Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil


Autoria(s): Goncalves, Renata R. V.; Zullo, Jurandir, Jr.; Romani, Luciana A. S.; Nascimento, Cristina R.; Traina, Agma Juci Machado
Contribuinte(s)

UNIVERSIDADE DE SÃO PAULO

Data(s)

05/11/2013

05/11/2013

2012

Resumo

Brazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international market places. 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.

FAPESP

CNPq

CAPES

Embrapa

Microsoft Research

Identificador

INTERNATIONAL JOURNAL OF REMOTE SENSING, ABINGDON, v. 33, n. 15, Special Issue, pp. 4653-4672, SEP, 2012

0143-1161

http://www.producao.usp.br/handle/BDPI/40985

10.1080/01431161.2011.638334

http://dx.doi.org/10.1080/01431161.2011.638334

Idioma(s)

eng

Publicador

TAYLOR & FRANCIS LTD

ABINGDON

Relação

INTERNATIONAL JOURNAL OF REMOTE SENSING

Direitos

restrictedAccess

Copyright TAYLOR & FRANCIS LTD

Palavras-Chave #AVHRR IMAGE NAVIGATION #LEAF-AREA INDEX #VEGETATION INDEXES #REMOTE SENSING #IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
Tipo

article

original article

publishedVersion