kristin, A., Jean-Philippe, A., Mark, D., Claire, D., Caroline, G., Matti, G., Aaron, G., Andrew, K., Thelma, K., Timo, L., Tatiana, L., Cromwel, L., … Ambrosio, Y. del R. (2025). Earth observations for climate adaptation: Tracking progress towards the Global Goal on Adaptation through satellite-derived indicators. Npj Climate and Atmospheric Science, 8(1). https://doi.org/10.1038/s41612-025-01251-1
Senapati, U., Das, U., & Das, T. K. (2025). Spatio-temporal Scenario and Farmers’ Perceptions of Agricultural Drought in the Context of Climate Change in a Drought–Prone River Basin of India. Journal of the Indian Society of Remote Sensing, 53(11), 3755-3773. Scopus. https://doi.org/10.1007/s12524-025-02134-x
Sengupta, S., & Chu, H.-J. (2025). Near real-time satellite soil moisture estimation via residual learning integrated with sensor networks. Journal of Hydrology, 663. https://doi.org/10.1016/j.jhydrol.2025.134302
Serkendiz, H., Tatli, H., Özelkan, E., & Çetin, M. (2025). Machine learning and geographic information systems-based framework for multidimensional analysis of cascading drought impacts using remote sensing and in-situ data. Science of the Total Environment, 1001. Scopus. https://doi.org/10.1016/j.scitotenv.2025.180504
Sharma, P., & Dadheech, P. (2026). Evaluating Vegetation Indices for Crop Monitoring Using Multispectral Satellite Imagery: 2543 CCIS (R. C. Bansal, R. C. Bansal, V. Devedzic, R. Nayak, B. Agarwal, A. Tandon, & P. Jain, Eds.; pp. 197-206). Springer Science and Business Media Deutschland GmbH. Scopus. https://doi.org/10.1007/978-3-031-95540-2_18
Sharma, S. S., Mukherjee, J., & Dell’Acqua, F. (2025). Leveraging Sentinel-2 Data and Machine Learning for Drought Detection in India: The Process of Ground Truth Construction and a Case Study. Remote Sensing, 17(18). Scopus. https://doi.org/10.3390/rs17183159
Sims, M. J., Stanimirova, R., Raichuk, A., Neumann, M., Richter, J., Follett, F., MacCarthy, J., Lister, K., Randle, C., Sloat, L., Esipova, E., Jupiter, J., Stanton, C., Morris, D., Melhart Slay, C., Purves, D., & Harris, N. (2025). Global drivers of forest loss at 1 km resolution. Environmental Research Letters, 20(7). https://doi.org/10.1088/1748-9326/add606
Smith-Tripp, S. M., Eskelson, B. N. I., Coops, N. C., & Schwartz, N. B. (2022). Canopy height impacts on the growing season and monthly microclimate in a burned forest of British Columbia, Canada. Agricultural and Forest Meteorology, 323. https://doi.org/10.1016/j.agrformet.2022.109067
Song, Y., Kalacska, M., Gašparović, M., Yao, J., & Najibi, N. (2023). Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping. International Journal of Applied Earth Observation and Geoinformation, 120. https://doi.org/10.1016/j.jag.2023.103300
Sulphey, M. M., AlKahtani, N. S., Senan, N. A. M., & Adow, A. H. E. (2024). A bibliometric study on organization citizenship behavior for the environment. Global Journal of Environmental Science and Management, 10(2), 891-906. Scopus. https://doi.org/10.22035/gjesm.2024.02.29
Sun, Y., Zhang, D., Miao, Z., Yang, S., Liu, Q., & Qu, Z. (2025). Season-Specific CNN and TVDI Approach for Soil Moisture and Irrigation Monitoring in the Hetao Irrigation District, China. Agriculture (Switzerland), 15(18). Scopus. https://doi.org/10.3390/agriculture15181946
Tigchelaar, M., Battisti, D. S., Naylor, R. L., & Ray, D. K. (2018). Future warming increases probability of globally synchronized maize production shocks. Proceedings of the National Academy of Sciences of the United States of America, 115(26), 6644-6649. https://doi.org/10.1073/pnas.1718031115
United Nations. (2020). Global Goal On Adaptation. Global Adaptation Network (GAN). https://www.unep.org/gan/what-we-do/global-goal-adaptation
Valjarević, A., Šiljeg, A., Šiljeg, S., Vujović, F., & Sahay, A. (2025). GIS-based water stress analysis in North African drylands. Journal of Arid Environments, 230. Scopus. https://doi.org/10.1016/j.jaridenv.2025.105427
Valle, D., Izbicki, R., & Leite, R. V. (2023). Quantifying uncertainty in land-use land-cover classification using conformal statistics. Remote Sensing of Environment, 295. https://doi.org/10.1016/j.rse.2023.113682
Van Eck, N. J., & Waltman, L. (2024). VOSviewer—Visualizing scientific landscapes (Versión 1.6.20) [Software]. Centre for Science and Technology Studies (CWTS). https://www.vosviewer.com//
Wang, X., Zeng, J., Chen, F., Wang, Z., Liu, H., Zhang, Q., Liu, W., Wang, W., Guo, Y., Niu, Y., Yuan, L., Ren, C., Yang, G., Zhong, Z., & Han, X. (2024). Aridity shapes distinct biogeographic and assembly patterns of forest soil bacterial and fungal communities at the regional scale. Science of the Total Environment, 948. https://doi.org/10.1016/j.scitotenv.2024.174812
Wang, Y., Wang, J., Gong, D., Ding, M., Zhong, W., Deng, M., Kang, Q., Ding, Y., Liu, Y., & Zhang, J. (2025). Spatiotemporal Heterogeneity and Zonal Adaptation Strategies for Agricultural Risks of Compound Dry and Hot Events in China’s Middle Yangtze River Basin. Remote Sensing, 17(16). https://doi.org/10.3390/rs17162892
Waqas, M. S., Bayabil, H. K., Hailegnaw, N. S., Hussain, S., Tariq, A., & Abubakar, S. (2025). Drought mitigation and livelihood improvement options through rainwater harvesting structures in a rainfed agricultural system. Agricultural Systems, 230. Scopus. https://doi.org/10.1016/j.agsy.2025.104469
Yeboah, E., Sarfo, I., Zhu, Q., Kwang, C., Puplampu, D. A., Nikoi, E., Arthur, I. K., Owusu, A. B., Fynn, I. E. M., El Rhadiouini, C., Okrah, A., Jaffry, A. H., Siddique, F., Aftab, R., Boyetey, D., Siaw, W., & Batasuma, S. (2025). Traceability and projected patterns of Africa’s land use systems and climate variability (1993–2053). Land Use Policy, 157. Scopus. https://doi.org/10.1016/j.landusepol.2025.107680
Yimam, A., & Woldmariam, M. F. (2025). Satellite-based agro-meteorological drought assessment in North Eastern Ethiopia. Environmental Challenges, 20. https://doi.org/10.1016/j.envc.2025.101234
Yin, X., Jiang, B., Chen, Y., Zhao, Y., & Tan, X. (2025). Observational evidence highlights the spatial divergence of vegetation changes in altering land surface energy balance over the Three-North region in China in summer. Ecological Indicators, 179. Scopus. https://doi.org/10.1016/j.ecolind.2025.114152
Yu, W., Xiong, Y., Li, X., Zheng, H., Jiang, C., Yao, X., Zhu, Y., Cao, W., Qiu, L., Song, L., & Cheng, T. (2026). Rice yield prediction in unseen years at field level with high-resolution gross primary productivity derived from Sentinel-2 imagery. Remote Sensing of Environment, 332. https://doi.org/10.1016/j.rse.2025.115061
Zeleke, E. B., Mohammed, M., & Kidanewold, B. B. (2025). Vulnerability assessment of smallholder farmers to climate change in the Awash Basin, Ethiopia. Environmental and Sustainability Indicators, 28. https://doi.org/10.1016/j.indic.2025.100927
Zhang, F., Biederman, J. A., Devine, C. J., Pierce, N. A., Yan, D., Javadian, M., Potts, D. L., & Smith, W. K. (2023). Using high frequency digital repeat photography to quantify the sensitivity of a semi-arid grassland ecosystem to the temporal repackaging of precipitation. Agricultural and Forest Meteorology, 338. https://doi.org/10.1016/j.agrformet.2023.109539
Zhao, C., Liu, B., Piao, S., Wang, X., Lobell, D. B., Huang, Y., Huang, M., Yao, Y., Bassu, S., Ciais, P., Durand, J.-L., Elliott, J., Ewert, F., Janssens, I. A., Li, T., Lin, E., Liu, Q., Martre, P., Müller, C., … Asseng, S. (2017). Temperature increase reduces global yields of major crops in four independent estimates. Proceedings of the National Academy of Sciences of the United States of America, 114(35), 9326-9331. https://doi.org/10.1073/pnas.1701762114
Zhou, X., Song, J., Dang, Y., Xiao, Z., & Yang, H. (2025). Real-time prediction of corn yield from single-phase SAR and optical remote sensing data using deep learning. European Journal of Agronomy, 171. https://doi.org/10.1016/j.eja.2025.127819
Zubair, M., Zafar, Z., Yao, S., Guo, Z., Nadeem, A. A., & Fahd, S. (2025). Agricultural drought forecasting using remote sensing: A hybrid modeling framework by integrating wavelet transformation and machine learning techniques. Agricultural Water Management, 321. https://doi.org/10.1016/j.agwat.2025.109922