02134naa a2200289 a 450000100080000000500110000800800410001910000250006024501470008526000090023252011780024165000110141965000250143065000140145565000250146965000260149465300340152065300170155465300270157165300250159865300380162370000220166170000210168370000220170470000210172677300970174719167342020-01-08 2012 bl uuuu u00u1 u #d1 aGONÇALVES, R. R. V. aAnalysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil.h[electronic resource] c2012 aBrazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international marketplaces. To maintain the high production of sugarcane, it is fundamental to improve the forecasting models of crop seasons through the use of alternative technologies, such as remote sensing. Thus, the main purpose of this article is to assess the results of two different statistical forecasting methods applied to an agroclimatic index (the water requirement satisfaction index; WRSI) and the sugarcane spectral response (normalized difference vegetation index; NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images. We also evaluated the cross-correlation between these two indexes. According to the results obtained, there are meaningful correlations between NDVI and WRSI with time lags. Additionally, the adjusted model for NDVI presented more accurate results than the forecasting models for WRSI. Finally, the analyses indicate that NDVI is more predictable due to its seasonality and the WRSI values are more variable making it difficult to forecast. aModels aStatistical analysis aSugarcane aTime series analysis aAnálise Estatística aAnálise de séries temporais aImagens NDVI aMétodos estatísticos aModelos de previsão aMonitoramento de cana-de-açúcar1 aZULLO JÚNIOR, J.1 aROMANI, L. A. S.1 aNASCIMENTO, C. R.1 aTRAINA, A. J. M. tInternational Journal of Remote Sensing, Basingstokegv. 33, n. 15, p. 4653-4672, Aug. 2012.