Sugarcane yield estimates using time series analysis of spot vegetation images

Jeferson Lobato Fernandes,Rubens Augusto Camargo Lamparelli,Jansle Vieira Rocha

doi:10.1590/s0103-90162011000200002

Jeferson Lobato Fernandes, Rubens Augusto Camargo Lamparelli + Show 1 more

Open Access

https://doi.org/10.1590/s0103-90162011000200002

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Abstract

The current system used in Brazil for sugarcane (Saccharum officinarum L.) crop forecasting relies mainly on subjective information provided by sugar mill technicians and on information about demands of raw agricultural products from industry. This study evaluated the feasibility to estimate the yield at municipality level in São Paulo State, Brazil, using 10-day periods of SPOT Vegetation NDVI images and ECMWF meteorological data. Twenty municipalities and seven cropping seasons were selected between 1999 and 2006. The plant development cycle was divided into four phases, according to the sugarcane physiology, obtaining spectral and meteorological attributes for each phase. The most important attributes were selected and the average yield was classified according to a decision tree. Values obtained from the NDVI time profile from December to January next year enabled to classify yields into three classes: below average, average and above average. The results were more effective for 'average' and 'above average' classes, with 86.5 and 66.7% accuracy respectively. Monitoring sugarcane planted areas using SPOT Vegetation images allowed previous analysis and predictions on the average municipal yield trend.

Highlights

The estimation of sugarcane (Saccharum officinarum L.) yield can be conducted at local and regional scales
The yield estimation methods for sugarcane adopted by the Brazilian government are considered subjective because they are based on information gathered from direct inquiries to the production sector, such as field research using questionnaires, surveys on information about demands on agriculture raw materials, use of yield historical data and field observations on plant behavior (IBGE, 2002; CONAB, 2007)
Vegetation indices such as NDVI, originated from low spatial resolution sensors, have been showing to be adequate for the plantation monitoring aiming at yield estimation (Boken and Shayewich, 2002; Labus et al, 2002; Ferencz et al, 2004)

Summary

Introduction

The estimation of sugarcane (Saccharum officinarum L.) yield can be conducted at local (e.g. sugar mills) and regional (e.g. government) scales. It is necessary to study this potential in a regional scale, in order to gather timely information about the plant development and about the expected yield before the harvesting. Vegetation indices such as NDVI, originated from low spatial resolution sensors, have been showing to be adequate for the plantation monitoring aiming at yield estimation (Boken and Shayewich, 2002; Labus et al, 2002; Ferencz et al, 2004). Monitoring sugarcane plantations using NDVI time series derived from the Systeme Pour L’Observation de

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