Integration of Sentinel-1 and Landsat-8 images for crop detection: The case study of Manisa, Turkey
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Abstract
In this study, the accuracy performance of crop detection through classification was investigated in the integration of Sentinel-1 Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarized Synthetic Aperture Radar (SAR) and Landsat-8 satellite images belonging to a single date. A study area was selected from a region with dense agricultural lands within the boundaries of Manisa, Turkey. Wheat, Tomato, Corn, Corn_2, Cotton, Grapes, Clover and Olive Trees were determined as the crop types. Feature level integration was used to generate image stack and random forest (RF) machine learning algorithm was used for image classification. Classification was carried out using only Sentinel-1 SAR data, only Landsat-8 optical data and the merged data set of Sentinel-1 VV+VH and Landsat 8. Image stacking of Sentinel-1 VV+VH and Landsat 8 increased the classification accuracy. The highest overall accuracy (81.46%) was achieved through classification based on the stacked dataset of the Sentinel-1 VV+VH bands and the Landsat-8 optical bands. The study has shown that the stacked dataset of Sentinel-1 VV+VH and Landsat-8 belonging to a single date has great potential in extracting summer crop types.
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References
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