Wheat yield prediction in Andalucía using MERIS Terrestrial Chlorophyll Index (MTCI) time series


  • V. Egea-Cobrero Universidad de Sevilla
  • V.F. Rodríguez-Galiano Universidad de Sevilla https://orcid.org/0000-0002-5422-8305
  • E. Sánchez-Rodríguez Universidad de Sevilla
  • M.A. García-Pérez Universidad de Sevilla




remote sensing, MTCI, model, wheat, yield, time series


There is a relationship between net primary production of wheat and vegetation indices obtained from satellite imaging. Most wheat production studies use the Normalised Difference Vegetation Index (NDVI) to estimate the production and yield of wheat and other crops. On the one hand, few studies use the MERIS Terrestrial Chlorophyll Index (MTCI) to determine crop yield and production on a regional level. This is possibly due to a lack of continuity of MERIS. On the other hand, the emergence of Sentinel 2 open new possibilities for the research and application of MTCI. This study has built two empirical models to estimate wheat production and yield in Andalusia. To this end, the study used the complete times series (weekly images from 2006–2011) of the MTCI vegetation index from the Medium Resolution Imaging Spectrometer (MERIS) sensor associated with the Andalusian yearbook for agricultural and fishing statistics (AEAP"”Anuario de estadísticas agrarias y pesqueras de Andalucía). In order to build these models, the optimal development period for the plant needed to be identified, as did the time-based aggregation of MTCI values using said optimal period as a reference, and relation with the index, with direct observations of production and yield through spatial aggregation using coverage from the Geographic Information System for Agricultural Parcels (SIGPAC"”Sistema de información geográfica de parcelas agrícolas) and requests for common agricultural policy (CAP) assistance. The obtained results indicate a significant association between the MTCI index and the production and yield data collected by AEAP at the 95% confidence level (R2 =0.81 and R2 =0.57, respectively).


Download data is not yet available.

Author Biographies

V. Egea-Cobrero, Universidad de Sevilla

Geografía Física y Análisis Geográfico Regional

V.F. Rodríguez-Galiano, Universidad de Sevilla

Geografía Física y Análisis Geográfico Regional

E. Sánchez-Rodríguez, Universidad de Sevilla

Geografía Física y Análisis Geográfico Regional

M.A. García-Pérez, Universidad de Sevilla

Geografía Física y Análisis Geográfico Regional


Ahmed, B.M., Tanakamaru, H., Tada, A. 2010. Application of remote sensing for estimating crop water requirements, yield and water productivity of wheat in the Gezira Scheme. International Journal of Remote Sensing, 31(16), 4281-4294. https://doi.org/10.1080/01431160903246733

Arévalo-Barroso, A. 1992. Atlas Nacional de España. Sección II. Grupo 9. Recuperado el 5 de noviembre de 2016, a partir de http://www.ign.es/ane/ane1986-2008/

Asseng, S., Ewert, F., Martre, P., Rötter, R.P., Lobell, D.B., Cammarano, D., … Zhu, Y. 2014. Rising temperatures reduce global wheat production. Nature Climate Change, 5(2), 143-147. https://doi.org/10.1038/nclimate2470

Atanasova, N., Todorovski, L., Džeroski, S., Kompare, B. 2008. Application of automated model discovery from data and expert knowledge to a real-world domain: Lake Glumsø. Ecological Modelling, 212(1-2), 92- 98. https://doi.org/10.1016/j.ecolmodel.2007.10.032

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

Becker-Reshef, I., Justice, C., Sullivan, M., Vermote, E., Tucker, C., Anyamba, A., … Doorn, B. 2010. Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project. Remote Sensing, 2(6), 1589-1609. https://doi.org/10.3390/rs2061589

Boissard, P., Guérif, M., Pointel, J.G., Guinot, J.P. 1989. Application of SPOT data to wheat yield estimation. Advances in Space Research, 9(1), 143-154. https://doi.org/10.1016/0273-1177(89)90479-1

Boken, V.K., Shaykewich, C.F. 2002. Improving an operational wheat yield model using phenological phase-based Normalized Difference Vegetation Index. International Journal of Remote Sensing, 23(20), 4155-4168. https://doi.org/10.1080/014311602320567955

CAPDER. 2012. Seguimiento de los mercados de cereales y oleaginosas. Consejería de Agricultura, Pesca y Desarrollo Rural (CAPDER). Secretaría General Técnica. Servicio de Publicaciones y Divulgación. Junta de Andalucía.

CAPDER. 2015. Anuario de estadísticas agrarias y pesqueras de Andalucía. Recuperado a partir de

http://www.juntadeandalucia.es/organismos/agriculturapescaydesarrollorural/consejeria/sobreconsejeria/estadisticas/paginas/agrarias-anuario. html [Último acceso: junio de 2018].

Chahbi, A., Zribi, M., Lili-Chabaane, Z., Duchemin, B., Shabou, M., Mougenot, B., Boulet, G. 2014. Estimation of the dynamics and yields of cereals in a semi-arid area using remote sensing and the SAFY growth model. International Journal of Remote Sensing, 35(3), 1004-1028. https://doi.org/10.1080/ 01431161.2013.875629

Curtis, T., Halford, N. G. 2014. Food security: the challenge of increasing wheat yield and the importance of not compromising food safety. Annals of Applied Biology, 164(3), 354-372. https://doi.org/10.1111/aab.12108

Dash, J., Curran, P. 2007. Relationship between the MERIS vegetation indices and crop yield for the state of South Dakota, USA. Proc. Envisat Symposium.

Dash, J., Jeganathan, C., Atkinson, P.M. 2010. The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India. Remote Sensing of Environment, 114(7), 1388-1402. https://doi.org/10.1016/j. rse.2010.01.021

Dempewolf, J., Adusei, B., Becker-Reshef, I., Hansen, M., Potapov, P., Khan, A., Barker, B. 2014. Wheat yield forecasting for Punjab Province from vegetation index time series and historic crop statistics. Remote Sensing, 6(10), 9653-9675. https://doi.org/10.3390/rs6109653

Dente, L., Satalino, G., Mattia, F., Rinaldi, M. 2008. Assimilation of leaf area index derived from ASAR and MERIS data into CERESWheat model to map wheat yield. Remote Sensing of Environment, 112(4), 1395-1407. https://doi.org/10.1016/j.rse.2007.05.023

Duncan, J.M.A., Dash, J., Atkinson, P.M. 2015. Elucidating the impact of temperature variability and extremes on cereal croplands through remote sensing. Global change biology, 21(4), 1541-51. https://doi.org/10.1111/gcb.12660

FAOSTAT. 2013. Productos agrícolas. Recuperado 17 de agosto de 2016, a partir de http://ec.europa.eu/eurostat/statistics-explained/index.php/Agricultural_products/es#Fuente_de_los_datos_de_las_tablas_y_los_gr.C3.A1ficos_.28MS_Excel.29

Foley, J.A., Ramankutty, N., Brauman, K.A., Cassidy, E.S., Gerber, J.S., Johnston, M., … Zaks, D.P.M. 2011. Solutions for a cultivated planet. Nature, 478(7369), 337-342. https://doi.org/10.1038/ nature10452

Fontana, D.C., Potgieter, A.B., Apan, A. 2007. Assessing the relationship between shire winter crop yield and seasonal variability of the MODIS NDVI and EVI images. Applied GIS, 3(7).

Huang, J., Sedano, F., Huang, Y., Ma, H., Li, X., Liang, S., … Wu, W. 2016. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agricultural and Forest Meteorology, 216, 188-202. https://doi.org/10.1016/j.agrformet.2015.10.013

Huang, J., Tian, L., Liang, S., Ma, H., Becker-Reshef, I., Huang, Y., … Wu, W. 2015. Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agricultural and Forest Meteorology, 204, 106-121. https://doi. org/10.1016/j.agrformet.2015.02.001

Huang, Y., Zhu, Y., Li, W. L., Cao, W. X., & Tian, Y. C. 2013. Assimilating remotely sensed information with the wheatgrow model based on the ensemble square root filter for improving regional wheat yield forecasts. Plant Production Science, 16(4), 352-364. https://doi.org/10.1626/pps.16.352

ITACyL, AEMET, Consejería de Agricultura y Ganadería de la Junta de Castilla y León. 2016. Boletín de predicción de cosechas de Castilla y León. Recuperado 25 de octubre de 2016, a partir de https://cosechas.itacyl.es/es/inicio

Jégo, G., Pattey, E., Liu, J. 2012. Using Leaf Area Index, retrieved from optical imagery, in the STICS crop model for predicting yield and biomass of field crops. Field Crops Research, 131, 63-74. https://doi. org/10.1016/j.fcr.2012.02.012

Johnson, M.D., Hsieh, W.W., Cannon, A.J., Davidson, A., Bédard, F. 2016. Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods. Agricultural and Forest Meteorology, 218-219, 74-84. https://doi.org/10.1016/j.agrformet.2015.11.003

Kouadio, L., Duveiller, G., Djaby, B., El Jarroudi, M., Defourny, P., Tychon, B. 2012. Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data. International Journal of Applied Earth Observation and Geoinformation, 18(1), 111- 118. https://doi.org/10.1016/j.jag.2012.01.009

Kowalik, W., Dabrowska-Zielinska, K., Meroni, M., Raczka, T.U., de Wit, A. 2014. Yield estimation using SPOT-VEGETATION products: A case study of wheat in European countries. International Journal of Applied Earth Observation and Geoinformation, 32(1), 228-239. https://doi.org/10.1016/j.jag.2014.03.011

Kumar, M. 2016. Impact of climate change on crop yield and role of model for achieving food security. Environmental Monitoring and Assessment, 188(8), 1-14. https://doi.org/10.1007/s10661-016-5472-3

Lobell, D.B., Schlenker, W., Costa-Roberts, J. 2011. Climate trends and global crop production since 1980. Science, 333(6042), 616-20. https://doi.org/10.1126/science.1204531

MAGRAMA. 2015. Encuesta sobre Superficies y Rendimientos Cultivos (ESYRCE) de 2004 a 2015. Recuperado a partir de http://www.magrama. gob.es/es/estadistica/temas/estadisticas-agrarias/ agricultura/esyrce/resultados-de-anos-anteriores/ default.aspx

Mika, J., Kerényi, J., Rimóczi-Paál, A., Merza, Á., Szinell, C., Csiszár, I. 2002. On correlation of maize and wheat yield with NDVI: Example of Hungary (1985-1998). Advances in Space Research, 30(11), 2399-2404. https://doi.org/10.1016/S0273- 1177(02)80288-5

Padilla, F.L.M., Maas, S.J., González-Dugo, M.P., Mansilla, F., Rajan, N., Gavilán, P., Domínguez, J. 2012. Monitoring regional wheat yield in Southern Spain using the GRAMI model and satellite imagery. Field Crops Research, 130, 145-154. https://doi.org/10.1016/j.fcr.2012.02.025

Rawson, H.M., Macpherson, H.G. 2001. Trigo regado: manejo del cultivo. FAO.

Reeves, M.C., Zhao, M., Running, S.W. 2005. Usefulness and limits on MODIS GPP for estimating wheat yield. International Journal of Remote Sensing, 26(7), 1403-1421. http://doi.org/10.1080/ 01431160512331326567

Rembold, F., Atzberger, C., Savin, I., Rojas, O. 2013. Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. Remote Sensing, 5(4), 1704-1733. https://doi.org/10.3390/rs5041704

Ren, J., Chen, Z., Zhou, Q., Tang, H. 2008. Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geoinformation, 10(4), 403- 413. https://doi.org/10.1016/j.jag.2007.11.003

Salazar, L., Kogan, F., Roytman, L. 2007. Use of remote sensing data for estimation of winter wheat yield in the United States. International Journal of Remote Sensing, 28(17), 3795-3811. https://doi.org/10.1080/01431160601050395

Tadesse, T., Senay, G.B., Berhan, G., Regassa, T., Beyene, S. 2015. Evaluating a satellitebased seasonal evapotranspiration product and identifying its relationship with other satellitederived products and crop yield: A case study for Ethiopia. International Journal of Applied Earth Observation and Geoinformation, 40, 39-54. http://doi.org/10.1016/j.jag.2015.03.006

Vazifedoust, M., van Dam, J.C., Bastiaanssen, W.G. M., Feddes, R.A. 2009. Assimilation of satellite data into agrohydrological models to improve crop yield forecasts. International Journal of Remote Sensing, 30(10), 2523-2545. https://doi.org/10.1080/01431160802552769

Wall, L., Larocque, D., Léger, P.M. 2008. The early explanatory power of NDVI in crop yield modelling. International Journal of Remote Sensing, 29(8), 2211-2225. https://doi.org/ 10.1080/01431160701395252

Wu, C., Niu, Z., Tang, Q., Huang, W., Rivard, B., Feng, J. 2009. Remote estimation of gross primary production in wheat using chlorophyllrelated vegetation indices. Agricultural and Forest Meteorology, 149(6), 1015-1021. https://doi.org/10.1016/j.agrformet.2008.12.007

Zhang, S., Liu, L. 2014. The potential of the MERIS Terrestrial Chlorophyll Index for crop yield prediction. Remote Sensing Letters, 5(8), 733-742. https://doi.org/10.1080/2150704X.2014.963734





Research articles