Maize (Zea Mays L.) Yield Estimation Using High Spatial and Temporal Resolution Sentinel-2 Remote Sensing Data

References

• Andreo, V., M. Belgiu, D. Brito Hoyos, F. Osei, and C. Provensal, y A. Stein. 2019. Rodents and satellites: Predicting mice abundance and distribution with Sentinel-2 data. Ecological Informatics 51:157–67. doi:10.1016/J.ECOINF.2019.03.001.
   Web of Science ®Google Scholar
• Araya, S., B. Ostendorf, G. Lyle, and M. Lewis. 2018. CropPhenology: An R package for extracting crop phenology from time series remotely sensed vegetation index imagery. Ecological Informatics 46:45–56. doi:10.1016/j.ecoinf.2018.05.006.
   Web of Science ®Google Scholar
• Ballesteros, R., J. F. Ortega, D. Hernández, and M. A. Moreno. 2014. Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: Description of image acquisition and processing. Precision Agriculture 15 (6):579–92. doi:10.1007/s11119-014-9355-8.
   Web of Science ®Google Scholar
• Battude, M., A. Al Bitar, D. Morin, J. Cros, M. Huc, C. Marais Sicre, V. Le Dantec, and V. Demarez. 2016. Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data. Remote Sensing of Environment 184:668–81. doi:10.1016/j.rse.2016.07.030.
   Web of Science ®Google Scholar
• Borràs, J., J. Delegido, A. Pezzola, M. Pereira-Sandoval, G. Morassi, and G. Camps-Valls. 2017. Land use classification from Sentinel-2 imagery. Revista de Teledetección 0 (48):55–66. doi:10.4995/RAET.2017.7133.
   Google Scholar
• Cao, R., M. Shen, J. Zhou, and J. Chen. 2018. Modeling vegetation green-up dates across the Tibetan Plateau by including both seasonal and daily temperature and precipitation. Agricultural and Forest Meteorology 249:176–86. doi:10.1016/j.agrformet.2017.11.032.
   Web of Science ®Google Scholar
• Chazarreta, Y. D., J. Ignacio Amas, A. Gabriel Cirilo, and M. Elena Otegui. 2018. Llenado y secado del grano en hIbridos de maIz liberados entre 1980 y 201. Rta 10 (38):22–25. http://repositorio.inta.gob.ar:80/handle/20.500.12123/4384.
   Google Scholar
• Cirilo, A. G., F. Andrade, S. A. Uhart, and M. E. Otegui. 2012. Ecofisiologia del cultivo de maiz. Bases para el Manejo del Cultivo de Maíz, ed. G. H Eyhérabide, 25–57. Buenos Aires, Argentina: INTA. https://apunteca.usal.edu.ar/id/eprint/2611/
   Google Scholar
• Congedo, L. 2021. Semi-automatic classification plugin documentation. Release 7.9.5.1. 2021.
   Google Scholar
• Cozzolino, D. 2017. The role of near-infrared sensors to measure water relationships in crops and plants. Applied Spectroscopy Reviews 52 (10):837–49. doi:10.1080/05704928.2017.1331446.
   Web of Science ®Google Scholar
• De Bernardis, C., F. Vicente-Guijalba, T. Martinez-Marin, and J. M. Lopez-Sanchez. 2016. Particle filter approach for real-time estimation of crop phenological states using time series of NDVI images. Remote Sensing 8 (7):610. doi:10.3390/RS8070610.
   Web of Science ®Google Scholar
• De La Casa, A., and G. Ovando. 2007. Integración del Índice de Vegetación de la Diferencia Normalizada (NDVI) y del ciclo fenológico de maíz para estimar el rendimiento a escala departamental en Córdoba, Argentina. Agricultura Tecnica 67 (4):362–71. doi:10.4067/s0365-28072007000400004.
   Google Scholar
• D’Odorico, P., A. Gonsamo, A. Damm, and M. E. Schaepman. 2013. Experimental evaluation of sentinel-2 spectral response functions for NDVI time-series continuity. IEEE Transactions on Geoscience & Remote Sensing 51 (3):1336–48. doi:10.1109/TGRS.2012.2235447.
   Web of Science ®Google Scholar
• Dong, C., G. Zhao, Y. Qin, and H. Wan. 2019. Area extraction and spatiotemporal characteristics of winter wheat–summer maize in Shandong Province using NDVI time series. PLos One 14 (12):e0226508. doi:10.1371/JOURNAL.PONE.0226508.
   PubMed Web of Science ®Google Scholar
• Drusch, M., U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, C. Isola, P. Laberinti, P. Martimort, et al. 2012. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment 120:25–36. doi:10.1016/J.RSE.2011.11.026.
   Web of Science ®Google Scholar
• Evans, L. T., and R. A. Fischer. 1999. Yield potential: Its definition, measurement, and significance. Crop Science 39 (6):1544–51. doi:10.2135/cropsci1999.3961544x.
   Web of Science ®Google Scholar
• Frampton, W. J., J. Dash, G. Watmough, and E. J. Milton. 2013. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. Isprs Journal of Photogrammetry & Remote Sensing 82:83–92. doi:10.1016/j.isprsjprs.2013.04.007.
   Web of Science ®Google Scholar
• Gao, Z., H. Y. Feng, X. G. Liang, L. Zhang, S. Lin, X. Zhao, S. Shen, L. L. Zhou, and S. L. Zhou. 2018. Limits to maize productivity in the North China Plain: A comparison analysis for spring and summer maize. Field Crops Research 228:39–47. doi:10.1016/j.fcr.2018.08.022.
   Web of Science ®Google Scholar
• Gavilán, S. A., J. Ignacio Pastore, I. Quignard, N. Damián Marasco, and P. Gilberto Aceñolaza. 2019. Energy balance model as real evapotranspiration estimator with satellite and meteorological data. Interciencia 44 (7):400–07. https://ri.conicet.gov.ar/handle/11336/105497.
   Web of Science ®Google Scholar
• Gitelson, A., A. Solovchenko, and A. Viña. 2020. Foliar absorption coefficient derived from reflectance spectra: A gauge of the efficiency of in situ light-capture by different pigment groups. Journal of Plant Physiology 254:153277. doi:10.1016/j.jplph.2020.153277.
   PubMed Web of Science ®Google Scholar
• Holzworth, D. P., N. I. Huth, P. G. DeVoil, E. J. Zurcher, N. I. Herrmann, G. McLean, K. Chenu, E. J. van Oosterom, V. Snow, C. Murphy, et al. 2014. APSIM – Evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software 62:327–50. doi:10.1016/j.envsoft.2014.07.009.
   Web of Science ®Google Scholar
• Huang, L. S., and J. Chen. 2008. Analysis of variance, coefficient of determination and F-test for local polynomial regression. Annals of Statistics 36 (5). doi:10.1214/07-AOS531.
   Web of Science ®Google Scholar
• Huang, S., L. Tang, J. P. Hupy, Y. Wang, and G. Shao. 2021. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research 32 (1):1–6. doi:10.1007/s11676-020-01155-1.
   Web of Science ®Google Scholar
• Jin, X., L. Kumar, Z. Li, H. Feng, X. Xingang, G. Yang, and J. Wang. 2018. A review of data assimilation of remote sensing and crop models. The European Journal of Agronomy 92:141–52. doi:10.1016/j.eja.2017.11.002.
   Web of Science ®Google Scholar
• Kadam, S. A., S. D. Gorantiwar, S. R. Dahiwalkar, S. R. Satpute, and P. G. Popale. 2017. Relationship between spectral reflectance, NDVI and water stress conditions of soybean (Glycine max L.). Journal of Agriculture Research and Technology 42 (3):149–53.
   Google Scholar
• Lee, S. H., M. A. Bailey, M. A. R. Mian, T. E. Carter, D. A. Ashley, R. S. Hussey, W. A. Parrott, and H. R. Boerma. 1996. Molecular markers associated with soybean plant height, lodging, and maturity across locations. Crop science 36 (3):728–35. doi:10.2135/cropsci1996.0011183X003600030035x.
   Web of Science ®Google Scholar
• Liu, J., J. Shang, B. Qian, T. Huffman, Y. Zhang, T. Dong, Q. Jing, and T. Martin. 2019. Crop Yield Estimation Using Time-Series MODIS Data and the Effects of Cropland Masks in Ontario, Canada. Remote Sensing 11 (20):2419. doi:10.3390/RS11202419.
   Web of Science ®Google Scholar
• Main-Knorn, M., B. Pflug, J. Louis, V. Debaecker, U. Müller-Wilm, and F. Gascon. 2017. Sen2Cor for Sentinel-2. En spiedigitallibrary.org 3. doi:10.1117/12.2278218.
   Google Scholar
• Marshall, M., P. Thenkabail, T. Biggs, and K. Post. 2016. Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation). Agricultural and Forest Meteorology 218-219:122–34. doi:10.1016/j.agrformet.2015.12.025.
   Web of Science ®Google Scholar
• Martinez, G. A., D. L. Mazzanti, C. Quintana, A. F. Zucol, M. M. Colobig, G. S. Hassan, M. Brea, and E. Passeggi. 2013. Geoarchaeological and Paleoenvironmental context of the human settlement in the Eastern Tandilia Range, Argentina. Quaternary International 299:23–37. doi:10.1016/j.quaint.2012.12.032,
   Web of Science ®Google Scholar
• Mihai, H., and S. Florin. 2016. Biomass prediction model in maize based on satellite images. AIP Conference Proceedings, American Institute of Physics Inc Vol. 1738. 10.1063/1.4952132.
   Google Scholar
• Mubeen, M., A. Ahmad, A. Wajid, T. Khaliq, and A. Bakhsh. 2013. Evaluating CSM-CERES-maize model for irrigation scheduling in semi-arid conditions of Punjab, Pakistan. International Journal of Agriculture & Biology 15 (1):1–10.
   Web of Science ®Google Scholar
• Mubeen, M., A. Ahmad, A. Wajid, T. Khaliq, H. Mohkum Hammad, R. S. Sultana, S. Ahmad, S. Fahad, and W. Nasim. 2016. Application of CSM-CERES-maize model in optimizing irrigated conditions. Outlook on agriculture 45 (3):173–84. doi:10.1177/0030727016664464.
   Web of Science ®Google Scholar
• Noor, N. M., M. M. Al Bakri Abdullah, A. S. Yahaya, and N. A. Ramli. 2015. Comparison of linear interpolation method and mean method to replace the missing values in environmental data set. Materials Science Forum 803:278–81. doi:10.4028/0000www.scientific.net/MSF.803.278.
   Google Scholar
• Ostertagová, E. 2012. Modelling using polynomial regression. Procedia Engineering 48:500–06. doi:10.1016/j.proeng.2012.09.545.
   Google Scholar
• Panigrahi, N., and B. Sankar Das. 2021. Evaluation of regression algorithms for estimating leaf area index and canopy water content from water stressed rice canopy reflectance. Information Processing in Agriculture 8 (2):284–98. doi:10.1016/j.inpa.2020.06.002.
   Google Scholar
• Ressl, C., and N. Pfeifer. 2018. Evaluation of the elevation model influence on the orthorectification of Sentinel-2 satellite images over Austria. European Journal of Remote Sensing 51 (1):693–709. doi:10.1080/22797254.2018.1478676.
   Web of Science ®Google Scholar
• Rezaei, E. E., G. Ghazaryan, J. González, N. Cornish, O. Dubovyk, and S. Siebert. 2021. The use of remote sensing to derive maize sowing dates for large-scale crop yield simulations. International journal of biometeorology 65 (4):565–76. doi:10.1007/s00484-020-02050-4.
   PubMed Web of Science ®Google Scholar
• Schwalbert, R. A., T. J. Amado, L. Nieto, S. Varela, G. M. Corassa, T. A. Horbe, C. W. Rice, N. R. Peralta, and I. A. Ciampitti. 2018. Forecasting maize yield at field scale based on high-resolution satellite imagery. Biosystems Engineering 171:179–92. doi:10.1016/j.biosystemseng.2018.04.020.
   Web of Science ®Google Scholar
• Schwieder, M., M. Buddeberg, K. Kowalski, K. Pfoch, J. Bartsch, H. Bach, J. Pickert, and P. Hostert. 2020. Estimating Grassland Parameters from Sentinel-2: A Model Comparison Study. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science 88 (5):379–90. doi:10.1007/S41064-020-00120-1.
   Web of Science ®Google Scholar
• Sereda, I. I., and O. V. Tutubalina. 2019. Application of SAFY Crop Growth Model for Maize Yield Forecast. Information Technologies in Remote Sensing of the Earth-RORSE: 45–48. 2018. doi:10.21046/rorse2018.48.
   Google Scholar
• Shanahan, J. F., J. S. Schepers, D. D. Francis, G. E. Varvel, W. W. Wilhelm, J. M. Tringe, M. R. Schlemmer, and D. J. Major. 2001. Use of remote-sensing imagery to estimate corn grain yield. Agronomy journal 93 (3):583–89. doi:10.2134/agronj2001.933583x.
   Web of Science ®Google Scholar
• Szantoi, Z., and P. Strobl. 2019. Copernicus Sentinel-2 Calibration and Validation. European Journal of Remote Sensing 52 (1):253–55. doi:10.1080/22797254.2019.1582840.
   Web of Science ®Google Scholar
• Tandzi, L. N., and C. S. Mutengwa. 2020. Estimation of Maize (Zea mays L.) Yield Per Harvest Area: Appropriate methods. Agronomy 10 (1):29. doi:10.3390/agronomy10010029.
   Google Scholar
• Teal, R. K., B. Tubana, K. Girma, K. W. Freeman, D. B. Arnall, O. Walsh, and W. R. Raun. 2006. In‐season prediction of corn grain yield potential using normalized difference vegetation index. Agronomy journal 98 (6):1488–94. doi:10.2134/agronj2006.0103.
   Web of Science ®Google Scholar
• Vaiman, N. 2018. Comparación de índices climáticos y espectrales en la estimación de rendimiento de maíz y soja a nivel departamental en Entre Ríos ( Doctoral dissertation), Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales.
   Google Scholar
• Vazquez, P., L. Zulaica, and N. Sequeira. 2017. Tasas de cambio de uso del suelo y agriculturización en el partido de Lobería, Argentina. Revista de Investigaciones de la Facultad de Ciencias Agrarias-UNR 29:028–036. https://test-cienciasagronomicas.unr.edu.ar/journal/index.php/agronom/a.
   Google Scholar
• Vithu, P., and J. A. Moses. 2016. Machine vision system for food grain quality evaluation: A review. Trends in Food Science & Technology 56:13–20. doi:10.1016/j.tifs.2016.07.011.
   Web of Science ®Google Scholar
• Wang, Q., and P. M. Atkinson. 2018. Spatio-temporal fusion for daily Sentinel-2 images. Remote sensing of environment 204:31–42. doi:10.1016/j.rse.2017.10.046.
   Web of Science ®Google Scholar
• Wang, J., X. Xiao, R. Bajgain, P. Starks, J. Steiner, R. B. Doughty, and Q. Chang. 2019. Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images. Isprs Journal of Photogrammetry & Remote Sensing 154:189–201. doi:10.1016/j.isprsjprs.2019.06.007.
   Web of Science ®Google Scholar
• Xinbing, W., Y. Miao, Y. Guan, T. Xia, L. Junjun, and D. J. Mulla. 2016. An evaluation of two active canopy sensor systems for non-destructive estimation of spring maize biomass. En 2016 5th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2016. 10.1109/Agro-Geoinformatics.2016.7577610.
   Google Scholar
• Zhou, G., and X. Yin. 2018. Assessing nitrogen nutritional status, biomass and yield of cotton with NDVI, spad and petiole sap nitrate concentration. Experimental Agriculture 54 (4):531–48. doi:10.1017/S0014479717000229.
   Web of Science ®Google Scholar