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Journal of Automation and Information Sciences

Publicou 12 edições por ano

ISSN Imprimir: 1064-2315

ISSN On-line: 2163-9337

SJR: 0.173 SNIP: 0.588 CiteScore™:: 2

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Winter Wheat Yield Forecasting: a Comparative Analysis of Results of Regression and Biophysical Models

Volume 45, Edição 6, 2013, pp. 68-81
DOI: 10.1615/JAutomatInfScien.v45.i6.70
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RESUMO

Relative efficiency of using satellite data to winter wheat yield forecasting in Ukraine at region level is assessed. The efficiency of forecasting on the basis of empirical and biophysical models of agricultural crops is compared. As empirical yields models the linear regression models of yield dependency on 16-day index NDVI composite on the basis of MODIS data with spatial resolution 250 m (MOD 13) are considered as well as nonlinear regression model, in which daily meteorological data of 180 local meteorological stations are used as predictors. The empirical approach to prediction is compared with biophysical which is implemented in the system CGMS, adapted for the Ukraine and based on the WOFOST model. For parameters identification of the yield models the official statistical data is used of winter wheat yield at the regional level for the period of 2000−2009. Validation of models is done on independent data for 2010 and 2011. The obtained results showed that when training models for 2000−2009 and 2000−2010 years and validating for 2010 and 2011 respectively all three approaches show similar accuracy. Average root mean square prediction error is approximately 0.6 c/ha.

CITADO POR
  1. Skakun Sergii, Kussul Nataliia, Shelestov Andrii, Kussul Olga, The use of satellite data for agriculture drought risk quantification in Ukraine, Geomatics, Natural Hazards and Risk, 7, 3, 2016. Crossref

  2. Skakun Sergii, Kussul Nataliia, Kussul Olga, Shelestov Andrii, Quantitative estimation of drought risk in Ukraine using satellite data, 2014 IEEE Geoscience and Remote Sensing Symposium, 2014. Crossref

  3. Kravchenko Oleksii, Lavrenyuk Mykola, Kussul Nataliia, Orthorectification of Sich-2 satellite images using elastic models, 2014 IEEE Geoscience and Remote Sensing Symposium, 2014. Crossref

  4. Castaldi F., Casa R., Pelosi F., Yang H., Influence of acquisition time and resolution on wheat yield estimation at the field scale from canopy biophysical variables retrieved from SPOT satellite data, International Journal of Remote Sensing, 36, 9, 2015. Crossref

  5. Kussul Nataliia, Skakun Sergii, Shelestov Andrii, Kussul Olga, The use of satellite SAR imagery to crop classification in Ukraine within JECAM project, 2014 IEEE Geoscience and Remote Sensing Symposium, 2014. Crossref

  6. Kussul Nataliia, Lemoine Guido, Gallego Javier, Skakun Sergii, Lavreniuk Mykola, Parcel based classification for agricultural mapping and monitoring using multi-temporal satellite image sequences, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015. Crossref

  7. Kussul Nataliia, Lemoine Guido, Gallego Francisco Javier, Skakun Sergii V., Lavreniuk Mykola, Shelestov Andrii Yu., Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 6, 2016. Crossref

  8. Zhang Xiaoyang, Zhang Qingyuan, Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations, ISPRS Journal of Photogrammetry and Remote Sensing, 114, 2016. Crossref

  9. Skakun S., Franch B., Roger J.-C., Vermote E., Becker-Reshef I., Justice C., Santamaria-Artigas A., Incorporating yearly derived winter wheat maps into winter wheat yield forecasting model, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016. Crossref

  10. Kussul Nataliia, Kolotii Andrii, Shelestov Andrii, Yailymov Bohdan, Lavreniuk Mykola, Land degradation estimation from global and national satellite based datasets within UN program, 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2017. Crossref

  11. Kussul N., Kolotii A., Adamenko T., Yailymov B., Shelestov A., Lavreniuk M., Ukrainian cropland through decades: 1990–2016, 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), 2017. Crossref

  12. Shelestov Andrii, Lavreniuk Mykola, Kussul Nataliia, Novikov Alexei, Skakun Sergii, Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping, Frontiers in Earth Science, 5, 2017. Crossref

  13. Kussul Nataliia, Lavreniuk Mykola, Skakun Sergii, Shelestov Andrii, Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data, IEEE Geoscience and Remote Sensing Letters, 14, 5, 2017. Crossref

  14. Shelestov Andrii, Kolotii Andrii, Skakun Sergii, Baruth Bettina, Lozano Raul Lopez, Yailymov Bohdan, Biophysical parameters mapping within the SPOT-5 Take 5 initiative, European Journal of Remote Sensing, 50, 1, 2017. Crossref

  15. Kussul Nataliia, Kolotii Andrii, Shelestov Andrii, Lavreniuk Mykola, Bellemans Nicolas, Bontemps Sophie, Defourny Pierre, Koetz Benjamin, Sentinel-2 for agriculture national demonstration in ukraine: Results and further steps, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017. Crossref

  16. YAILYMOV B.Ya., LAVRENIUK M.S., SHELESTOV A.Yu., KOLOTII A.V., YAILYMOVA H.O., FEDOROV O.P., Methods of essential variables determination for the Earth’s surface state assessing, Kosmìčna nauka ì tehnologìâ, 24, 4, 2018. Crossref

  17. Kussul Nataliia, Lavreniuk Mykola, Skakun Sergii, Shelestov Andrii, Cropland productivity assessment for Ukraine based on time series of optical satellite images, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017. Crossref

  18. Sui Juan, Qin Qiming, Ren Huazhong, Sun Yuanheng, Zhang Tianyuan, Wang Jiandong, Gong Shihong, Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations, Remote Sensing, 10, 6, 2018. Crossref

  19. Basso Bruno, Liu Lin, , 154, 2019. Crossref

  20. Lukin Vladimir, Rubel Oleksii, Kozhemiakin Ruslan, Abramov Sergey, Shelestov Andrii, Lavreniuk Mykola, Meretsky Mykola, Vozel Benoit, Chehdi Kacem, Despeckling of Multitemporal Sentinel SAR Images and Its Impact on Agricultural Area Classification, in Recent Advances and Applications in Remote Sensing, 2018. Crossref

  21. Zhao Hongwei, Chen Zhongxin, Jiang Hao, Jing Wenlong, Sun Liang, Feng Min, Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China, Remote Sensing, 11, 22, 2019. Crossref

  22. Habyarimana Ephrem, Piccard Isabelle, Catellani Marcello, De Franceschi Paolo, Dall’Agata Michela, Towards Predictive Modeling of Sorghum Biomass Yields Using Fraction of Absorbed Photosynthetically Active Radiation Derived from Sentinel-2 Satellite Imagery and Supervised Machine Learning Techniques, Agronomy, 9, 4, 2019. Crossref

  23. Palchowdhuri Y., Valcarce-Diñeiro R., King P., Sanabria-Soto M., Classification of multi-temporal spectral indices for crop type mapping: a case study in Coalville, UK, The Journal of Agricultural Science, 156, 1, 2018. Crossref

  24. Yang Wenze, Kogan Felix, Guo Wei, An Ongoing Blended Long-Term Vegetation Health Product for Monitoring Global Food Security, Agronomy, 10, 12, 2020. Crossref

  25. Filippi Patrick, Whelan Brett M., Vervoort R. Willem, Bishop Thomas F.A., Mid-season empirical cotton yield forecasts at fine resolutions using large yield mapping datasets and diverse spatial covariates, Agricultural Systems, 184, 2020. Crossref

  26. Zhao Hongwei, Duan Sibo, Liu Jia, Sun Liang, Reymondin Louis, Evaluation of Five Deep Learning Models for Crop Type Mapping Using Sentinel-2 Time Series Images with Missing Information, Remote Sensing, 13, 14, 2021. Crossref

  27. Habyarimana Ephrem, Bartelds Nicole, Yield Prediction in Sorghum (Sorghum bicolor (L.) Moench) and Cultivated Potato (Solanum tuberosum L.), in Big Data in Bioeconomy, 2021. Crossref

  28. Pan Haizhu, Chen Zhongxin, Crop Growth Modeling and Yield Forecasting, in Agro-geoinformatics, 2021. Crossref

  29. Zhang Ning, Zhao Chen, Quiring Steven M., Li Jinlin, Winter Wheat Yield Prediction Using Normalized Difference Vegetative Index and Agro‐Climatic Parameters in Oklahoma, Agronomy Journal, 109, 6, 2017. Crossref

  30. Shangguan Yulin, Li Xiyu, Lin Yi, Deng Jinsong, Yu Le, Mapping spatial-temporal nationwide soybean planting area in Argentina using Google Earth Engine, International Journal of Remote Sensing, 43, 5, 2022. Crossref

  31. Ahmed A. A. Masrur, Sharma Ekta, Jui S. Janifer Jabin, Deo Ravinesh C., Nguyen-Huy Thong, Ali Mumtaz, Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors, Remote Sensing, 14, 5, 2022. Crossref

  32. Hnatushenko Volodvmyr V., Sierikova Kateryna Yu., Sierikov Ivan Yu., Development of a Cloud-Based Web Geospatial Information System for Agricultural Monitoring Using Sentinel-2 Data, 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), 2018. Crossref

  33. Gallego Francisco Javier, Kussul Nataliia, Skakun Sergii, Kravchenko Oleksii, Shelestov Andrii, Kussul Olga, Efficiency assessment of using satellite data for crop area estimation in Ukraine, International Journal of Applied Earth Observation and Geoinformation, 29, 2014. Crossref

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