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

Omar Alsenjar; Mahmut Çetin; Hakan Aksu; Mehmet Ali Akgül; Muhammet Said Golpinar

Makale detayları

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

Yazarlar:

Omar Alsenjar | Mahmut Çetin | Hakan Aksu | Mehmet Ali Akgül | Muhammet Said Golpinar

Yayıncı:

Ankara University

Ana Başlıklar:

Engineering

Dergi:

Journal of Agricultural Sciences Vol.29 Issue 2, pp.677-689

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001285926

pISSN:

1300-7580

Anahtar kelimeler:

Özet:

 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 RS and artificial neural networks 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 ETc. This study was conducted in the Akarsu Irrigation District 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 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 1586 ha and citrus 4121 ha were preponderant in summer. The confusion matrix of the ANN algorithm showed high agreement 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 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 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.

 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 RS and artificial neural networks 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 ETc. This study was conducted in the Akarsu Irrigation District 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 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 1586 ha and citrus 4121 ha were preponderant in summer. The confusion matrix of the ANN algorithm showed high agreement 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 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 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.

 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 RS and artificial neural networks 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 ETc. This study was conducted in the Akarsu Irrigation District 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 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 1586 ha and citrus 4121 ha were preponderant in summer. The confusion matrix of the ANN algorithm showed high agreement 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 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 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.

 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 RS and artificial neural networks 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 ETc. This study was conducted in the Akarsu Irrigation District 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 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 1586 ha and citrus 4121 ha were preponderant in summer. The confusion matrix of the ANN algorithm showed high agreement 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 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 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.

Makale detayları

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

Yazarlar:

Omar Alsenjar | Mahmut Çetin | Hakan Aksu | Mehmet Ali Akgül | Muhammet Said Golpinar

Yayıncı:

Ankara University

Ana Başlıklar:

Engineering

Dergi:

Journal of Agricultural Sciences Vol.29 Issue 2, pp.677-689

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001285926

pISSN:

1300-7580

Crop-type classification

Crop evapotranspiration

Sentinel-2

Supervised classification

Remote sensing

Sayı

Makale detayları

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

Yazarlar: Omar Alsenjar | Mahmut Çetin | Hakan Aksu | Mehmet Ali Akgül | Muhammet Said Golpinar

Yayıncı: Ankara University

Ana Başlıklar: Engineering

Dergi: Journal of Agricultural Sciences Vol.29 Issue 2, pp.677-689

Belge Tipi: Makale

Yayın Tarihi: 3/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001285926

pISSN: 1300-7580

Crop-type classification

Crop evapotranspiration

Sentinel-2

Supervised classification

Remote sensing

Sayı

Yazarlar:

Omar Alsenjar | Mahmut Çetin | Hakan Aksu | Mehmet Ali Akgül | Muhammet Said Golpinar

Yayıncı:

Ankara University

Ana Başlıklar:

Engineering

Dergi:

Journal of Agricultural Sciences Vol.29 Issue 2, pp.677-689

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001285926

pISSN:

1300-7580