A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images

Remote Sensing

Fernando Sedano³  Pieter Kempeneers²  George Hurtt³  Clement Atzberger¹ 

[1] Department of Geographical Sciences, University of Maryland, 2181 LeFrak Hall, College Park, MD 20740, USA; E-Mail;VITO, Boeretang 200, B-2400 Mol, Belgium; E-Mail:;Department of Geographical Sciences, University of Maryland, 2181 LeFrak Hall, College Park, MD 20740, USA; E-Mail:

关键词: Kalman filter;    Landsat;    MODIS;    time series;    data fusion;    filtering;    smoothing;    monitoring;    uncertainty;   

DOI  :  10.3390/rs61212381

来源: mdpi

【 摘 要 】

A data assimilation method to produce complete temporal sequences of synthetic medium-resolution images is presented. The method implements a Kalman filter recursive algorithm that integrates medium and moderate resolution imagery. To demonstrate the approach, time series of 30-m spatial resolution NDVI images at 16-day time steps were generated using Landsat NDVI images and MODIS NDVI products at four sites with different ecosystems and land cover-land use dynamics. The results show that the time series of synthetic NDVI images captured seasonal land surface dynamics and maintained the spatial structure of the landscape at higher spatial resolution. The time series of synthetic medium-resolution NDVI images were validated within a Monte Carlo simulation framework. Normalized residuals decreased as the number of available observations increased, ranging from 0.2 to below 0.1. Residuals were also significantly lower for time series of synthetic NDVI images generated at combined recursion (smoothing) than individually at forward and backward recursions (filtering). Conversely, the uncertainties of the synthetic images also decreased when the number of available observations increased and combined recursions were implemented.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.