Cropping Pattern Classification Using Artificial Neural Networks and Evapotranspiration Estimation in the Eastern Mediterranean Region of Turkey

Authors : Omar ALSENJAR, Mahmut ÇETİN, Hakan AKSU, Mehmet Ali AKGÜL, Muhammet Said GOLPİNAR

Pages : 677-689

Doi:10.15832/ankutbd.1174645

View : 15 | Download : 6

Publication Date : 2023-03-31

Article Type : Research Paper

Abstract :Determining cropping patterns is crucial for quantifying irrigation water requirements at a catchment scale. For this reason, new and innovative technologies such as remote sensing insert ignore into journalissuearticles values(RS); and artificial neural networks insert ignore into journalissuearticles values(ANNs); are robust tools for generating the spatiotemporal variation of crops. In line with this, this study aims to classify each crop type using the ANN algorithm and calculate crop evapotranspiration insert ignore into journalissuearticles values(ETc);. This study was conducted in the Akarsu Irrigation District insert ignore into journalissuearticles values(9495 ha); in the Lower Seyhan Plain in southeastern Turkey in the 2021 hydrological year. Crop types were classified using the ANN algorithm in the Environment for Visualizing Images insert ignore into journalissuearticles values(ENVI); program based on combined data from Sentinel-2 images with a 10-m resolution and ground truth data collected during the winter and summer seasons. The image analysis results demonstrated that bare soil and citrus made up 3666 ha and 3742 ha respectively in the winter season, while first crop corn insert ignore into journalissuearticles values(1586 ha); and citrus insert ignore into journalissuearticles values(4121 ha); were preponderant in summer. The confusion matrix of the ANN algorithm showed high agreement insert ignore into journalissuearticles values(wheat 89.76%, onion 91.67%; citrus 97.67% in winter and 98.98% in summer; 100% for lettuce, potato, sesame-2, palm, and watermelon); and medium agreement insert ignore into journalissuearticles values(fruit 58.33% in winter, 42.86% in summer); with ground truth data in growing seasons. Furthermore, the agreement was more than 80% for the first and second crops insert ignore into journalissuearticles values(cotton, soybean, peanut, and corn); in the summer season. Annual reference evapotranspiration and ETc were around 1308 mm and 890 mm, respectively. The ETc values for wheat, citrus, first-crop corn, and second-crop soybean were found to be consistent with previous studies of direct evapotranspiration methods conducted in the Cukurova region. Overall, RS and ANNs can be used to classify crop types accurately in the growing season. This study builds upon and expands the application of RS and ANNs in large-scale irrigation schemes.
Keywords : Crop type classification, Crop evapotranspiration, Sentinel 2, Supervised classification, Remote sensing