Analysis of large long-term remote sensing image sequence for agricultural yield forecasting

Alexander Murynin, Konstantin Gorokhovskiy, Valery Bondur, Vladimir Ignatiev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 4th International Workshop on Image Mining. Theory and Applications, IMTA 2013, In Conjunction with VISIGRAPP 2013
Pages48-55
Number of pages8
Publication statusPublished - 2013
Externally publishedYes
Event4th International Workshop on Image Mining. Theory and Applications, IMTA 2013, In Conjunction with VISIGRAPP 2013 - Barcelona, Spain
Duration: 23 Feb 201323 Feb 2013

Publication series

NameProceedings of the 4th International Workshop on Image Mining. Theory and Applications, IMTA 2013, In Conjunction with VISIGRAPP 2013

Conference

Conference4th International Workshop on Image Mining. Theory and Applications, IMTA 2013, In Conjunction with VISIGRAPP 2013
Country/TerritorySpain
CityBarcelona
Period23/02/1323/02/13

Fingerprint

Dive into the research topics of 'Analysis of large long-term remote sensing image sequence for agricultural yield forecasting'. Together they form a unique fingerprint.

Cite this