Person: GÜNDOĞDU, KEMAL SULHİ
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GÜNDOĞDU
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KEMAL SULHİ
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Publication Comparative analysis of the pysebal model and lysimeter for estimating actual evapotranspiration of soybean crop in Adana, Turkey(Selçuk Üniversitesi Yayınları, 2020-06-01) Sawadogo, Alidou; Tim, Hessels; Gündoğdu, Kemal Sulhi; Demir, Ali Osman; Ünlü, Mustafa; Zwart, Sander Jaap; Sawadogo, Alidou; GÜNDOĞDU, KEMAL SULHİ; DEMİR, ALİ OSMAN; Bursa Uludağ Üniversitesi/Ziraat Fakültesi/Biyosistem Mühendisliği Bölümü; 0000-0002-5591-4788; 0000-0002-5091-1801; JLX-2232-2023; DXY-6494-2022; ABI-4047-2020Accurate estimation of evapotranspiration (ET) is an important factor in water management, especially in irrigated agriculture. Accurate irrigation scheduling requires accurate estimation of ET. The objective of this study was to estimate the actual evapotranspiration (ETa) by the pySEBAL model and to compare it with the actual evapotranspiration measured by the lysimeter method of soybean crop in Adana, Turkey. Five Landsat 5 Thematic Mapper (TM) images and weather data were used for this study to estimate actual evapotranspiration by the pySEBAL model. The results showed a good relationship between ETa estimated by the pySEBAL model and ETa measured by the lysimeter method, with an R-2 of 0.73, an RMSE of 0.51 mm.day(-1), an MBE of 0.04 mm.day(-1) and a Willmott's index of agreement (d) of 0.90. Based on this study, there is a good relationship between the actual evapotranspiration estimated by the pySEBAL model and the actual evapotranspiration measured by the lysimeter method. Consequently, ETa of soybean crop can be estimated with high accuracy by the pySEBAL model in Adana, Turkey.Publication Estimating in-season actual evapotranspiration over a large-scale irrigation scheme in resource-limited conditions(Publ House Bulgarian, 2020-01-01) Sawadogo, Alidou; Gündoğdu, Kemal Sulhi; Traore, Farid; Kouadio, Louis; Hessels, Tim; Sawadogo, Alidou; GÜNDOĞDU, KEMAL SULHİ; Bursa Uludağ Üniversitesi/Ziraat Fakültesi/Biyosistem Mühendisliği Bölümü.; 0000-0002-7437-8415; 0000-0002-5591-4788; 0000-0002-7264-7220; ABI-4047-2020; DXY-6494-2022Reliable and readily available data on actual evapotranspiration (ETa) over large-scale areas throughout the crop growing season are critical for improved agricultural irrigation and water resource management. On-site data collection is costly, labour-intensive, and very challenging in resource-limited conditions. Thus, open-source satellite-based approaches might be adopted as cost-effective alternatives. In this study, the performance of a cost-effective and open source satellite-based approach for estimating ETa over a large-scale (1200 ha) irrigation system, the Kou Valley Irrigation Scheme (KVIS), in Burkina Faso was assessed. ETa values over the critical irrigation period during the 2014 dry season (January-April) were estimated using the Python module for Surface Energy Balance Algorithm for Land model (PySEBAL). Then, they were compared against the Water Productivity Open-access (FAO-WaPOR), and United States Geological Survey-Famine Early Warning Systems Network Operational Simplified Surface Energy Balance (USGS-FEWS NET's SSEBop) ETa over the same period at different temporal scales. Overall, ETa values were satisfactorily estimated throughout the crop growth season across the Kou Valley irrigation scheme using PySEBAL. They spatially varied depending on the soil type and crop, with daily values ranging from 4.09 mm day(-1) to 7.7 mm day(-1), for a seasonal average of 619 mm. The finer spatial resolution (30 m) of PySEBAL outputs allowed better estimations compared to the FAO-WaPOR and SSEBop-based approaches. Our findings help ascertain the use of the PySEBAL model in semi-arid environment in Burkina Faso, and could serve as a basis for developing strategies for improved irrigation water management in countries experiencing similar conditions such as Burkina Faso.Publication Crop type classification using Sentinel 2A-derived Normalized Difference Red Edge Index (NDRE) and machine learning approach(Bursa Uludağ Üniversitesi, 2024-03-20) GÜNDOĞDU, KEMAL SULHİ; Bantchina, Benjamin Bere; Bursa Uludağ Üniversitesi/Fen Bilimleri Enstitüsü/Biyosistem Mühendisliği Bölümü; 0000-0002-2593-426X; 0000-0002-5591-4788Satellite remote sensing (RS) enables the extraction of vital information on land cover and crop type. Land cover and crop type classification using RS data and machine learning (ML) techniques have recently gained considerable attention in the scientific community. This study aimed to enhance remote sensing research using high-resolution satellite imagery and a ML approach. To achieve this objective, ML algorithms were employed to demonstrate whether it was possible to accurately classify various crop types within agricultural areas using the Sentinel 2A-derived Normalized Difference Red Edge Index (NDRE). Five ML classifiers, namely Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP), were implemented using Python programming on Google Colaboratory. The target land cover classes included cereals, fallow, forage, fruits, grassland-pasture, legumes, maize, sugar beet, onion-garlic, sunflower, and watermelon-melon. The classification models exhibited strong performance, evidenced by their robust overall accuracy (OA). The RF model outperformed, with an OA rate of 95% and a Kappa score of 92%. It was followed by DT (88%), KNN (87%), SVM (85%), and MLP (82%). These findings showed the possibility of achieving high classification accuracy using NDRE from a few Sentinel 2A images. This study demonstrated the potential enhancement of the application of high-resolution satellite RS data and ML for crop type classification in regions that have received less attention in previous studies.