Mapping of corn phenological stages using NDVI from OLI and MODIS sensors

Luan Peroni Venancio, Roberto Filgueiras, Fernando França da Cunha, Francisco Charles dos Santos Silva, Robson Argolo dos Santos, Everardo Chartuni Mantovani


Crop phenology knowledge is relevant to a series of actions related to its management and can be accessed through vegetation indexes. Thus, this study aimed to evaluate the use of the Normalized Difference Vegetation Index (NDVI), from images of OLI and MODIS sensors, to obtain phenological information from corn crops. To this end, we evaluated two corn cropping areas, irrigated by a central pivot, and located western Bahia state, Brazil. These areas were managed with high technology and had no record of biotic and abiotic stresses. NDVI showed a well-defined temporal pattern throughout the corn cycle, with a rapid increase at the beginning, stabilization at intermediate stages, and decreases at the end of the cycle. Excellent fits for polynomial equations were obtained to estimate NDVI as a function of days after sowing (DAS), with R² values of 0.96 and 0.95 for images of OLI and MODIS sensors, respectively. This demonstrates that both sensors could characterize corn canopy changes over time. NDVI ranges were correlated with the main phenological stages (PE), using the direct relationship between both variables (NDVI and PE) with days after sowing (DAS). For the beginning and end of each phenological stage, NDVI ranges were validated through model identity testing. NDVI proved to be a suitable parameter to assess corn phenology accurately and remotely. Finally, NDVI was also an important tool for detecting biotic and abiotic stresses throughout the crop cycle, and hence for decision making based on corn phenology.


Vegetation index; Satellite images; Remote sensing; Zea mays L.

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Adole, T., Dash, J., & Atkinson, P. M. (2016). Ecological informatics a systematic review of vegetation phenology in Africa. Ecological Informatics, 34, 117-128. doi: 10.1016/j.ecoinf.2016.05.004

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56. Rome, Italy: Food and Agriculture Organization of the United Nations.

Alvares, C. A., Stape, J. L., Sentelhas, P. C., Moraes Gonçalves, J. L. de, & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22(6), 711-728. doi: 10.1127/0941-2948/2013/0507

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. doi: 10.3390/rs5020949

Battude, M., Al Bitar, A., Morin, D., Cros, J., Huc, M., Marais Sicre, C., & Demarez, V. (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, 668-681. doi: 10.1016/j.rse.2016.07.030

Bergamaschi, H., Dalmago, G. A., Comiran, F., Bergonci, J. I., Müller, A. G., França, S., & Pereira, P. G. (2006). Deficit hídrico e produtividade na cultura do milho. Pesquisa Agropecuaria Brasileira, 41(2), 243-249. doi: 10.1590/S0100-204X2006000200008

Bernardo, S., Mantovani, E. C., Silva, D. D., & Soares, A. A. (2019). Manual de irrigação. Viçosa, MG: Editora UFV.

Bertolin, N. de O., Filgueiras, R., Venancio, L. P., & Mantovani, E. C. (2017). Predição da produtividade de milho irrigado com auxílio de imagens de satélite. Revista Brasileira de Agricultura Irrigada, 11(4), 1627-1638. doi: 10.7127/rbai.v11n400567

Borges, I. D., Von Pinho, R. G., & Pereira, J. L. D. A. R. (2009). Acúmulo de micronutrientes em híbridos de milho em diferentes estádios de desenvolvimento. Ciência e Agrotecnologia, 33(4), 1018-1025. doi: 10.1590/S1413-70542009000400011

Campos, I., González-Gómez, L., Villodre, J., González-Piqueras, J., Suyker, A. E., & Calera, A. (2018). Remote sensing-based crop biomass with water or light-driven crop growth models in wheat commercial fields. Field Crops Research, 216, 175-188. doi: 10.1016/j.fcr.2017.11.025

Castro, A. I., Six, J., Plant, R. E., & Peña, J. M. (2018). Mapping crop calendar events and phenology-related metrics at the parcel level by object-based image analysis (OBIA) of MODIS-NDVI time-series: a case study in central California. Remote Sensing, 10(11), 973. doi: 10.3390/rs10111745

Cross, H. Z., & Zuber, M. S. (1972). Prediction of flowering dates in maize based on different methods of estimating thermal units 1. Agronomy Journal, 64(3), 351-355. doi: 10.2134/agronj1972. 00021962006400030029x

Cruz, C. D. (2001). Programa GENES - versão windows. Aplicativo computacional em genética e estatística. Viçosa, MG: Editora UFV.

Cutforth, H. W., & Shaykewich, C. F. (1990). A temperature response function for corn development. Agricultural and Forest Meteorology, 50(3), 159-171. doi: 10.1016/0168-1923(90)90051-7

Ding, Y., Zhao, K., Zheng, X., & Jiang, T. (2014). Temporal dynamics of spatial heterogeneity over cropland quantified by time-series NDVI, near infrared and red reflectance of Landsat 8 OLI imagery. International Journal of Applied Earth Observation and Geoinformation, 30(1), 139-145. doi: 10.1016/j.jag.2014.01.009

European Space Agency (2015). SENTINEL-2 User Handbook. European Space Agency. Retrieved from

Fancelli, A. L., & Dourado, D., Neto. (2000). Produção de milho (2a ed.). Guaiba, RS: Agropecuária.

Fontana, D. C., Pinto, D. G., Junges, A. H., & Bremm, C. (2015). Using temporal NDVI/MODIS profiles for inferences on the crop soybean calendar. Bragantia, 74(3), 350-358. doi: 10.1590/1678-4499.0439

Forsthofer, E. L., Silva, P. R. F. D., Argenta, G., Strieder, M. L., Suhre, E., & Rambo, L. (2004). Desenvolvimento fenológico e agronômico de três híbridos de milho em três épocas de semeadura. Ciência Rural, 34(5), 1341-1348. doi: 10.1590/S0103-84782004000500004

Gadioli, J. L., Dourado, D., Neto, García, A. G., & Basanta, M. D. V. (2000). Temperatura do ar, rendimento de grãos de milho e caracterização fenológica associada à soma calórica. Scientia Agricola, 57(3), 377-383. doi: 10.1590/S0103-90162000000300001

Gitelson, A. A., Peng, Y., & Huemmrich, K. F. (2014). Relationship between fraction of radiation absorbed by photosynthesizing maize and soybean canopies and NDVI from remotely sensed data taken at close range and from MODIS 250m resolution data. Remote Sensing of Environment, 147, 108-120. doi: 10.1016/j.rse.2014.02.014

Guindin-Garcia, N., Gitelson, A. A., Arkebauer, T. J., Shanahan, J., & Weiss, A. (2012). An evaluation of MODIS 8- and 16-day composite products for monitoring maize green leaf area index. Agricultural and Forest Meteorology, 161, 15-25. doi: 10.1016/j.agrformet.2012.03.012

Hanway, J. J. (1966). How a corn plant develops. How a corn plant develops. (Special Report, 38). Ames: Iowa State University of Science and Technology Cooperative Extension Service.

Hatfield, J. L., & Prueger, J. H. (2010). Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices. Remote Sensing, 2(2), 562-578. doi: 10.3390/rs2020562

Huang, S., Miao, Y., Zhao, G., Yuan, F., Ma, X., Tan, C., & Bareth, G. (2015). Satellite remote sensing-based in-season diagnosis of rice nitrogen status in Northeast China. Remote Sensing, 7(8), 10646-10667. doi: 10.3390/rs70810646

Huete, A., Justice, C., & Leeuwen, W. (1999). MODIS vegetation index (MOD 13) - Algorithm Theoretical Basis Document Version 3. Washington: National Aeronautics and Space Administration, 129 p.

Instituto Nacional de Meteorologia (2018). Normais Climatológicas do Brasil 1961-1990. Recuperado de

Jayawardhana, W. G. N. N., & Chathurange, V. M. I. (2016). Extraction of agricultural phenological parameters of Sri Lanka using MODIS, NDVI time series data. Procedia Food Science, 6, 235-241. doi: 10.1016/j.profoo.2016.02.027

Jensen, J. R. (2011). Sensoriamento remoto do ambiente: uma perspectiva em recursos terrestres. São José dos Campos, SP: Parêntese.

Ji, L., & Peters, A. J. (2007). Performance evaluation of spectral vegetation indices using a statistical sensitivity function. Remote Sensing of Environment, 106(1), 59-65. doi: 10.1016/j.rse.2006.07.010

Justice, C. O., Townshend, J. R. G., Vermote, E. F., Masuoka, E., Wolfe, R. E., Saleous, N., & Morisette, J. T. (2002). An overview of MODIS Land data processing and product status. Remote Sensing of Environment, 83(1-2), 3-15. doi: 10.1016/S0034-4257(02)00084-6

Kalfas, J. L., Xiao, X., Vanegas, D. X., Verma, S. B., & Suyker, A. E. (2011). Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO2 flux tower data. Agricultural and Forest Meteorology, 151(12), 1514-1528. doi: 10.1016/j.agrformet.2011.06.007

Khanal, S., Fulton, J., & Shearer, S. (2017). An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture, 139, 22-32. doi: 10.1016/j.compag.2017.05.001

Kozlowski, L. A. (2002). Período crítico de interferência das plantas daninhas na cultura do milho baseado na fenologia da cultura. Planta Daninha, 20(3), 365-372. doi: 10.1590/S0100-83582002000300006

Kozlowski, L. A., Koehler, H. S., & Pitelli, R. A. (2009). Épocas e extensões do período de convivência das plantas daninhas interferindo na produtividade da cultura do milho (Zea mays). Planta Daninha, 27(3), 481-490. doi: 10.1590/S0100-83582009000300008

Kross, A., McNairn, H., Lapen, D., Sunohara, M., & Champagne, C. (2015). Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. International Journal of Applied Earth Observation and Geoinformation, 34(1), 235-248. doi: 10.1016/j.jag.2014.08.002

Kuplich, T. M., Moreira, A., & Fontana, D. C. (2013). Série temporal de índice de vegetação sobre diferentes tipologias vegetais no Rio Grande do Sul. Revista Brasileira de Engenharia Agrícola e Ambiental, 17(10), 1116-1123. doi: 10.1590/s1415-43662013001000014

Li, F., Miao, Y., Feng, G., Yuan, F., Yue, S., Gao, X., & Chen, X. (2014). Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops Research, 157, 111-123. doi: 10.1016/j.fcr.2013.12.018

Maresma, Á., Ariza, M., Martínez, E., Lloveras, J., & Martínez-Casasnovas, J. A. (2016). Analysis of vegetation indices to determine nitrogen application and yield prediction in maize (Zea Mays L.) from a standard uav service. Remote Sensing, 8(12), 1-15. doi: 10.3390/rs8120973

Martins, K. V., Dourado- D., Neto, Reichardt, K., Favarin, J. L., Sartori, F. F., Felisberto, G., & Mello, S. C. (2017). Maize dry matter production and macronutrient extraction model as a new approach for fertilizer rate estimation. Anais da Academia Brasileira de Ciencias, 89(1), 705-716. doi: 10.1590/0001-3765201720160525

Matsushita, B., Yang, W., Chen, J., Onda, Y., & Qiu, G. (2007). Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: a case study in high-density cypress forest. Sensors, 7, 2636-2651. doi: 10.3390/s7112636

Pan, Z., Huang, J., Zhou, Q., Wang, L., Cheng, Y., Zhang, H., & Liu, J. (2015). Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data. International Journal of Applied Earth Observation and Geoinformation, 34(1), 188-197. doi: 10.1016/j.jag.2014.08.011

Ponzoni, F. J., Shimabukuro, Y. E., & Kuplich, T. M. (2012). Sensoriamento remoto da vegetação (2a ed.). São Paulo, SP: Oficina de Textos.

Povh, F. P., Molin, J. P., Gimenez, L. M., Pauletti, V., Molin, R., & Salvi, J. V. (2008). Comportamento do NDVI obtido por sensor ótico ativo em cereais. Pesquisa Agropecuaria Brasileira, 43(8), 1075-1083. doi: 10.1590/S0100-204X2008000800018

QGIS Development Team (2019). Geographic Information System (QGIS) software, version 3.6.1. Open source geospatial foundation project. Retrieved from

Regazzi, A. J. (1993). Teste para se verificar a identidade de modelos de regressão e a igualdade de alguns parâmetros num modelo polinomial ortogonal. Revista Ceres, 40(228), 176-195.

Ritchie, S. W., Hanway, J. J., & Benson, G. O. (1993). How a corn plant develops. Ames: Iowa State University of Science and Technology Cooperative Extension Service. (Special Report, 48).

Rizzard, M. A., Silva, L. F., & Vargas, L. (2006). Controle de plantas daninhas em milho em função de quantidades de palha de nabo forrageiro. Planta Daninha, 24(2), 263-270. doi: 10.1590/S0100-83582006000200008

Rouse, J. W., Hass, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the great plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Washington, D.C., USA.

Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., & Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154-172. doi: 10.1016/j.rse.2014.02.001

Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., & Ohno, H. (2005). A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment, 96(3-4), 366-374. doi: 10.1016/j.rse.2005.03.008

Santos, H. G., Carvalho, W., Jr., Dart, R. de O., Áglio, M. L. D., Sousa, J. S., Pares, J. G., & Oliveira, A. P. O. (2011). O novo mapa de solos do Brasil: legenda atualizada, escala 1:5.000.000 (1st ed.). Rio de Janeiro, RJ, Brasil: EMBRAPA Solos.

Soufizadeh, S., Munaro, E., McLean, G., Massignam, A., Van Oosterom, E. J., Chapman, S. C., & Hammer, G. L. (2018). Modelling the nitrogen dynamics of maize crops - Enhancing the APSIM maize model. European Journal of Agronomy, 100(2016), 118-131. doi: 10.1016/j.eja.2017.12.007

Tsimba, R., Edmeades, G. O., Millner, J. P., & Kemp, P. D. (2013). The effect of planting date on maize: Phenology, thermal time durations and growth rates in a cool temperate climate. Field Crops Research, 150, 145-155. doi: 10.1016/j.fcr.2013.05.021

United States Geological Survey (2016). Landsat 8 (L8) data users handbook. Version 2.0. Department of the Interior U.S. Geological Survey, United States Geological Survey. Retrieved from

Venancio, Cunha, Mantovani, Amaral, & Reis. (2019). Evapotranspiração de cultura: uma abordagem dos principais métodos aplicados às pesquisas científicas e na agricultura. Irriga, 24 (3), 719-746. doi: 10.15809/irriga.2019v24n4p719-746

Vermote, E. F. (2015). MODIS surface reflectance user’s guide. Collection 6. MODIS land surface reflectance science computing facility. Retrieved from UserGuide_v1.4.pdf

Wang, M., Tao, F. L., & Shi, W. J. (2014). Corn yield forecasting in northeast china using remotely sensed spectral indices and crop phenology metrics. Journal of Integrative Agriculture, 13(7), 1538-1545. doi: 10.1016/S2095-3119(14)60817-0

Wardlow, B. D., & Egbert, S. L. (2008). Large-area crop mapping using time-series MODIS 250 m NDVI data: an assessment for the U.S. Central Great Plains. Remote Sensing of Environment, 112(3), 1096-1116. doi: 10.1016/j.rse.2007.07.019

Xiao, J., & Moody, A. (2005). A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sensing of Environment, 98(2-3), 237-250. doi: 10.1016/j.rse.2005.07.011

Zanzarini, F. V., Pissarra, T. C. T., Brandao, F. J. C., & Teixeira, D. D. B. (2013). Spatial correlation of the vegetation index (NDVI) of a Landsat/ETM plus images with soil attributes. Revista Brasileira de Engenharia Agricola e Ambiental, 17(6), 608-614. doi: 10.1590/S1415-43662013000600006

Zheng, Y., Wu, B., Zhang, M., & Zeng, H. (2016). Crop phenology detection using high spatio-temporal resolution data fused from SPOT5 and MODIS products. Sensors, 16(12), 2099. doi: 10.3390/s16122099


Semina: Ciênc. Agrár.
Londrina - PR
E-ISSN 1679-0359
DOI: 10.5433/1679-0359
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