Determining crop acreage estimates for specific winter crops using shape attributes from sequential MODIS imagery

Potgieter, A. B., Lawson, K. and Huete, A. R. (2013) Determining crop acreage estimates for specific winter crops using shape attributes from sequential MODIS imagery. International Journal of Applied Earth Observation and Geoinformation, 23 1: 254-263. doi:10.1016/j.jag.2012.09.009

Author Potgieter, A. B.
Lawson, K.
Huete, A. R.
Title Determining crop acreage estimates for specific winter crops using shape attributes from sequential MODIS imagery
Journal name International Journal of Applied Earth Observation and Geoinformation   Check publisher's open access policy
ISSN 1569-8432
Publication date 2013-01-01
Year available 2012
Sub-type Article (original research)
DOI 10.1016/j.jag.2012.09.009
Volume 23
Issue 1
Start page 254
End page 263
Total pages 10
Place of publication Amsterdam, The Netherlands
Publisher Elsevier BV
Language eng
Subject 1903 Journalism and Professional Writing
1904 Performing Arts and Creative Writing
2306 Global and Planetary Change
2308 Management, Monitoring, Policy and Law
Abstract There are increasing societal and plant industry demands for more accurate, objective and near realtime crop production information to meet both economic and food security concerns. The advent of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite platform has augmented the capability of satellite-based applications to monitor large agricultural areas at acceptable pixel scale, cost and accuracy. Fitting parametric profiles to growing season vegetation index time series reduces the volume of data and provides simple quantitative parameters that relates to crop phenology (sowing date, flowering). In this study, we modelled various Gaussian profiles to time sequential MODIS enhanced vegetation index (EVI) images over winter crops in Queensland, Australia. Three simple Gaussian models were evaluated in their effectiveness to identify and classify various winter crop types and coverage at both pixel and regional scales across Queensland's main agricultural areas. Equal to or greater than 93% classification accuracies were obtained in determining crop acreage estimates at pixel scale for each of the Gaussian modelled approaches. Significant high to moderate correlations (log-linear transformation) were also obtained for determining total winter crop (R2 = 0.93) areas as well as specific crop acreage for wheat (R2 = 0.86) and barley (R2 = 0.83). Conversely, it was much more difficult to predict chickpea acreage (R2 ≤ 0.26), mainly due to very large uncertainties in survey data. The quantitative approach utilised here further had additional benefits of characterising crop phenology in terms of length of growing season and providing regression diagnostics of how well the fitted profiles matched the EVI time series. The Gaussian curve models utilised here are novel in application and therefore will enhance the use and adoption of remote sensing technologies in targeted agricultural application. With innate simplicity and accuracies comparable to other more convoluted multi-temporal approaches it is a good candidate in determining total and specific crop acreage estimates in future national and global food security frameworks.
Keyword Crop area estimates
Crop growth profile
Food security
Gaussian curve
Shape attributes
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Alliance for Agriculture and Food Innovation
Official 2014 Collection
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Citation counts: TR Web of Science Citation Count  Cited 10 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 9 times in Scopus Article | Citations
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Created: Fri, 29 Nov 2013, 03:26:21 EST by System User on behalf of Qld Alliance for Agriculture and Food Innovation