Comparative evaluation of vegetation indices to estimate yield level of Vicia villosa Roth in the Central-West Pampas, Argentine
Submitted: 2025-12-11
|Accepted: 2026-03-30
|Published: 2026-05-13
Copyright (c) 2026 María Belén D'Amico, Fabián Marini, Nicanor Ponti Gonzalez, Juan Pablo Renzi Pugni, Guillermo Chantre
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Keywords:
legume, yield, model, estimation, vegetation indices
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Abstract:
The increasing availability of high-resolution satellite imagery, georeferenced field measurements, and big data management tools facilitates the development of predictive models that improve agricultural planning. This type of support is particularly valuable in regions like the central-western pampas (Argentine) where the adoption of cover crops such as Vicia villosa Roth is limited due to their high yield variability. This study evaluates the individual capacity of different vegetation indices derived from Sentinel-2 imagery to serve as input variables in models for predicting vetch seed yield. Four groups of indices, calculated from different spectral bands and across multiple dates between vegetation growth and seed maturation were considered. For each index, temporal observations were stacked to form a multiband image and associated with yield measurements recorded by harvesters. Using this information, supervised classification models were trained and validated using the Random Forest algorithm. The study was conducted with available data from seven fields in the region. Results showed that indices derived from red-edge bands exhibited the highest discriminative capacity, even when the input stack was reduced to observations from the final stages of the crop phenological cycle. In addition, certain indices incorporating corrections related to soil background effects or photosynthetically active area also achieved high classification performance. This analysis contributes to establishing a solid base for developing a predictive tool applicable to vetch seed production in central-western Pampas.
References:
Amankulova, K., Farmonov, N., Akramova, P., Tursunov, I., Mucsi, L. (2023). Comparison of PlanetScope, Sentinel-2, and Landsat 8 data in soybean yield estimation within-field variability with random forest regression. Heliyon, 9(6). https://doi.org/10.1016/j.heliyon.2023.e17432
Aslan, M.F., Sabanci, K., Aslan, B. (2024). Artificial Intelligence Techniques in Crop Yield Estimation Based on Sentinel-2 Data: A Comprehensive Survey. Sustainability, 16, 8277. https://doi.org/10.3390/su16188277
Atzberger, C., (2013). Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sensing, 5(2), 949–981. https://doi.org/10.3390/rs5020949
Bahadur, T. (2018). NDVI, NDBI & NDWI Calculation Using Landsat 7, 8. Publicado en linkedin. https://www.linkedin.com/pulse/ndvi-ndbi-ndwi-calculation-using-landsat-7-8-tek-bahadur-kshetri
Baret, F., Guyot, G., Major, D. (1989). TSAVI: A Vegetation Index Which Minimizes Soil Brightness Effects on LAI and APAR Estimation. 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium (pp. 1355-1358). https://doi.org/10.1109/IGARSS.1989.576128
Belgiu, M., Drăguț, L. 2016. Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Bishop, Y., Fienberg, S., Holland, P. (1975). Discrete multivariate analysis. Theory and practice. Cambridge: MIT Press. https://doi.org/10.1177/014662167700100218
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010950718922
Clark, A. 2007. Managing cover crops profitably. College Park: Sustainable Agriculture Research and Education (SARE) Program
Copernicus Sentinel-2 Mission. 2023. Sentinel-2 Level-1 Algorithm Theoretical Bases Document. Copernicus SentiWiki. Retrieved 2026, from https://sentiwiki.copernicus.eu
Daughtry, C.S.T., Walthall, C.L., Kim, M.S., de Colstoun, E.B., McMurtrey, J.E. 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2), 229–239. https://doi.org/10.1016/S0034-4257(00)00113-9
Delegido, J., Verrelst, J., Alonso, L., Moreno, J., (2011). Evaluation of Sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11(7), 7063–7080. https://doi.org/10.3390/s110707063
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., Bargellini, P. 2012. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, 25–36. https://doi.org/10.1016/j.rse.2011.11.026
Fuentes, I., Al-Shammari, D., Al-Nasrawi, A.K.M., Wang, Y., Wang, J., Lebrini, Y., Chen, Y., Jones, B.G., Bishop, T.F.A. 2025. The normalised difference vegetation index as an analytic tool for wheat crop yield prediction: A review and meta-analysis, Precision Agriculture, 26(55). https://doi.org/10.1007/s11119-025-10247-z
Gámez, A., Segarra, J., Vatter, T., Santesteban, L., Araus, J., Aranjuelo, I. 2025.Alfalfa yield estimation using the combination of Sentinel-2 and meteorological data. Field Crops Research, 326, 109857. https://doi.org/10.1016/j.fcr.2025.109857
Gamon, J.A., Filella, I., Penuelas, J. 1993. The dynamic 531-nanometer delta reflectance signal: a survey of twenty angiosperm species. HY Yamamoto, CM Smith, eds. Photosynthetic responses to the environment. Rockville, MD, USA. American Society of Plant Physiologists, 172–177.
Gavilán, S., Aceñolaza, P., Pastore, JI. (2023). Maize (Zea Mays L.) Yield Estimation Using High Spatial and Temporal Resolution Sentinel-2. Remote Sensing Data; Taylor & Francis; Communications in Soil Science and Plant Analysis; 54; 5-2023; 1-14. https://doi.org/10.1080/00103624.2023.2211115
Gilabert, M. A.; González-Piqueras, J.; García-Haro, F.J. (1997) Acerca de los Índices de Vegetación, Revista de Teledetección, 8.
Gitelson, A., Merzlyak, M. (1994). Quantitative Estimation of Chlorophyll-a Using Reflectance Spectra: Experiments with Autumn Chestnut and Maple Leaves. J. Photochem. Photobiol. B Biol.,22, 247–252. https://doi.org/10.1016/1011-1344(93)06963-4
Gitelson, A., Kaufman, Y., Merzlyak, M. (1996). Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS. Remote Sensing of Environment, 58, 289-298. http://dx.doi.org/10.1016/S0034-4257(96)00072-7
Gitelson, A., Kaufman, Y., Merzlyak, M. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves, Journal of Plant Physiology, Volume 160, Issue 3. https://doi.org/10.1078/0176-1617-00887.
Google Earth Engine Team. 2026. Editor de Código de Google Earth Engine (Playground). Google Developers. Retrieved 2026, from https://developers.google.com/earth-engine/guides/playground?hl=es-419&utm_source=chatgpt.com
Haboudane, D., Miller, J., Tremblay, N., Zarco-Tejada, P., Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ., 81(2-3): 416-426. https://doi.org/10.1016/S0034-4257(02)00018-4
Huang, H., Roy, D.P., Boschetti, B., Zhang, H. K., Yan, L., Kumar, S.S., Gomez-Dans, J., Li, J. (2016). Separability analysis of Sentinel-2A MSI data for burned area discrimination. Remote Sensing, 8(10), 873. https://doi.org/10.3390/rs8100873
Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/10.1016/0034-4257(88)90106-X
Huete, A. y Warrick A. (1990). Assessment of vegetation and soil water regimes in partial canopies with optical remotely sensed data. Remote Sensing of Environment. 32: 155-167. https://doi.org/10.1016/0034-4257(90)90015-E
INTA - Instituto Nacional de Tecnología Agropecuaria. (1990). Atlas de suelos de la República Argentina. Secretaría de Agricultura, Ganadería y Pesca. Proyecto PNUD ARG. 85/019, 731 pp.
Iranzo, C., Montorio, R., García-Martín, A. (2022). Estimación de la producción de cebada a partir de imágenes Sentinel-1, Sentinel-2 y variables climáticas, Revista de Teledetección, 59, 61-72. https://doi.org/10.4995/raet.2022.15099
Jensen, J. R. (1986). Introductory digital image processing. New Jersey: Prentice-Hall.
Justice, C.O., Vermote, E., Townshend, J.R., Defries, R., Roy, D.P., Hall, D.K., Lucht, W. (1998). The Moderate Resolution Spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1228–1249. https://doi.org/10.1109/36.701075
Kamilaris, A., y Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016
Kawashima, S., Nakatani, M, (1998). An algorithm for estimating chlorophyll content in leaves using a video camera. Ann. Bot. 81: 49-54. https://doi.org/10.1006/anbo.1997.0544.
Kayad, A.G., Al-Gaadi, K.A., Tola, E., Madugundu, R., Zeyada, A.M., Kalaitzidis, C. (2016). Assessing the spatial variability of alfalfa yield using satellite imagery and ground-based data. PLoS ONE, 11(6). https://doi.org/10.1371/journal.pone.0157166
Larrañaga Urien, A. (2016). Clasificación de cultivos a partir de observaciones de teledetección radar. Tesis Doctoral. Ed. Universidad Pública de Navarra, España.
Li, Y., Zhang, Z., Wang, F., Sun, X., 2023. A Method for Estimating Alfalfa (Medicago sativa L.) Forage Yield Based on Remote Sensing Data. Agronomy 2023, 13, 2597. https://doi.org/10.3390/agronomy13102597
Liaw, A. and Wiener, M. 2002. Classification and Regression by Random Forest. R News, 2, 18–22. https://journal.r-project.org/articles/RN-2002-022/RN-2002-022.pdf
Liu, H.Q., Huete, A.R. (1995). A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 457–465. https://doi.org/10.1109/TGRS.1995.8746027
Lobell, D.B., Thau, D., Seifert, C., Engle, E., Little, B. (2015). A scalable satellite-based crop yield mapper. PLoS ONE, 10(6), e0130133. https://doi.org/10.1016/j.rse.2015.04.021
Ma, C., Liu, M., Ding, F, Li, C., Cui, Y. Chen, W., Wang, Y. (2022). Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the SAFY crop growth model. Scientific Reports, 12, 5473. https://doi.org/10.1038/s41598-022-09535-9
Manafifard, M., y Huang, J. (2024) A comprehensive review on wheat yield prediction based on remote sensing. Multimedia Tools and Applications, online http://dx.doi.org/10.1007/ s11042-024-19820-6
Marini, F., D’Amico, M.B., Calandrini, G.L., Renzi, J.P., Chantre, G.R. 2025. Utilización de índices de vegetación satelitales para predecir niveles de rendimiento de Vicia villosa Roth. SADIO Electronic Journal of Informatics and Operations Research, 24(2), e076. https://doi.org/10.24215/15146774e076
Marini, M.F., Vergara, M.F., Krüger, H. 2007. Determinación del uso de la tierra en el partido de Guaminí (Argentina) mediante un estudio multitemporal con imágenes Landsat. Revista de Teledetección, 27, 80–88. https://aet.org.es/revistas/AET27-08.pdf
McFeeters, S. (1996) The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features. International Journal of Remote Sensing, 17, 1425-1432. http://dx.doi.org/10.1080/01431169608948714
Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358–371. https://doi.org/10.1016/j.biosystemseng.2012.08.009
Pasqualotto, Vicente, N., Delegido, J., Van Wittenberghe, S., Rinaldi, M., Moreno, J. (2018). Estimación de LAI verde mediante el nuevo índice SeLi para Sentinel-2. In: XVIII Simposio SELPER, La Habana, Cuba.
Pejak, D., Djurovic, D., Filipovic, V., Stojanovic, S., Protic, R. (2023). Soybean yield prediction using Sentinel-2 data and machine learning approaches. Heliyon, 9(8), e18668. https://doi.org/10.3390/rs14092256
Pereyra, F.X. 2012. Suelos de la Argentina. Buenos Aires: SEGEMAR-AACS-GAEA, ANALES N° 50, 178 pp.
QGIS.org, (2025). QGIS Geographic Information System. QGIS Association. Retrieved 2026, from http://www.qgis.org/
Qi, J., Chehbouni, A, Huete, A.R., Kerr, Y.H., Sorooshian, S. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2),119–126. https://doi.org/10.1016/0034-4257(94)90134-1
Qiao, K., Zhu, W., Xie, Z., Wu, S., Li, S. (2024). New three red-edge vegetation index (VI3RE) for crop seasonal LAI prediction using Sentinel-2 data. International Journal of Applied Earth Observation and Geoinformation, Volume 130, https://doi.org/10.1016/j.jag.2024.103894.
Renzi, J.P., Cantamutto, M.A. (2013). Vicias: agronomic bases for management in the Pampean region of Argentina. Ediciones INTA, 299 pp.
Renzi, J.P., Garayalde, A.F., Brus, J., Pohankova, T., Smýkal, P., Cantamutto, M.A. (2023). Environmental and agronomic determinants of hairy vetch seed yield in rainfed temperate agroecosystems. European Journal of Agronomy, 147, 126822. https://doi.org/10.1016/j.eja.2023.126822
Renzi, J.P., Verdinelli, M., Santiago, F., Bilbao, F., Cantamutto, M.A. 2025. Site-specific determinants of hairy vetch (Vicia villosa Roth) seed yield. Soil and Tillage Research, 253, 106682. https://doi.org/10.1016/j.still.2025.106682
Rouse, J.W., Haas, R.H., Schell, J.A., Deerino, D.W., Harlan, J.C. (1974). Monitoring the vernal advancement of retrogradation of natural vegetation. NASA/OSFC Final Report, 371 pp.
Roy, D.P., Wulder, M.A., Loveland, T.R., et al.2014. Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172.
https://doi.org/10.1016/j.rse.2014.02.001
Sharma, L.K., Bu, H., Denton, A., Franzen, D.W. 2015. Active-Optical Sensors Using Red NDVI Compared to Red Edge NDVI for Prediction of Corn Grain Yield in North Dakota, U.S.A. Sensors, 15(11), 27832–27853. https://doi.org/10.3390/s151127832
SIIA – Sistema Integrado de Información Agropecuaria. (2012). Estadísticas de Producción Agrícola. Ministerio de Agricultura de la Nación.
Silleos, N.G., Alexandridis, T.K., Gitas, I.Z., Perakis, K. (2006). Vegetation Indices: Advances Made in Biomass Estimation and Vegetation Monitoring in the Last 30 Years. Geocarto, 21, 21–28. https://doi.org/10.1080/10106040608542399.
Snapp, S.S., Swinton, S.M., Labarta, R., Mutch, D., Black, J.R., Leep, R., Nyiraneza, J., O’Neil, K. 2005. Evaluating cover crops for benefits, costs and performance within cropping system niches. Agronomy Journal, 97, 322–332. https://doi.org/10.2134/agronj2005.0322a
USDA (United States Department of Agriculture). (1999). Soil taxonomy, a basic system of soil classification for making and interpreting soil surveys. 2nd edition. Agricultural Handbook No. 436. Washington, DC. 869 pp.
USGS - United States Geological Survey. 2019. Soil Adjusted Vegetation Index (SAVI). USGS Landsat Program. https://www.usgs.gov/landsat-missions/landsat-soil-adjusted-vegetation-index
Viña, A., Gitelson, A.A, Nguy-Robertson, A.L. y Peng, Y. (2011): Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sens. Environ., vol. 115, pp. 3468–3478. https://doi.org/10.1016/j.rse.2011.08.010
Zhao, Y., Potgieter, A. B., Zhang, M., Wu, B., Hammer, G. L. (2020). Predicting wheat yield at the field scale by combining high-resolution Sentinel-2 imagery and crop modelling. Remote Sensing, 12, 1024. https://doi.org/10.3390/rs12061024
