Determining crop acreage estimates for specific winter crops using shape attributes from sequential MODIS imagery |
| |
| Affiliation: | 1. Queensland Agricultural Alliance for Food Innovation, The University of Queensland, Toowoomba 4350, Australia;2. Productivity, Water and Social Sciences, ABARES, GPO Box 1563, Canberra 2601, Australia;3. School of Environmental Sciences, University of Technology, Sydney, PO Box 123, Sydney 2007, Australia;1. Department of Food and Human Nutritional Sciences, 209 Human Ecology Building, University of Manitoba, Winnipeg R3T 2N2, Canada;2. Agriculture and Agri-Food Canada, Richardson Centre for Functional Foods and Nutraceuticals, 196 Innovation Drive, University of Manitoba, Winnipeg R3T 6C5, Canada;1. Agriculture and Agri-Food Canada, Richardson Centre for Functional Foods and Nutraceuticals, 196 Innovation Drive, University of Manitoba, Winnipeg, R3T 6C5, Canada;2. Department of Food and Human Nutritional Sciences, 209 Human Ecology Building, University of Manitoba, Winnipeg, R3T 2N2, Canada;3. Agriculture and Agri-Food Canada, Swift Current Research and Development Centre, 1 Airport Road, Swift Current, S9H 3X2, Canada;4. Canadian International Grains Institute, 303 Main Street, Winnipeg, R3C 3G7, Canada;1. Water Research Laboratory, School of Civil & Environmental Engineering, 110 King Street, Manly Vale, UNSW Australia, NSW 2093, Australia;2. UNSW Australia, Kensington, NSW 2052, Australia |
| |
| Abstract: | There are increasing societal and plant industry demands for more accurate, objective and near real-time 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. |
| |
| Keywords: | Gaussian curve Crop growth profile Shape attributes Crop area estimates Food security |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
