APPLICATION OF REMOTE SENSING IN THE ESTIMATION OF AGRICULTURAL CROPS YIELDS: A BIBLIOMETRIC REVIEW

Irene Gutierrez Mora, Aleida Selene Hernández Cázares, Juan Valente Hidalgo Contreras, Jose Luis López Ayala, Joel Velasco Velasco

Abstract


Background: Agriculture, as a fundamental sector of the global economy, plays a key role in the socioeconomic fabric of countries. Obtaining accurate information on crop area and production is essential for promoting more efficient agricultural practices. Remote sensing has transformed modern agriculture by providing precise, real-time data on crop conditions. Bibliometric studies use quantitative methods to analyze the production and distribution of scientific information contained in various documents, aiding the understanding of knowledge trends in a specific area. These studies help to identify the evolution of remote sensing applied to agriculture. Objective: To analyze research on yield estimation/prediction in agricultural crops through satellite remote sensing in agricultural sciences. Methodology: Documents were retrieved from the Scopus® database using the equation “yield estimation AND crop AND satellite image OR mapping.” An exploratory analysis was conducted using Microsoft Excel®, and a bibliometric analysis was performed using VOSviewer® software. Results: A total of 818 documents from 1975 to 2023 were analyzed, grouped into six clusters, with prominent terms including deep learning, Sentinel 1 and 2, leaf area index, satellite, and digital mapping. The leading countries in scientific production are China and the USA. The main contributing fields of knowledge are Earth and Planetary Sciences (24%), Agricultural and Biological Sciences (18%), and Computer Science (17%). Implications: This study contributes to the synergistic exploration of agriculture, advanced data analysis techniques, and remote sensing to improve yield estimation by integrating satellite imagery and geographic data to enhance calibration and spatiotemporal accuracy. Conclusions: Four key components were identified in yield estimation through satellite imagery: 1) Artificial intelligence tools, 2) Use of NIR and SWIR bands, 3) Use and generation of NDVI, LAI, and biomass indices, and 4) Statistics applied to data science, correlation coefficients, and time series analysis.

Keywords


crop; yield estimation; satellite image; mapping.

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References


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URN: http://www.revista.ccba.uady.mx/urn:ISSN:1870-0462-tsaes.v28i1.56132

DOI: http://dx.doi.org/10.56369/tsaes.5613

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Copyright (c) 2025 Irene Gutierrez Mora, Aleida Selene Hernández Cázares, Juan Valente Hidalgo Contreras, Jose Luis López Ayala, Joel Velasco Velasco

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