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Estimating crop area using seasonal time series of enhanced vegetation index from MODIS satellite imagery

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Potgieter, A.B., Apan, A., Dunn, P. and Hammer, G. (2007) Estimating crop area using seasonal time series of enhanced vegetation index from MODIS satellite imagery. Australian Journal of Agricultural Research, 58 (4). pp. 316-325.

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Article Link: http://dx.doi.org/10.1071/AR06279

Publisher URL: http://www.publish.csiro.au


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.

Item Type:Article
Corporate Creators:Department of Employment, Economic Development and Innovation (DEEDI), Agri-Science, Crop and Food Science, Emerging Technologies
Business groups:Crop and Food Science
Additional Information:© CSIRO.
Keywords:Harmonic analysis; multi-temporal MODIS; principal component analysis.
Subjects:Science > Statistics
Plant culture > Field crops > Grain. Cereals
Agriculture > Agriculture (General) > Special aspects of agriculture as a whole > Remote sensing
Live Archive:16 Apr 2009 04:22
Last Modified:03 Sep 2021 16:43

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