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

Yıl 2023, Cilt: 29 Sayı: 2, 677 - 689, 31.03.2023

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

https://doi.org/10.15832/ankutbd.1174645

Cited By: 1

Öz

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.

Anahtar Kelimeler

Crop-type classification, Crop evapotranspiration, Sentinel-2, Supervised classification, Remote sensing

Destekleyen Kurum

Turkish National Geodesy and Geophysics Union (TUJJB)

Proje Numarası

TUJJB-TUMEHAP-2020-01

Teşekkür

The authors wish to thank the Turkish National Geodesy and Geophysics Union (TUJJB) for the financial support of this work (Project Number: TUJJB-TUMEHAP-2020-01)

Kaynakça

• Akpolat A (2011). Mikrometeorolojik ve lizimetre Yöntemleriyle Belirlenen Buğday Bitki Su Tüketimlerinin Karşılaştırılması. Yüksek Lisans Tezi. Çukurova Üniversitesi Fen Bilimleri Enstitüsü Tarımsal Yapılar ve Sulama Anabilim Dalı, Türkiye. (In Turkish)
• Aksu H & Arikan A (2017). Satellite-based estimation of actual evapotranspiration in the Buyuk Menders Bain, Turkey. Hydrology Resarch 48(2):559-570 hhtps://doi.org/10.2166/nh.2016.226.
• Aksu H, Cavus Y, Aksoy H, Akgul M.A, Turker S& Eris E (2022). Spatiotemporal analysis of drought by CHIRPS precipitation estimates. Theor Appl Climatol 148, 517–529 https://doi.org/10.1007/s00704-022-03960-6
• Allen R. G, Pereira L. S, Raes D& Smith M (1998). Crop evapotranspiration - guidelines for computing crop water requirements. FAO irrigation and drainage paper 56, Fao, Rome, 300(9): D05109.
• Belgiu M & Csillik O (2018.( Remote Sensing of Environment Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote Sensing of Environment, 204, pp: 509-523.
• Boken V.K, Hoogenboom G, Hook J.E, Thomas D.L, Guerra L.C & Harrison K.A (2004). Agricultural water use estimation using geospatial modeling and a geographic information system. Agricultural Water Management, 67 (3): 185-199.
• Cetin M (2020). Agricultural Water Use. In: N. B. Harmancioglu, D. Altinbilek (eds.), Water Resources of Turkey, World Water Resources, Vol. 2, Springer Nature Switzerland, pp. 257-302. https://doi.org/10.1007/978-3-030-11729-0_9
• Cetin M, Kaman H, Kirda C & Sesveren S )2020(. Analysis of irrigation performance in water resources planning and management: A case study. Fresenius Environmental Bulletin (FEB), vol 29)05(: 3409-3414.
• Defourny P, Bontemps S, Bellemans N, Cara C, Dedieu G, Guzzonato E, Hagolle O, Inglada J, Nicola L & Rabaute T )2019(. Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world. Remote Sens. Environ, 221: 551-568.
• DSİ Devlet Su İşleri Genel Müdürlüğü (2017). Türkiye'de Sulanan Bitkilerin Bitki Su Tüketimleri, Ankara. (In Turkish)
• Golpınar M.S (2017). Yüzey akişlarin swat modeli ile belirlenmesi: akarsu sulama birliği sahasi örneği. Yüksek Lisans Tezi. Çukurova Üniversitesi Fen Bilimleri Enstitüsü Tarımsal Yapılar ve Sulama Anabilim Dalı, Türkiye. (In Turkish)
• Jayanth J, Aravind R & Amulya C. M )2021(. Classification of crops and crop rotation using remote sensing and GIS-Based approach: A case study of Doddakawalande Hobli, Nanjangudu Taluk. Journal of the Indian Society of Remote Sensing, 50:197-215 https://doi.org/10.1007/s12524-020-01296-0.
• Jiang Y, Lu Z, Li S, Lei Y, Chu Q, Yin X & Chen F )2020(. Large-scale and high-resolution crop mapping in China using sentinel-2 satellite imagery. Agriculture, 10 (10):433. https://doi.org/10.3390/agriculture10100433.
• Koc DL & Kanber R (2020). Bowen Oranı Enerji Dengesi Yöntemiyle Buğday Su Tüketiminin Belirlenmesi. KSÜ Tarım ve Doğa Derg 23(2): 544-553. DOI: 10.18016/ksutarimdoga.vi.597980
• Kuzay M, Tuna M & Tombul M (2022). Determining the relationship of evapotranspiration with precipitation and temperature over Turkey. Tarım Bilimleri Dergisi, (X), 1–18. https://doi.org/10.15832/ankutbd.952845
• Mahlayeye M, Darvishzadeh R & Nelson A (2022). Cropping Patterns of Annual Crops: A Remote Sensing Review. Remote Sens. 2022, 14, 2404. https://doi.org/10.3390/rs14102404
• Nur A (2019). Çukurova Koşullarında Lizimetere Yöntemiyle Mısır Bitki Su Tüketiminin ve bitki Katsayılarının Belilenmesi. Yüksek Lisans Tezi. Çukurova Üniversitesi Fen Bilimleri Enstitüsü Tarımsal Yapılar ve Sulama Anabilim Dalı, Türkiye. (In Turkish)
• Oguz H (2015) .A Software Tool for Retrieving Land Surface Temperature from ASTER Imagery. Tarim Bilimleri Dergisi, 471-482. https://doi.org/10.1501/Tarimbil_0000001350
• Ozcan H, Cetin M & Diker K )2003(. Monitoring and Assessment of Land Use Status by Gis. Environmental Monitoring and Assessment 87: 33-45. Rouse J. W, Haas R. H, Deering D. W & Sehell J. A )1974(. Monitoring the vernal advancement and retrogradation (green wave effct) of natural vegetation. Final Rep. RSC 1978-4, Remote Sensing Center.
• Santos L, Da C, Cruz G. H. T, Capuchinho F. F, José J. V & Dos Reis E. F )2019(. Assessment of empirical methods for estimation of reference evapotranspiration in the Brazilian Savannah. Australian Journal of Crop Science, 13(7):1094-1104. https://doi.org/10.21475/ajcs.19.13.07., pp. 1569.
• Selek B , Yazici D.D , Aksu H & Özdemir A.D (2016). Seyhan Dam, Turkey, and climate change adaptation strategies, In Increasing Resilience to Climate Variability and Change, Springer, Singapore, pp. 205-231.
• Sonobe R, Yamaya Y, Tani H, Wang X, Kobayashi N, Mochizuki & Kan-ichiro )2017(. Assessing the suitability of data from Sentinel-1A and 2A for crop classification. GIScience & Remote Sensing , 54:6, 918-938.
• Unlu M, Kanber R & Kapur B (2010). Comparison of soybean evapotranspirations measured by weighing lysimeter and Bowen ratio-energy balance methods. African Journal of Biotechnology , Vol. 9(30), pp. 4700-4713.
• Unlu M, Kanber R, Koc D. L, Ozekici B & Kekec U (2014). Irrigation scheduling of grapefruit trees in a Mediterranean environment throughout evaluation of plant water status and evapotranspiration. Turkish Journal of Agriculture and Forestry, pp: 908–915. https://doi.org/10.3906/tar-1403-58
• Unlu M, Kanber R, Koc D. L, Tekin S & Kapur B (2011). Effects of deficit irrigation on the yield and yield components of drip irrigated cotton in a mediterranean environment. Agricultural Water Management, 98(4), 597–605. https://doi.org/10.1016/j.agwat.2010.10.020
• Whyte A, Ferentinos K. P & Petropoulos G. P )2018( A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms. Environmental Modelling & Software, 104: pp. 40-54.
• Yildirim T & Asik S (2018). Index-based assessment of agricultural drought using remote sensing in the semi-arid region of western Turkey. Tarim Bilimleri Dergisi, 24(4), 510–516. https://doi.org/10.15832/ankutbd.337136
• Yildiz A M (2019). Çukurova Koşullarında Lizimetre, Eddy Kovaryans Ve Su Bütçesi Yöntemleri İle Belirlenen Buğday Bitki Su Tüketimlerinin Karşılaştırılması. Yüksek Lisans Tezi. Çukurova Üniversitesi Fen Bilimleri Enstitüsü Tarımsal Yapılar ve Sulama Anabilim Dalı, Türkiye. (In Turkish)
• Zheng B, Myint S.W, Thenkabail P.S & Aggarwal R.M )2015(.A support vector machine to identify irrigated crop types using time-series Landsat NDVI data. International Journal of Applied Earth Observation and Geoinformation Int. J. Appl. Earth Obs Geoinf, 34: pp:103-112.