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

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...

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Bibliographic Details
Main Authors:	Fernando Sedano, Pieter Kempeneers, George Hurtt
Format:	Article
Language:	English
Published:	MDPI AG 2014-12-01
Series:	Remote Sensing
Subjects:	Kalman filter Landsat MODIS time series data fusion filtering smoothing monitoring uncertainty
Online Access:	http://www.mdpi.com/2072-4292/6/12/12381

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spelling	doaj-316a707741824f3db77272228e7e15ea2020-11-25T00:47:53ZengMDPI AGRemote Sensing2072-42922014-12-01612123811240810.3390/rs61212381rs61212381A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI ImagesFernando Sedano0Pieter Kempeneers1George Hurtt2Department of Geographical Sciences, University of Maryland, 2181 LeFrak Hall, College Park, MD 20740, USAVITO, Boeretang 200, B-2400 Mol, BelgiumDepartment of Geographical Sciences, University of Maryland, 2181 LeFrak Hall, College Park, MD 20740, USAA 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.http://www.mdpi.com/2072-4292/6/12/12381Kalman filterLandsatMODIStime seriesdata fusionfilteringsmoothingmonitoringuncertainty
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Fernando Sedano Pieter Kempeneers George Hurtt
spellingShingle	Fernando Sedano Pieter Kempeneers George Hurtt A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images Remote Sensing Kalman filter Landsat MODIS time series data fusion filtering smoothing monitoring uncertainty
author_facet	Fernando Sedano Pieter Kempeneers George Hurtt
author_sort	Fernando Sedano
title	A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images
title_short	A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images
title_full	A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images
title_fullStr	A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images
title_full_unstemmed	A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images
title_sort	kalman filter-based method to generate continuous time series of medium-resolution ndvi images
publisher	MDPI AG
series	Remote Sensing
issn	2072-4292
publishDate	2014-12-01
description	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.
topic	Kalman filter Landsat MODIS time series data fusion filtering smoothing monitoring uncertainty
url	http://www.mdpi.com/2072-4292/6/12/12381
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A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images

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