APPLICATION OF REMOTE SENSING IN THE ESTIMATION OF AGRICULTURAL CROPS YIELDS: A BIBLIOMETRIC REVIEW
Abstract
Keywords
Full Text:
PDFReferences
Abdelmoneim, A.A., Khadra, R., Elkamouh, A., Derardja, B. and Dragonetti, G., 2024. Towards Affordable Precision Irrigation: An Experimental Comparison of Weather-Based and Soil Water Potential-Based Irrigation Using Low-Cost IoT-Tensiometers on Drip Irrigated Lettuce. Sustainability, [online] 16(1), p.306. https://doi.org/10.3390/su16010306
Aiolfi, S. and Luceri, B., 2024. See you on the Metaverse: A bibliometric expedition through the Metaverse landscape. Technological Forecasting and Social Change, [online] 207, p.123605. https://doi.org/10.1016/j.techfore.2024.123605
Akiyama, T., Inoue, Y., Shibayama, M., Awaya, Y. and Tanaka, N., 1996. Monitoring and predicting crop growth and analysing agricultural ecosystems by remote sensing. Agricultural and Food Science in Finland, [online] 5(3), pp.367–376. https://doi.org/10.23986/afsci.72741
Amankulova, K., Farmonov, N. and Mucsi, L., 2023. Time-series analysis of Sentinel-2 satellite images for sunflower yield estimation. Smart Agricultural Technology, [online] 3. https://doi.org/10.1016/j.atech.2022.100098
Bastiaanssen, W.G.M. and Ali, S., 2003. A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agriculture, Ecosystems and Environment, [online] 94(3), pp.321–340. https://doi.org/10.1016/S0167-8809(02)00034-8
Bastian, M., Heymann, S. and Jacomy, M., 2009. Gephi: An Open Source Software for Exploring and Manipulating Networks. Proceedings of the International AAAI Conference on Web and Social Media, [online] 3(1), pp.361–362. https://doi.org/10.1609/icwsm.v3i1.13937
Bauer, M.E., 1975. The role of remote sensing in determining the distribution and yield of crops. Advances in Agronomy, [online] 27(C), pp.271–304. https://doi.org/10.1016/S0065-2113(08)70012-9
Bose, P., Kasabov, N.K., Bruzzone, L. and Hartono, R.N., 2016. Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series. IEEE Transactions on Geoscience and Remote Sensing, [online] 54(11), pp.6563–6573. https://doi.org/10.1109/TGRS.2016.2586602
Celik, M.F., Isik, M.S., Taskin, G., Erten, E. and Camps-Valls, G., 2023. Explainable Artificial Intelligence for Cotton Yield Prediction With Multisource Data. IEEE Geoscience and Remote Sensing Letters, [online] 20. https://doi.org/10.1109/LGRS.2023.3303643
Chew, B.J., Wiratama, W. and Goh, M.H., 2023. CANOPY NITROGEN ESTIMATION ON COTTON PLANT USING SATELLITE IMAGERY. In: Altan O., Sunar F., and Klein D., eds. Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci. - ISPRS Arch. [online] International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. International Society for Photogrammetry and Remote Sensing. pp.73–79. https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-73-2023
Cobo, M.J., López-Herrera, A.G., Herrera-Viedma, E. and Herrera, F., 2011. An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, [online] 5(1), pp.146–166. https://doi.org/10.1016/j.joi.2010.10.002
Conrad, C., Fritsch, S., Zeidler, J., Rücker, G. and Dech, S., 2010. Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data. Remote Sensing, [online] 2(4), pp.1035–1056. https://doi.org/10.3390/rs2041035
Courault, D., Hossard, L., Demarez, V., Dechatre, H., Irfan, K., Baghdadi, N., Flamain, F. and Ruget, F., 2021. STICS crop model and Sentinel-2 images for monitoring rice growth and yield in the Camargue region. Agronomy for Sustainable Development, [online] 41(4). https://doi.org/10.1007/s13593-021-00697-w
Deines, J.M., Patel, R., Liang, S.-Z., Dado, W. and Lobell, D.B., 2021. A million kernels of truth: Insights into scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt. Remote Sensing of Environment, [online] 253. https://doi.org/10.1016/j.rse.2020.112174
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N. and Lim, W.M., 2021. How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, [online] 133, pp.285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Donthu, N., Kumar, S. and Pattnaik, D., 2020. Forty-five years of Journal of Business Research: A bibliometric analysis. Journal of Business Research, [online] 109, pp.1–14. https://doi.org/10.1016/j.jbusres.2019.10.039.
van Eck, N.J. and Waltman, L., 2010. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, [online] 84(2), pp.523–538. https://doi.org/10.1007/s11192-009-0146-3
van Eck, N.J. and Waltman, L., 2014. CitNetExplorer: A new software tool for analyzing and visualizing citation networks. Journal of Informetrics, [online] 8(4), pp.802–823. https://doi.org/10.1016/j.joi.2014.07.006
Faqe Ibrahim, G.R., Rasul, A. and Abdullah, H., 2023. Sentinel?2 accurately estimated wheat yield in a semi-arid region compared with Landsat 8. International Journal of Remote Sensing, [online] 44(13), pp.4115–4136. https://doi.org/10.1080/01431161.2023.2232542
French, A.N., Jacob, F., Anderson, M.C., Kustas, W.P., Timmermans, W., Gieske, A., Su, Z., Su, H., McCabe, M.F., Li, F., Prueger, J. and Brunsell, N., 2005. Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA). Remote Sensing of Environment, [online] 99(1–2), pp.55–65. https://doi.org/10.1016/j.rse.2005.05.015
Gao, F., Anderson, M.C., Zhang, X., Yang, Z., Alfieri, J.G., Kustas, W.P., Mueller, R., Johnson, D.M. and Prueger, J.H., 2017. Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery. Remote Sensing of Environment, [online] 188, pp.9–25. https://doi.org/10.1016/j.rse.2016.11.004
Gao, F. and Zhang, X., 2021. Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities. Journal of Remote Sensing (United States), [online] 2021. https://doi.org/10.34133/2021/8379391
Gomarasca, M.A., Tornato, A., Spizzichino, D., Valentini, E., Taramelli, A., Satalino, G., Vincini, M., Boschetti, M., Colombo, R., Rossi, L., Mondino, E.B., Perotti, L., Alberto, W. and Villa, F., 2019. Sentinel for applications in agriculture. In: Ray S.S., Navalgund R., and Justice C., eds. Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci. - ISPRS Arch. [online] International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. International Society for Photogrammetry and Remote Sensing. pp.91–98. https://doi.org/10.5194/isprs-archives-XLII-3-W6-91-2019
Immitzer, M., Vuolo, F. and Atzberger, C., 2016. First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sensing, [online] 8(3), p.166. https://doi.org/10.3390/rs8030166
Kaplan, G., Fine, L., Lukyanov, V., Malachy, N., Tanny, J. and Rozenstein, O., 2023. Using Sentinel-1 and Sentinel-2 imagery for estimating cotton crop coefficient, height, and Leaf Area Index. Agricultural Water Management, [online] 276. https://doi.org/10.1016/j.agwat.2022.108056
Kaur, R., Tiwari, R.K., Maini, R. and Singh, S., 2023. A Framework for Crop Yield Estimation and Change Detection Using Image Fusion of Microwave and Optical Satellite Dataset. Quaternary, [online] 6(2). https://doi.org/10.3390/quat6020028
Lakmal, D., Kugathasan, K., Nanayakkara, V., Jayasena, S., Perera, A.S. and Fernando, L., 2019. Brown planthopper damage detection using remote sensing and machine learning. In: Wani M.A., Khoshgoftaar T.M., Wang D., Wang H., and Seliya N., eds. Proc. - IEEE Int. Conf. Mach. Learn. Appl., ICMLA. [online] Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019. Institute of Electrical and Electronics Engineers Inc. pp.97–104. https://doi.org/10.1109/ICMLA.2019.00024
Lambert, M.-J., Traoré, P.C.S., Blaes, X., Baret, P. and Defourny, P., 2018. Estimating smallholder crops production at village level from Sentinel-2 time series in Mali’s cotton belt. Remote Sensing of Environment, [online] 216, pp.647–657. https://doi.org/10.1016/j.rse.2018.06.036
Moral-Muñoz, J.A., Herrera-Viedma, E., Santisteban-Espejo, A. and Cobo, M.J., 2020. Software tools for conducting bibliometric analysis in science: An up-to-date review. Profesional de la información, [online] 29(1). https://doi.org/10.3145/epi.2020.ene.03
Muhuri, P.K., Shukla, A.K. and Abraham, A., 2019. Industry 4.0: A bibliometric analysis and detailed overview. Engineering Applications of Artificial Intelligence, [online] 78, pp.218–235. https://doi.org/10.1016/j.engappai.2018.11.007
Noyons, E. c. m., Moed, H. f. and Luwel, M., 1999. Combining mapping and citation analysis for evaluative bibliometric purposes: A bibliometric study. Journal of the American Society for Information Science, [online] 50(2), pp.115–131. https://doi.org/10.1002/(SICI)1097-4571(1999)50:2<115::AID-ASI3>3.0.CO;2-J
Pantazi, X.E., Moshou, D., Alexandridis, T., Whetton, R.L. and Mouazen, A.M., 2016. Wheat yield prediction using machine learning and advanced sensing techniques. Computers and Electronics in Agriculture, [online] 121, pp.57–65. https://doi.org/10.1016/j.compag.2015.11.018
Prasad, A.K., Chai, L., Singh, R.P. and Kafatos, M., 2006. Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation, [online] 8(1), pp.26–33. https://doi.org/10.1016/j.jag.2005.06.002
Ren, T., Xu, H., Cai, X., Yu, S. and Qi, J., 2022. Smallholder Crop Type Mapping and Rotation Monitoring in Mountainous Areas with Sentinel-1/2 Imagery. Remote Sensing, [online] 14(3). https://doi.org/10.3390/rs14030566
Sagan, V., Maimaitijiang, M., Sidike, P., Maimaitiyiming, M., Erkbol, H., Hartling, S., Peterson, K.T., Peterson, J., Burken, J. and Fritschi, F., 2019. Uav/satellite multiscale data fusion for crop monitoring and early stress detection. In: Vosselman G., Oude Elberink S.J., and Yang M.Y., eds. Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci. - ISPRS Arch. [online] International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. International Society for Photogrammetry and Remote Sensing. pp.715–722. https://doi.org/10.5194/isprs-archives-XLII-2-W13-715-2019
Sakamoto, T., Gitelson, A.A., Nguy-Robertson, A.L., Arkebauer, T.J., Wardlow, B.D., Suyker, A.E., Verma, S.B. and Shibayama, M., 2012. An alternative method using digital cameras for continuous monitoring of crop status. Agricultural and Forest Meteorology, [online] 154–155, p.113. https://doi.org/10.1016/j.agrformet.2011.10.014
Santana, L.S., Ferraz, G.A.E.S., Teodoro, A.J.D.S., Santana, M.S., Rossi, G. and Palchetti, E., 2021. Advances in Precision Coffee Growing Research: A Bibliometric Review. Agronomy, [online] 11(8), p.1557. https://doi.org/10.3390/agronomy11081557
Semeraro, T., Mastroleo, G., Pomes, A., Luvisi, A., Gissi, E. and Aretano, R., 2019. Modelling fuzzy combination of remote sensing vegetation index for durum wheat crop analysis. Computers and Electronics in Agriculture, [online] 156, pp.684–692. https://doi.org/10.1016/j.compag.2018.12.027
Shao, Y., Fan, X., Liu, H., Xiao, J., Ross, S., Brisco, B., Brown, R. and Staples, G., 2001. Rice monitoring and production estimation using multitemporal RADARSAT. Remote Sensing of Environment, [online] 76(3), pp.310–325. https://doi.org/10.1016/S0034-4257(00)00212-1
Shao, Z., Cai, J., Fu, P., Hu, L. and Liu, T., 2019. Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product. Remote Sensing of Environment, [online] 235. https://doi.org/10.1016/j.rse.2019.111425
Shi, J., Wang, C., Wang, J., Xi, X., Yang, X. and Ding, X., 2022. Study on the LAI and FPAR inversion of maize from airborne LiDAR and hyperspectral data. International Journal of Remote Sensing, [online] 43(13), pp.4793–4809. https://doi.org/10.1080/01431161.2022.2121187
Sozzi, M., Cantalamessa, S., Cogato, A., Kayad, A. and Marinello, F., 2022. Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms. Agronomy, [online] 12(2). https://doi.org/10.3390/agronomy12020319
Suarez, L.A., Robson, A., McPhee, J., O’Halloran, J. and van Sprang, C., 2020. Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data. Precision Agriculture, [online] 21(6), pp.1304–1326. https://doi.org/10.1007/s11119-020-09722-6
Terliksiz, A.S. and Altýlar, D.T., 2019. Use Of Deep Neural Networks For Crop Yield Prediction: A Case Study Of Soybean Yield in Lauderdale County, Alabama, USA. In: 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). [online] 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). pp.1–4. https://doi.org/10.1109/Agro-Geoinformatics.2019.8820257
Unganai, L.S. and Kogan, F.N., 1998. Drought monitoring and corn yield estimation in southern Africa from AVHRR data. Remote Sensing of Environment, [online] 63(3), pp.219–232. https://doi.org/10.1016/S0034-4257(97)00132-6
Vallentin, C., Harfenmeister, K., Itzerott, S., Kleinschmit, B., Conrad, C. and Spengler, D., 2022. Suitability of satellite remote sensing data for yield estimation in northeast Germany. Precision Agriculture, [online] 23(1), pp.52–82. https://doi.org/10.1007/s11119-021-09827-6
Verma, S. and Gustafsson, A., 2020. Investigating the emerging COVID-19 research trends in the field of business and management: A bibliometric analysis approach. Journal of Business Research, [online] 118, pp.253–261. https://doi.org/10.1016/j.jbusres.2020.06.057
Waleed, M., Mubeen, M., Ahmad, A., Habib-ur-Rahman, M., Amin, A., Farid, H.U., Hussain, S., Ali, M., Qaisrani, S.A., Nasim, W., Javeed, H.M.R., Masood, N., Aziz, T., Mansour, F. and EL Sabagh, A., 2022. Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation. Scientific Reports, [online] 12(1). https://doi.org/10.1038/s41598-022-17454-y
Wang, J., Dai, Q., Shang, J., Jin, X., Sun, Q., Zhou, G. and Dai, Q., 2019. Field-scale rice yield estimation using sentinel-1A synthetic aperture radar (SAR) data in coastal saline region of Jiangsu Province, China. Remote Sensing, [online] 11(19). https://doi.org/10.3390/rs11192274
Wang, M., Wang, J., Cui, Y., Liu, J. and Chen, L., 2022. Agricultural Field Boundary Delineation with Satellite Image Segmentation for High-Resolution Crop Mapping: A Case Study of Rice Paddy. Agronomy, [online] 12(10). https://doi.org/10.3390/agronomy12102342
Wang, N., Xue, J., Peng, J., Biswas, A., He, Y. and Shi, Z., 2020. Integrating remote sensing and landscape characteristics to estimate soil salinity using machine learning methods: A case study from southern xinjiang, china. Remote Sensing, [online] 12(24), pp.1–21. https://doi.org/10.3390/rs12244118
Xiao, Z., Liang, S., Wang, J., Xiang, Y., Zhao, X. and Song, J., 2016. Long-Time-Series Global Land Surface Satellite Leaf Area Index Product Derived from MODIS and AVHRR Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, [online] 54(9), pp.5301–5318. https://doi.org/10.1109/TGRS.2016.2560522
Yang, C. and Anderson, G.L., 2000. Mapping grain sorghum yield variability using airborne digital videography. Precision Agriculture, [online] 2(1), pp.7–23. https://doi.org/10.1023/A:1009928431735
Yang, Q., Shi, L., Han, J., Zha, Y. and Zhu, P., 2019. Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images. Field Crops Research, [online] 235, pp.142–153. https://doi.org/10.1016/j.fcr.2019.02.022
Yli-Heikkila, M., Wittke, S., Luotamo, M., Puttonen, E., Sulkava, M., Pellikka, P., Heiskanen, J. and Klami, A., 2022. Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network. Remote Sensing, [online] 14(17). https://doi.org/10.3390/rs14174193
Yue, J., Yang, G., Li, C., Li, Z., Wang, Y., Feng, H. and Xu, B., 2017. Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sensing, [online] 9(7). https://doi.org/10.3390/rs9070708
Zhang, H., Zhang, Y., Liu, K., Lan, S., Gao, T. and Li, M., 2023. Winter wheat yield prediction using integrated Landsat 8 and Sentinel-2 vegetation index time-series data and machine learning algorithms. Computers and Electronics in Agriculture, [online] 213. https://doi.org/10.1016/j.compag.2023.108250
Zhang, P.-P., Zhou, X.-X., Wang, Z.-X., Mao, W., Li, W.-X., Yun, F., Guo, W.-S. and Tan, C.-W., 2020. Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat. Scientific Reports, [online] 10(1). https://doi.org/10.1038/s41598-020-62125-5
Zhou, X., Wang, P., Tansey, K., Zhang, S., Li, H. and Tian, H., 2020. Reconstruction of time series leaf area index for improving wheat yield estimates at field scales by fusion of Sentinel-2, -3 and MODIS imagery. Computers and Electronics in Agriculture, [online] 177. https://doi.org/10.1016/j.compag.2020.105692
Zhou, X., Zheng, H.B., Xu, X.Q., He, J.Y., Ge, X.K., Yao, X., Cheng, T., Zhu, Y., Cao, W.X. and Tian, Y.C., 2017. Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS Journal of Photogrammetry and Remote Sensing, [online] 130, pp.246–255. https://doi.org/10.1016/j.isprsjprs.2017.05.003
Zhu, L., Liu, X., Wang, Z. and Tian, L., 2023. High-precision sugarcane yield prediction by integrating 10-m Sentinel-1 VOD and Sentinel-2 GRVI indexes. European Journal of Agronomy, [online] 149. https://doi.org/10.1016/j.eja.2023.126889
URN: http://www.revista.ccba.uady.mx/urn:ISSN:1870-0462-tsaes.v28i1.56132
DOI: http://dx.doi.org/10.56369/tsaes.5613
Copyright (c) 2025 Irene Gutierrez Mora, Aleida Selene Hernández Cázares, Juan Valente Hidalgo Contreras, Jose Luis López Ayala, Joel Velasco Velasco

This work is licensed under a Creative Commons Attribution 4.0 International License.