paper-conference

Feature-wise Fusion of Optical Satellite Images and LiDAR Data: Pathway to Enhance Road Segmentation
Road segmentation based on optical images is still challenging due to the caused by occlusion, object blockage, and shadows. To address …
Dursun Zafer Seker, Ozan Ozturk, Mustafa Serkan Isik, Martin Kada
Corn Yield Prediction with Multi-Dimensional Earth Observation Data Using Machine Learning
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Mehmet Furkan Celik, Mustafa Serkan Isik
Machine Learning Based Soil Moisture Estimation using CYGNSS Mission
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Mustafa Serkan Isik, Mehmet Furkan Celik
Explainability of end and mid-season cotton yield predictors in CONUS
In this study, we examined the effectiveness of integrating satellite-based crop biophysical parameters, meteorological conditions, and soil properties for the end and mid-season cotton yield prediction in the continental United States (CONUS) region.
Mehmet Furkan Celik, Mustafa Serkan Isik, Esra Erten, Gustau Camps-Valls
Project DOI
Informative Earth Observation Variables for Cotton Yield Prediction Using Explainable Boosting Machine
Cotton, a vital crop in the global textile industry, faces challenges from climate and ecosystem changes. Accurate cotton yield …
Mehmet Furkan Celik, Mustafa Serkan Isik, Esra Erten, Gulsen Taskin
Project DOI
Interpretable cotton yield prediction model using Earth Observation time series
This study aimed to assess the influence of Earth observation (EO) time series data, specifically soil properties, climate variables, and Enhanced Vegetation Index, on predicting cotton yield using an explainable artificial intelligence model in Turkey.
Mustafa Serkan Isik, Mehmet Furkan Celik, Esra Erten
Project DOI
Monitoring Daily Soil Moisture in CONUS Region with CYGNSS Satellites
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Muhammed Rasit Cevikalp, Mehmet Furkan Celik, Mustafa Serkan Isik
Multi-source Data Fusion for Estimation of Soil Moisture with LSTM
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Mehmet Furkan Celik, Mustafa Serkan Isik, Onur Yuzugullu, Noura Fajraoui, Esra Erten