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Estimation of wheat (Triticum aestivum L.) growing areas using Sentinel-2 satellite images (Case study: Shushtar county)
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Fatemeh Modiri   , Mohammad Moazami , Kamran Almasieh |
| Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran |
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Abstract: (175 Views) |
Introduction: Up-to-date information on growing area is necessary for cropping pattern, and remote sensing is a useful tool in achieve this goal (Hunt et al., 2019). Among various image classification algorithms and vegetation indices for crop type identification, machine learning methods and normalized difference vegetation index (NDVI) have had better results (Mousavi et al., 2020). In addition, attention to plant phenological stages and using multi-temporal images have led to more accurate crop distinction. The aim of this study was estimation of wheat growing areas in Shushtar county in Khuzestan province in Iran.
Materials and Methods: Time series of NDVI index was obtained from eight Sentinel-2 satellite images from November to June, which correspond to the wheat growth stages. Then, maximum likelihood and support vector machine algorithms were used to identify wheat fields. To implement the classification methods, training and test samples were needed, which were obtained by matching the NDVI diagram of the wheat crop growth stages and field surveys.
Results: Error matrix was used to evaluate the classification results. Based on this, the support vector machine with overall accuracy and Kappa coefficient of 98.5 and 96.5 percent, respectively, had higher accuracy than the maximum likelihood with overall accuracy and Kappa coefficient of 97.8 and 95 percent, respectively. In addition, considering the wheat crop phenological stages using the time series of NDVI in training samples, selection increased the classifications accuracy. Based on the support vector machine results, the total wheat growing areas was estimated to be 48233 hectares, which showed a small deviation from the available surveys.
Conclusion: Using the pattern of NDVI time series to obtain training samples showed the efficiency of plant indices in estimation of wheat growing areas according to the crop phenological stages. In addition, the results revealed that the support vector machine classification was more accurate than the maximum likelihood in the study areas, and it was considered as a base method. One reason for the appropriate performance of the support vector machine was appropriate distribution and adeqaute number of training samples based on wheat phenological stages. It can be concluded that the time series of the normalized difference vegetation index in combination with the support vector machine classification provided the possibility of quick and accurate estimation of the wheat growing areas in Shushtar county in Khuzestan in Iran.
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| Keywords: Maximum likelihood, NDVI, Support vector machine, Training samples and Wheat |
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Full-Text [PDF 781 kb]
(73 Downloads)
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Type of Study: Scientific & Research |
Subject:
Special
Received: 2024/07/9 | Accepted: 2024/10/4 | Published: 2024/11/30
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