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Article detail · 2023

Cropping pattern classification using artificial neural networks and evapotranspiration estimation in the Eastern Mediterranean region of Turkey

Journal

Ankara University Faculty of Agriculture

ISSN 1300-7580

The ISSN points to another catalog journal; the name is from the YÖKSİS record.

YÖKSİS OpenAlex Open access · diamond SJR Q3 JCR Q3 Citations 12 Percentile 69.3% FWCI 0.8
Year
2023
Type
article

Data source split

  • YÖKSİS YÖKSİS article record
  • YÖKSİS venue Ankara University Faculty of Agriculture
  • Catalog match (ISSN) Tarim Bilimleri Dergisi
  • OpenAlex OpenAlex enrichment (abstract, citations, topics)

Abstract

OpenAlex · English

Determination of cropping pattern is a very important factor in quantifying irrigation water requirements at a catchment scale. In this regard, remote sensing is a robust tool for generating spatial-temporal variation of crops. This study focuses on crop classification by using remotely sensed data coupled with ground truth data. Therefore, this study aimed at both classifying each crop type and calculating crop evapotranspiration (ETc) based on reference evapotranspiration (ETo) by using the Penman-Monteith evapotranspiration model and crop coefficient (Kc). ETo was estimated by using data from two meteorological stations located in the study area. To this end, this study was conducted in Akarsu Irrigation District (≈95 km2), a sub-catchment in the Lower Seyhan Plain (LSP), in the 2021 hydrological year. Ground truth data were collected in the two growing seasons. The ENVI program was used to classify crop types from Sentinel 2A-2B satellite images with 10-m by 10-m spatial resolution. Image analysis results demonstrated that bare soil and citrus made up more than half of the area in the winter season, while corn and citrus were preponderant in summer. In addition, the total reference evapotranspiration and crop evapotranspiration were about 1308 mm and 890 mm, respectively in the 2021 water year. ETc values for second crop soybean, first crop corn, wheat, and citrus showed agreement with previous studies of direct methods of evapotranspiration in the Cukurova region. Furthermore, research findings led us to conclude that using remotely sensed satellite data in cropping pattern determination is promising in identifying the crops grown in large agricultural lands. Moreover, remote sensing images can be used to classify accurately crops in the winter and summer seasons, and this study has expanded the application value of remotely sensed data in large-scale irrigation schemes.

Topics

Citations

OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.

12 citations

OpenAlex cited_by_count (cache / database)

Authors

  1. Omar Alsenjar
  2. MAHMUT ÇETİN
  3. HAKAN AKSU SAMSUN ÜNİVERSİTESİ
  4. MEHMET ALİ AKGÜL
  5. MUHAMMET SAİD GÖLPINAR