Skip to Main Content
Fitting the multitemporal curve: a fourier series approach to the missing data problem in remote sensing analysisAuthor(s): Evan Brooks; Valerie Thomas; Wynne Randolph; John Coulston
Source: IEEE Transactions on GeoScience and Remote Sensing 50(9):3340–3353
Publication Series: Scientific Journal (JRNL)
Station: Southern Research Station
PDF: Download Publication (853.82 KB)
DescriptionWith the advent of free Landsat data stretching back decades, there has been a surge of interest in utilizing remotely sensed data in multitemporal analysis for estimation of biophysical parameters. Such analysis is confounded by cloud cover and other image-specific problems, which result in missing data at various aperiodic times of the year. While there is a wealth of information contained in remotely sensed time series, the analysis of such time series is severely limited due to the missing data. This paper illustrates a technique which can greatly expand the possibilities of such analysis, a Fourier regression algorithm, here on time series of normalized difference vegetation indices (NDVIs) for Landsat pixels with a 30-m resolution. It compares the results with those using the spatial and temporal adaptive reflectance fusion model (STAR-FM), a popular approach that depends on having MODIS pixels with resolutions of 250 m or coarser. STAR-FM uses changes in the MODIS pixels as a template for predicting changes in the Landsat pixels. Fourier regression had an R2 of at least 90% over three quarters of all pixels, and it had the highest R2Predicted values (compared to STAR-FM) on two thirds of the pixels. The typical root-mean-square error for Fourier regression fitting was about 0.05 for NDVI, ranging from 0 to 1. This indicates that Fourier regression may be used to interpolate missing data for multitemporal analysis at the Landsat scale, especially for annual or longer studies.
- You may send email to email@example.com to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationBrooks, E.B.; Thomas, V.A.; Wynne, R.H.; Coulston, J.W. 2012. Fitting the multitemporal curve: a fourier series approach to the missing data problem in remote sensing analysis. IEEE Transactions on GeoScience and Remote Sensing 50(9):3340–3353.
KeywordsData fusion, disturbance, harmonic analysis, interpolation, phenology, time series
- Re-sampling remotely sensed data to improve national and regional mapping of forest conditions with confidential field data
- Tracking MODIS NDVI time series to estimate fuel accumulation
- Harmonic regression of Landsat time series for modeling attributes from national forest inventory data
XML: View XML