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TitleA prediction smooth method for blending landsat and Moderate Resolution Imagine Spectroradiometer images
AuthorZhong, DORCID logo; Zhou, F
SourceRemote Sensing vol. 10, no. 9, 1371, 2018., Open Access logo Open Access
Alt SeriesNatural Resources Canada, Contribution Series 20181052
PublisherMDPI AG
Mediapaper; on-line; digital
File formatpdf
Subjectsgeophysics; remote sensing
ProgramCanada Centre for Remote Sensing Flood Mapping Guidelines
Released2018 08 29
AbstractLandsat images have been widely used in support of responsible development of natural resources, disaster risk management (e.g., forest fire, flooding etc.), agricultural production monitoring, as well as environmental change studies due to its medium spatial resolution and rich spectral information. However, its availability and usability are largely constrained by its low revisit frequency. On the other hand, MODIS (Moderate Resolution Imaging Spectroradiometer) images for land studies have much more frequent coverage but with a lower spatial resolution of 250-500 m. To take advantages of the two sensors and expand their availability and usability, during the last decade, a number of image fusion methods have been developed for generating Landsat-like images from MODIS observations to supplement clear-sky Landsat imagery. However, available methods are typically effective or applicable for certain applications. For a better result, a new Prediction Smooth Reflectance Fusion Model (PSRFM) for blending Landsat and MODIS images is proposed. PSRFM consists of a dynamic prediction model and a smoothing filter. The dynamic prediction model generates synthetic Landsat images from a pair of Landsat and MODIS images and another MODIS image, either forward or backward in time. The smoothing filter combines the forward and backward predictions by weighted average based on elapsed time or on the estimated prediction uncertainty. Optionally, the smooth filtering can be applied with constraints based on Normalized Difference Snow Index (NDSI) or Normalized Difference Vegetation Index (NDVI). In comparison to some published reflectance fusion methods, PSRFM shows the following desirable characteristics: (1) it can deal with one pair or two pairs of Landsat and MODIS images; (2) it can incorporate input image uncertainty during prediction and estimate prediction uncertainty; (3) it can track gradual vegetation phenological changes and deal with abrupt land-cover type changes; and (4) for predictions using two pairs of input images, the results can be further improved through the constrained smoothing filter based on NDSI or NDVI for certain applications. We tested PSRFM to generate a Landsat-like image time series by using Landsat 8 OLI and MODIS (MOD09GA) images and compared it to two reflectance fusion algorithms: STARFM (Spatial and Temporal Adaptive Reflectance Fusion Model) and ESTARFM (Enhanced version of STARFM). The results show that the proposed PSRFM is effective and outperforms STARFM and ESTARFM both visually and quantitatively.

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