The Use of Spectral and Textural Features in Crop Type Mapping Using Sentinel-2a Images: A Case Study, Çukurova Region, Turkey

Abstract

Abstract. Turkey has favorable agricultural conditions (i.e. fertile soils, climate and rainfall) and can grow almost any type of crop in many regions, making it one of the leading sectors of the economy. For sustainable agriculture management, all factors affecting the agricultural products should be analyzed on a spatial-temporal basis. Therefore, nowadays space technologies such as remote sensing are important tools in providing an accurate mapping of the agricultural fields with timely monitoring and higher repetition frequency and accuracy. In this study, object based classification method was applied to 2017 Sentinel 2 Level 2A satellite image in order to map crop types in the Adana, Çukurova region in Turkey. Support Vector Machine (SVM) was used as a classifier. Texture information were incorporated to spectral wavebands of Sentinel-2 image, to increase the classification accuracy. In this context, all of the textural features of Gray-Level Co-occurrence Matrix (GLCM) were tested and Entropy, Standard deviation, and Mean textural features were found to be the most suitable among them. Multi-spectral and textural features were used as an input separately and/or in combination to evaluate the potential of texture in differentiating crop types and the accuracy of output thematic maps. As a result, with the addition of textural features, it was observed that the Overall Accuracy and Kappa coefficient increased by 7% and 8%, respectively.

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“…Object-based image analysis uses a segmentation algorithm to group similar pixels into object-based clusters, which makes it possible to assess object properties such as shapes and is helpful in preventing noise during classification. The second is the calculation of textural measures, which help the classifier recognize differences in spatial patterns 78 . For this study, nine textural features were calculated using the gray-level co-occurrence matrix 79,80 and incorporated into one band using principal component analysis 81 .…”