Crop and Pasture Science Crop and Pasture Science Society
Plant sciences, sustainable farming systems and food quality

Estimating crop area using seasonal time series of Enhanced Vegetation Index from MODIS satellite imagery

A. B. Potgieter A E , A. Apan B , P. Dunn C and G. Hammer D
+ Author Affiliations
- Author Affiliations

A Emerging Technologies, Queensland Department of Primary Industries & Fisheries, Toowoomba, Qld 4350, Australia.

B Australian Centre for Sustainable Catchments & Faculty of Engineering and Surveying University of Southern Queensland, Toowoomba, Qld 4350, Australia.

C Australian Centre for Sustainable Catchments & Faculty of Sciences, University of Southern Queensland, Toowoomba, Qld 4350, Australia.

D School of Land and Food Sciences, The University of Queensland, Brisbane, Qld 4072, Australia.

E Corresponding author. Email:

Australian Journal of Agricultural Research 58(4) 316-325
Submitted: 25 August 2006  Accepted: 1 February 2007   Published: 12 April 2007


Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state, and national scales. There is now an increasing drive from industry for more accurate and cost-effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. Multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April–November) were investigated to estimate crop area for wheat, barley, chickpea, and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period; (ii) harmonic analysis of the time series (HANTS) of the EVI values; and (iii) principal component analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea, and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at pixel scale (>98% correct classification) for identifying overall winter cropping. However, discrimination among crops was less accurate. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire scale, the result contradicted the poor pixel-scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped-area estimates before harvest.

Additional keywords: multi-temporal MODIS, harmonic analysis, principal component analysis.


We thank W. Verhoef and A. van der Kamp from the National Aerospace Laboratory (NLR) in the Netherlands for supplying the HANTS software and providing guidance and advice on its use. We also thank Land and Water Australia, through their Managing Climate Variability Program, for partly funding this project.


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1Coefficient of variation for in-crop (i.e. May–October period) shire rainfall was >46% for the period 1977–2004, with rainfall station data weighted within a shire based on area represented.

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