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RESEARCH ARTICLE
Contents Vol 58(4)

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: andries.potgieter@dpi.qld.gov.au

Australian Journal of Agricultural Research 58(4) 316-325 https://doi.org/10.1071/AR06279
Submitted: 25 August 2006  Accepted: 1 February 2007   Published: 12 April 2007

Abstract

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.


Acknowledgments

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.


References


ABARE (2005) Australian Bureau of Agricultural Resource and Economics farm survey for the 2003 and 2004. Canberra, Australia.

Campbell JB (2002) ‘Introduction to remote sensing.’ (Guilford Press: New York)

Department of Natural Resources and Water (2006) Queensland Land Use Mapping Program, viewed May 2005, www.nrm.qld.gov.au/science/lump.

Davis JC (2002) ‘Statistics and data analysis in geology.’ (John Wiley & Sons Inc.: New York)

Hammer GL, Hansen JW, Phillips JG, Mjelde JW, Hill H, Love A, Potgieter AB (2001) Advances in application of climate prediction in agriculture. Agricultural Systems 70, 515–553.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83, 195–213.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Huete A, Justice C, Liu H (1994) Development of vegetation and soil indices for MODIS-EOS. Remote Sensing of Environment 49, 224–234.
| Crossref |
open url image1

Huete AR, Liu HQ, Batchily K, van Leeuwen W (1997) A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment 59, 440–451.
| Crossref |
open url image1

Jakubauskas ME, Legates DR, Kastens JH (2001) Harmonic analysis of time series AVHRR NDVI data. Photogrametric Engineering & Remote Sensing 67, 461–470. open url image1

Jakubauskas ME, Legates DR, Kastens JH (2002) Crop identification using harmonic analysis of time-series AVHRR NDVI data. Computers and Electronics in Agriculture 37, 127–139.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Justice CO, Townsend JRG, Vermote EF, Masuoka E, Wolfe RE, Saleous N, Roy DP, Morisette JT (2002) An overview of MODIS Land data processing and product status. Remote Sensing of Environment 83, 3–15.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Knopke P , O’Donnell V , Shepherd A (2000) ‘Productivity growth in the Australian grains industry.’ ABARE0 642 76413 1. (ABARE: Canberra)

Meinke H, Hammer GL, van Keulen H, Rabbinge R (1998) Improving wheat simulation capabilities in Australia from a cropping systems perspective. III. The integrated wheat model (I_WHEAT). European Journal of Agronomy 8, 101–116.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Meinke H, Hammer GL, van Keulen H, Rabbinge R, Keating BA (1997) Improving wheat simulation capabilities in Australia from a cropping systems perspective. Water and nitrogen effects on spring wheat in a semi-arid environment. European Journal of Agronomy 7, 75–88.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Muchoney D, Borak J, Chi H, Friedl M, Gopal S, Hodges J, Morrow N, Strahler A (2000) Application of MODIS global supervised classification model to vegetation and land cover mapping of Central America. International Journal of Remote Sensing 21, 1115–1138.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Nelson RA, Holzworth DP, Hammer GL, Hayman PT (2002) Infusing the use of seasonal climate forecasting into crop management practice in North East Australia using discussion support software. Agricultural Systems 74, 393–414.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Nix HA (1975) The Australian climate and its effects on grain yield and quality. In ‘Australian field crops’. (Eds A Lazenby, EM Matheson) pp. 183–226. (Angus and Robertson: Sydney, NSW)

Penm J (2002) ‘Economic overview.’ Vol 9, No. 4. (ABARE: Canberra)

Potgieter AB, Hammer GL, Butler D (2002) Spatial and temporal patterns in Australian wheat yield and their relationship with ENSO. Australian Journal of Agricultural Research 53, 77–89.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Potgieter AB , Hammer GL , Doherty A (2006) Oz-Wheat: a regional-scale crop yield simulation model for Australian wheat. Queensland Department of Primary Industries & Fisheries, Information Series No. QI06033, Brisbane, Qld (ISSN 0727-6273).

Potgieter AB, Hammer GL, deVoil P (2005) A simple regional-scale model for forecasting sorghum yield across North-Eastern Australia. Agricultural and Forest Meteorology 132, 143–153.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Price JC (2003) Comparing MODIS and ETM+ data for regional and global land classification. Remote Sensing of Environment 86, 491–499.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Richards JA , Jia X (1999) ‘An introduction to remote sensing digital image analysis.’ (Springer-Verlag: Berlin, Heidelberg)

RSI (2005) Research Systems Inc. www.rsinc.com

Stephens DJ (1998) Objective criteria for estimating the severity of drought in the wheat areas of Australia. Agricultural Systems 57, 333–350.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Stephens DJ , Butler D , Hammer GL (2000) Using seasonal climate forecasts in forecasting the Australian wheat crop. In ‘Applications of seasonal climate forecasting in agriculture an natural ecosystems: The Australian experience’. (Eds GL Hammer, N Nicholls, C Mitchell) pp. 351–366. (Kluwer Academic Publishers: Dordrecht, The Netherlands)

Stone RC, Hammer GL, Marcussen T (1996) Prediction of global rainfall probabilities using phases of the Southern Oscillation Index. Nature 384, 252–255.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Verhoef W, Menenti M, Azzali S (1996) A colour composite of NOAA-AVHRR-NDVI based on time series analysis (1981–1992). International Journal of Remote Sensing 17, 231–235. open url image1

Xiao X, Boles S, Liu J, Zhuang D, Frolking S, Li C, Salas W, Moore B (2005) Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sensing of Environment 95, 480–492.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Zhan X, Sohlberg RA, Townsend JRG, DiMiceli C, Carroll ML, Eastman JC, Hansen MC, DeFries RS (2002) Detection of land cover changes using MODIS 250 m data. Remote Sensing of Environment 83, 336–350.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JCF, Gao F, Reed C, Huete A (2003) Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 84, 471–475.
| Crossref | GoogleScholarGoogle Scholar | open url image1









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.