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Ömer Vanlı
Department of Geographical Information Technologies, Istanbul Technical University, Istanbul, Turkey

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Research article
Published: 10 October 2020 in Journal of the Indian Society of Remote Sensing
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Accurate and timely information on yield forecasting is necessary for policymakers in decision-making. The case study was planned to develop a framework for the regional wheat yield forecasting model for southeastern Turkey. Therefore, after implementing Top of Atmospheric (TOA) correction for all images and collecting ground-truthing point of 313 fields from the Nurdagi and Islahiye counties. A total of eight machine learning algorithms were tuned and tested for the satellite image classification so that best model was used for the spatial distribution of wheat crop. The results of machine learning algorithms showed an accuracy greater than 90%. As the best model, the random forest was used for image classification. The classification results showed that area estimated by the classifier were 11% more than those reported by the Turkish statistical department. The observed and predicted yield of the tested model was closed to each other with root mean square error (RMSE) of 198 kg ha−1. The observed and predicted yield showed a close agreement with RMSE of 144 kg ha−1 at Nurdagi and 68 kg ha−1 at Islahiye for 5 years. It is concluded that remote sensing is useful tools for estimation of yield and developed can be used for other regions and crops.

ACS Style

Ömer Vanli; Ishfaq Ahmad; Burak Berk Ustundag. Area Estimation and Yield Forecasting of Wheat in Southeastern Turkey Using a Machine Learning Approach. Journal of the Indian Society of Remote Sensing 2020, 48, 1757 -1766.

AMA Style

Ömer Vanli, Ishfaq Ahmad, Burak Berk Ustundag. Area Estimation and Yield Forecasting of Wheat in Southeastern Turkey Using a Machine Learning Approach. Journal of the Indian Society of Remote Sensing. 2020; 48 (12):1757-1766.

Chicago/Turabian Style

Ömer Vanli; Ishfaq Ahmad; Burak Berk Ustundag. 2020. "Area Estimation and Yield Forecasting of Wheat in Southeastern Turkey Using a Machine Learning Approach." Journal of the Indian Society of Remote Sensing 48, no. 12: 1757-1766.

Article
Published: 06 October 2019 in Water
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Although the complexity of physically-based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e., Hydrologiska Bryåns Vattenbalansavdelning (HBV). This has rarely been done for conceptual models, as satellite data are often used in the spatial calibration of the distributed models. Three different soil moisture products from the European Space Agency Climate Change Initiative Soil Measure (ESA CCI SM v04.4), The Advanced Microwave Scanning Radiometer on the Earth Observing System (EOS) Aqua satellite (AMSR-E), soil moisture active passive (SMAP), and total water storage anomalies from Gravity Recovery and Climate Experiment (GRACE) are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms, all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture, are used to analyze the contribution of each individual source of information.

ACS Style

Mehmet Cüneyd Demirel; Alparslan Özen; Selen Orta; Emir Toker; Hatice Kübra Demir; Ömer Ekmekcioğlu; Hüsamettin Tayşi; Sinan Eruçar; Ahmet Bilal Sağ; Ömer Sarı; Ecem Tuncer; Hayrettin Hancı; Türkan Irem Özcan; Hilal Erdem; Mehmet Melih Koşucu; Eyyup Ensar Başakın; Kamal Ahmed; Awat Anwar; Muhammet Bahattin Avcuoğlu; Ömer Vanlı; Simon Stisen; Martijn J. Booij. Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration. Water 2019, 11, 2083 .

AMA Style

Mehmet Cüneyd Demirel, Alparslan Özen, Selen Orta, Emir Toker, Hatice Kübra Demir, Ömer Ekmekcioğlu, Hüsamettin Tayşi, Sinan Eruçar, Ahmet Bilal Sağ, Ömer Sarı, Ecem Tuncer, Hayrettin Hancı, Türkan Irem Özcan, Hilal Erdem, Mehmet Melih Koşucu, Eyyup Ensar Başakın, Kamal Ahmed, Awat Anwar, Muhammet Bahattin Avcuoğlu, Ömer Vanlı, Simon Stisen, Martijn J. Booij. Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration. Water. 2019; 11 (10):2083.

Chicago/Turabian Style

Mehmet Cüneyd Demirel; Alparslan Özen; Selen Orta; Emir Toker; Hatice Kübra Demir; Ömer Ekmekcioğlu; Hüsamettin Tayşi; Sinan Eruçar; Ahmet Bilal Sağ; Ömer Sarı; Ecem Tuncer; Hayrettin Hancı; Türkan Irem Özcan; Hilal Erdem; Mehmet Melih Koşucu; Eyyup Ensar Başakın; Kamal Ahmed; Awat Anwar; Muhammet Bahattin Avcuoğlu; Ömer Vanlı; Simon Stisen; Martijn J. Booij. 2019. "Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration." Water 11, no. 10: 2083.

Research article
Published: 10 August 2019 in Environmental Science and Pollution Research
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The extreme temperatures and uneven distribution of rainfall associated with climate change are expected to affect agricultural productivity and food security. A study was conducted to evaluate the impact of climate change on wheat in southeastern regions of Turkey. The CERES-wheat crop simulation model was calibrated and evaluated with data from eight surveyed farms. The four farms were used for calibration and four for evaluation. Climate change scenarios were developed for the middle (2036–2065) and late 21st century (2066–2095) under representative concentration pathways (RCPs 4.5 and 8.5) for study sites in Islahiye and Nurdagi. Model calibration results showed a good agreement between observed and simulated yield with only a 1 to 11% range of error. The model evaluation results showed good fit between observed and simulated values of all parameters with % error ranged from 0.51 to 13.3%. Future climate change projections showed that maximum temperature (Tmax) will increase between 1.6 °C (RCP4.5) and 2.3 °C (RCP8.5), while minimum temperature (Tmin) will increase between 1.0 °C (RCP4.5) and 1.5 °C (RCP8.5) for mid-century. At the end of the century, Tmax is projected to increase from 2 °C (RCP4.5) to 4 °C (RCP8.5) and Tmin from 1.3 °C (RCP4.5) to 3.1 °C (RCP8.5). Climate change impacts results showed that future rise in temperature will reduce wheat yield by 16.3% in mid-century and 16.8% at the end of the century at Islahiye and for Nurdagi, while 13.0% in mid and 14.4% end of the century. The use of climate and crop modeling technique provides useful information in evaluating the climate change impacts and may assist stakeholders to make decisions to overcome the negative impacts in the near and long term.

ACS Style

Ömer Vanli; Burak Berk Ustundag; Ishfaq Ahmad; Ixchel M. Hernandez-Ochoa; Gerrit Hoogenboom. Using crop modeling to evaluate the impacts of climate change on wheat in southeastern turkey. Environmental Science and Pollution Research 2019, 26, 29397 -29408.

AMA Style

Ömer Vanli, Burak Berk Ustundag, Ishfaq Ahmad, Ixchel M. Hernandez-Ochoa, Gerrit Hoogenboom. Using crop modeling to evaluate the impacts of climate change on wheat in southeastern turkey. Environmental Science and Pollution Research. 2019; 26 (28):29397-29408.

Chicago/Turabian Style

Ömer Vanli; Burak Berk Ustundag; Ishfaq Ahmad; Ixchel M. Hernandez-Ochoa; Gerrit Hoogenboom. 2019. "Using crop modeling to evaluate the impacts of climate change on wheat in southeastern turkey." Environmental Science and Pollution Research 26, no. 28: 29397-29408.