Investigation of the Effect of Spectral Bands and Vegetation Index Selection on Agricultural Crop Type Classification (Especially for Double Crops)
Authors: Şimşek Fatih Fehmi 
Published: February 2024

Satellite imagery and remote sensing technology allow the identification, observation and assessment of dynamic agricultural areas. Image classification is one of the most widely used methods to determine the pattern of agricultural crops. The accuracy of the agricultural crops to be classified depends on many parameters such as the classification method used, satellite image resolution, number of images used, bands, indices and training data. In this study, a classification study was carried out using multi-temporal Sentinel-2 imagery and datasets generated from different vegetation and spectral indices, and the effects on the classification result were investigated. As the study area has very fertile soils, suitable climate and temperature conditions and irrigated land, it is possible to grow more than one crop on the same plot during a production season. Wheat_maize (winter_wheat + summer_maize), wheat_cotton (winter_wheat + summer_cotton), lentil_cotton (winter_lentil + summer_cotton), lentil_maize (winter_lentil + summer_maize) are the crops included in the classification study, except for single crops; maize, cotton, wheat and lentils are also included. Time series of vegetation indices can be used to capture information on plant phenology and can be used as reference information in crop classification. Time series curves of different vegetation indices were constructed and compared for all crops, especially for double crops with the same phenological periods. In addition to the vegetation indices, the variation of the time series reflectance values of each spectral band was also observed for all crops and the effect of different indices and bands on the classification result was investigated. The study generated 16 different data sets using conventional vegetation indices, NDVI, SAVI, EVI and NDRE vegetation indices and all other bands of the Sentinel-2 satellite except the 60m bands. While single crops with different time series (maize, cotton, lentil, wheat) had an accuracy of over 90% in each dataset, double crops could not exceed 81% accuracy by mixing with each other in the DS-5 (R-G-B-NIR) dataset. In the DS-1 (NDVI time series) dataset, the overall accuracy for double crops is in the range of 84–85%. Classification with DS-2 (NDRE time series) increased the overall accuracy for double crops to 90%. When comparing the time series reflectance values of each spectral band for all crop types, except the crop indices, it was observed that the B6 (Red Edge-2) and B11 (SWIR-1) bands were separated from the other bands and increased the classification result by 2% when included in the dataset. Especially in the classification studies carried out on products with close phenological periods, the Red Edge band (especially Red Edge-2) and the indices (NDRE) generated from these bands will improve the classification result by preventing confusion between classes, and the B11 (SWIR-1) band also has a positive effect on classification. This study has fully demonstrated the application potential of red edge bands and the indices constructed from them. It also promotes the use of red edge band optical satellite data in agricultural remote sensing.