Optimization of spectral indices and long-term separability analysis for classification of cereal crops using multi-spectral RapidEye imagery |
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| Affiliation: | 1. Martin Luther University Halle-Wittenberg, Institute for Geosciences and Geography, Department for Remote Sensing and Cartography, Von-Seckendorff-Platz 4, 06120 Halle (Saale), Germany;2. Martin Luther University Halle-Wittenberg, Institute of Agriculture and Nutrition Science, Department of Farm Management, Karl-Freiherr-von-Fritsch-Str. 4, 06120 Halle (Saale), Germany;1. Department of Chemistry, Rabigh College of Science & Arts, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia;2. Department of Chemistry, Faculty of Science, Alexandria University, Ibrahimia, Alexandria 21321, Egypt;3. Universidade Lusófona de Humanidades e Tecnologias, Av. Campo Grande n° 376, 1749-024 Lisbon, Portugal;4. Universidad de Valencia, Departamento de Química Orgánica, Dr. Moliner 50, E-46100 Burjassot, Valencia, Spain;1. German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, Germany;2. University of Osnabrueck, Institute for Geoinformatics and Remote Sensing, Germany;1. Martin Luther University Halle-Wittenberg, Department for Remote Sensing and Cartography, Von-Seckendorff-Platz 4, 06120 Halle (Saale), Germany;2. Helmholtz Centre for Environmental Research, Department for Computational Landscape Ecology, Permoserstraße 15, 04318 Leipzig, Germany;3. Martin Luther University Halle-Wittenberg, Institute of Agriculture and Nutrition Science, Department of Farm Management, Karl-Freiherr-von-Fritsch-Straße 4, 06120 Halle (Saale), Germany |
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| Abstract: | Crop monitoring using remotely sensed image data provides valuable input for a large variety of applications in environmental and agricultural research. However, method development for discrimination between spectrally highly similar crop species remains a challenge in remote sensing. Calculation of vegetation indices is a frequently applied option to amplify the most distinctive parts of a spectrum. Since no vegetation index exist, that is universally best-performing, a method is presented that finds an index that is optimized for the classification of a specific satellite data set to separate two cereal crop types. The η2 (eta-squared) measure of association – presented as novel spectral separability indicator – was used for the evaluation of the numerous tested indices. The approach is first applied on a RapidEye satellite image for the separation of winter wheat and winter barley in a Central German test site. The determined optimized index allows a more accurate classification (97%) than several well-established vegetation indices like NDVI and EVI (<87%). Furthermore, the approach was applied on a RapidEye multi-spectral image time series covering the years 2010–2014. The optimized index for the spectral separation of winter barley and winter wheat for each acquisition date was calculated and its ability to distinct the two classes was assessed. The results indicate that the calculated optimized indices perform better than the standard indices for most seasonal parts of the time series. The red edge spectral region proved to be of high significance for crop classification. Additionally, a time frame of best spectral separability of wheat and barley could be detected in early to mid-summer. |
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| Keywords: | Vegetation index Spectral separability Crop classification Time series RapidEye |
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