Determination of agricultural land use: incidence of atmospheric corrections and the implementation in multi-sensor and multi-temporal images


  • E. Willington FCA - Universidad Nacional de Córdoba
  • J. P. Clemente FCA - Universidad Nacional de Córdoba
  • M. Bocco FCA - Universidad Nacional de Córdoba



Argentina, Landsat 8, maximum likelihood, neural networks, SPOT 5


Soil coverage and its modifications are critical variables in human-environmental sciences. Changes in land use are causes and consequences of climate change. This situation makes that detailed and updated information is needed for many applications. Remote sensing provides data of large areas periodically, so it becomes a useful input to soil classification. The objectives of this work were to determinate algorithm and images combination that produces the best results to classify agricultural land and, simultaneously, evaluate the need of making atmospheric corrections over them, when classifying multi-temporal/multi-sensor series. The two supervised classification algorithms used were neural networks and maximum likelihood. In the study area, agriculture is the main land use, predominantly summer crops, and the area sown with soybean, corn and sorghum account for over 90% of the total. Time series of Landsat 8 and SPOT 5 images were used, and 164 plots were registered to train and validate the models as ground truth. Maximum likelihood and neural networks models produce very good results when multi-temporal/multi-sensor series are used, with global accuracy between 79.17% to 90.14% and Kappa index between 60% to 82%. The radiometric correction at surface level did not improve the results when the reflectance was corrected at the top of the atmosphere. The time series that use images taken in more advanced phenological stages of crops produce better coverage classifications than time series that use images from early stages.


Download data is not yet available.


Bargiel, D., Herrmann, S. 2011. Multi-temporal land-cover classification of agricultural areas in two European regions with high resolution spotlight TerraSAR-X data. Remote Sensing, 3(5), 859-877.

Bocco, M., Ovando, G., Sayago, S., Willington, E., Heredia, S. 2012. Estimating soybean ground cover from satellite images using neural-networks models. International Journal of Remote Sensing, 33(6), 1717- 728. 1.600347

Bocco, M., Ovando, G., Sayago, S., Willington, E. 2013. Simple models to estimate soybean and corn percent ground cover with vegetation indices from modis. Revista de Teledetección 39, 83-91. Available at (accessed December 2015).

Bocco, M., Sayago, S., Willington, E. 2014. Neural network and crop residue index multiband models for estimating crop residue cover from Landsat TM and ETM+ images. International Journal of Remote Sensing. 35(10), 3651-3663. 0/01431161.2014.915436

Cetin, M., Kavzoglu, T., Musaoglu, N. 2004. Classification of multi-spectral, multi-temporal and multi-sensor images using principal components analysis and artificial neural networks: Beykoz case. International Society for Photogrammetry and Remote Sensing. Available at (accessed July 2015).

Chavez, P.S. Jr. 1989. Radiometric calibration of Landsat Thematic Mapper multispectral images. Photogrammetric Engineering and Remote Sensing 55, 1285-1294.

Congalton, R.G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote sensing of Environment, 37(1), 35-46.

Foody, G.M. 2010. Assessing the accuracy of land cover change with imperfect ground reference data. Remote Sensing of Environment, 114(10), 2271-2285. http://

Guerschman, J.P., Paruelo, J.M., Di Bella, C., Giallorenzi, M.C., Pacin, F. 2003. Land cover classification in the Argentine Pampas using multi-temporal Landsat TM data. International Journal of Remote Sensing, 24(17), 3381-3402. http://dx.doi. org/10.1080/0143116021000021288

Hilera González, J.R., Martínez Hernando, V.J. 2000. Redes neuronales artificiales: fundamentos, modelos y aplicaciones. Madrid, España: Alfaomega Ra-Ma.

Ikiel C., Ustaoglu, B., Dutucu, A.A., Kilic, D.E. 2013. Remote sensing and GIS-based integrated analysis of land cover change in Duzce plain and its surroundings (north western Turkey). Environmental monitoring and assessment, 185(2), 1699-1709.

http://dx.doi. org/10.1007/s10661-012-2661-6

Marini, M.F., Vergara, M.F., Krüger, H. 2007. Determinación del uso de la tierra en el partido de Guamini (Argentina) mediante un estudio multitemporal con imagenes Landsat. Revista de Teledetección, 27, 80- 88. Available at http://www. (accessed December 2015)

Meliadis, I., Miltiadis, M. 2011. Multi-temporal Landsat image classification and change analysis of land cover/use in the Prefecture of Thessaloiniki, Greece. Proceedings of the International Academy of Ecology and Environmental Sciences, 1(1), 15-25.

Available at articles/2011-1(1)/Multi-temporal-landsat-image.pdf (accessed July 2015).

Ministerio de Agricultura, Ganadería y Pesca de la Nación. 2014. Sistema Integrado de Información Agropecuaria. Buenos Aires, Argentina. Available at estima2.php (accessed March 2015).

Monserud, R.A., Leemans, R. 1992. Comparing Global Vegetation Maps with the Kappa Statistic. Ecological modelling, 62(4), 275-293. http://dx.doi. org/10.1016/0304-3800(92)90003-W

Moreno-Ruiz, J., Arbelo, M., García-Lázaro, J., Riaño- Arribas, D. 2014. Desarrollo de una metodología para la detección de cambios de la cubierta vegetal en series temporales de imágenes de satélite diarias. Aplicación a la detección de áreas quemadas. Revista de Teledetección, 42, 11-28. raet.2014.2280

Murthy, C.S., Raju, P.V., Badrinath, K.V.S. 2003. Classification of wheat crop with multi-temporal images: performance of maximum likelihood and artificial neural networks. International Journal of Remote Sensing, 24(23), 4871-4890. http://dx.doi. org/10.1080/0143116031000070490

Nolasco, M., Willington, E.A., Bocco, M. 2014. Clasificación del uso de suelo en agricultura a partir de series temporales de imágenes LANDSAT. Proceedings 43JAIIO y VI Congreso Argentino de AgroInformática 1: 64-73. Available at http://43jaiio. (accessed July 2015).

Observatorio de Economías Regionales. 2013. La agricultura Argentina en cifras. ACOVI, Mendoza, Argentina.

Available at (accessed July 2015).

Willington, E., Nolasco, M., Bocco, M. 2013. Clasificación supervisada de suelos de uso agrícola en la zona central de Córdoba (Argentina):

comparación de distintos algoritmos sobre imágenes Landsat. Proceedings 42JAIIO y V Congreso Argentino de AgroInformática 1:207-216. Availableat

Trabajos/CAI/17.pdf (accessed July 2015).






Practical cases