Particle Filter Approach for Real-Time Estimation of Crop Phenological States Using Time Series of NDVI Images

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Title: Particle Filter Approach for Real-Time Estimation of Crop Phenological States Using Time Series of NDVI Images
Authors: De Bernardis, Caleb G. | Vicente-Guijalba, Fernando | Martínez Marín, Tomás | Lopez-Sanchez, Juan M.
Research Group/s: Señales, Sistemas y Telecomunicación
Center, Department or Service: Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Instituto Universitario de Investigación Informática
Keywords: Agriculture | Phenology | Time series | NDVI | State space formulation | Particle filter
Knowledge Area: Teoría de la Señal y Comunicaciones
Issue Date: 20-Jul-2016
Publisher: MDPI
Citation: De Bernardis C, Vicente-Guijalba F, Martinez-Marin T, Lopez-Sanchez JM. Particle Filter Approach for Real-Time Estimation of Crop Phenological States Using Time Series of NDVI Images. Remote Sensing. 2016; 8(7):610. doi:10.3390/rs8070610
Abstract: Knowing the current phenological state of an agricultural crop is a powerful tool for precision farming applications. In the past, it has been estimated with remote sensing data by exploiting time series of Normalised Difference Vegetation Index (NDVI), but always at the end of the campaign and only providing results for some key states. In this work, a new dynamical framework is proposed to provide real-time estimates in a continuous range of states, for which NDVI images are combined with a prediction model in an optimal way using a particle filter. The methodology is tested over a set of 8 to 13 rice parcels during 2008–2013, achieving a high determination factor R2=0.93 ( n=379 ) for the complete phenological range. This method is also used to predict the end of season date, obtaining a high accuracy with an anticipation of around 40–60 days. Among the key advantages of this approach, phenology is estimated each time a new observation is available, hence enabling the potential detection of anomalies in real-time during the cultivation. In addition, the estimation procedure is robust in the case of noisy observations, and it is not limited to a few phenological stages.
Sponsor: This work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under Projects TEC2011-28201-C02-02 and TIN2014-55413-C2-2-P.
URI: http://hdl.handle.net/10045/57293
ISSN: 2072-4292
DOI: 10.3390/rs8070610
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
Peer Review: si
Publisher version: http://dx.doi.org/10.3390/rs8070610
Appears in Collections:INV - SST - Artículos de Revistas

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