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 AgricultureISSN 1300-7580
The ISSN points to another catalog journal; the name is from the YÖKSİS record.
- 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)
6 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- Actual evapotranspiration estimation using METRIC model and Landsat satellite images over an irrigated field in the Eastern Mediterranean Region of Turkey 2023
- Developing a New ANN Model to Estimate Daily Actual Evapotranspiration Using Limited Climatic Data and Remote Sensing Techniques for Sustainable Water Management 2024
- Developing a New ANN Model to Estimate Daily Actual Evapotranspiration Using Limited Climatic Data and Remote Sensing Techniques for Sustainable Water Management 2024
- A Novel Method to Forecast Nitrate Concentration Levels in Irrigation Areas for Sustainable Agriculture 2025
- Developing a New ANN Model to Estimate Daily Actual Evapotranspiration Using Limited Climatic Data and Remote Sensing Techniques 2024
- Evaluation of GPM-IMERG V07 Precipitation Data Against In-Situ Measurements in a Semi-Arid Region of Türkiye 2025