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Authors: Alexander Murynin 1 ; Konstantin Gorokhovskiy 2 ; Valery Bondur 3 and Vladimir Ignatiev 4

Affiliations: 1 "AEROCOSMOS", Institute for Scientific Research of Aerospace Monitoring and Institution of Russian Academy of Sciences Dorodnicyn Computing Centre of RAS, Russian Federation ; 2 "AEROCOSMOS" and Institute for Scientific Research of Aerospace Monitoring, Russian Federation ; 3 "AEROCOSMOS" Institute for Scientific Research of Aerospace Monitoring, Russian Federation ; 4 "AEROCOSMOS", Institute for Scientific Research of Aerospace Monitoring and Moscow Institute of Physics and Technology State University, Russian Federation

ISBN: 978-989-8565-50-1

Keyword(s): Image mining, remote sensing, crop yield forecasting, nonlinear regression.

Abstract: Availability of detailed multi-year remote sensing image sequences allows finding a relation between the measured features of vegetation condition history and agricultural yields. The large image sequence over 10 years is used to build and compare 4 yield prediction models. The models are developed trough gradual addition of complexity. The initial model is based on linear regression using vegetation indices. The final model is non-linear and takes into consideration long-term technological advances in agricultural productivity. The accuracy of models has been estimated using cross-validation method. Further ways for model accuracy improvement have been proposed.

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Paper citation in several formats:
Murynin, A.; Gorokhovskiy, K.; Bondur, V. and Ignatiev, V. (2013). Analysis of Large Long-term Remote Sensing Image Sequence for Agricultural Yield Forecasting.In Proceedings of the 4th International Workshop on Image Mining. Theory and Applications - Volume 1: IMTA-4, (VISIGRAPP 2013) ISBN 978-989-8565-50-1, pages 48-55. DOI: 10.5220/0004393400480055

@conference{imta-413,
author={Alexander Murynin. and Konstantin Gorokhovskiy. and Valery Bondur. and Vladimir Ignatiev.},
title={Analysis of Large Long-term Remote Sensing Image Sequence for Agricultural Yield Forecasting},
booktitle={Proceedings of the 4th International Workshop on Image Mining. Theory and Applications - Volume 1: IMTA-4, (VISIGRAPP 2013)},
year={2013},
pages={48-55},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004393400480055},
isbn={978-989-8565-50-1},
}

TY - CONF

JO - Proceedings of the 4th International Workshop on Image Mining. Theory and Applications - Volume 1: IMTA-4, (VISIGRAPP 2013)
TI - Analysis of Large Long-term Remote Sensing Image Sequence for Agricultural Yield Forecasting
SN - 978-989-8565-50-1
AU - Murynin, A.
AU - Gorokhovskiy, K.
AU - Bondur, V.
AU - Ignatiev, V.
PY - 2013
SP - 48
EP - 55
DO - 10.5220/0004393400480055

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