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Sentinel-3 is a newly launched satellite implemented by the European Space Agency (ESA) for global observation. The Ocean and Land Colour Imager (OLCI) sensor onboard Sentinel-3 provides 21 band images with a fine spectral resolution and is of great value for ocean, land and atmospheric monitoring. The two platforms (Sentinel-3A and -3B) can provide OLCI images at an almost daily temporal resolution. The coarse spatial resolution of the 21 band OLCI images (i.e., 300 m), however, limits greatly their utility for local, precise monitoring. Sentinel-2, another satellite provided by ESA, carries the Multispectral Imager (MSI) sensor which can supply much finer spatial resolution (e.g., 10 m and 20 m) images. This paper introduces a new fusion framework integrating spatio-temporal-spectral information for downscaling Sentinel-3 OLCI images, which has two parts. Based on bands with similar wavelengths (i.e., bands 2, 3, 4 and 8a for Sentinel-2 and bands Oa4, Oa6, Oa8 and Oa17 for Sentinel-3), the four Sentinel-3 bands are first downscaled to the spatial resolution of Sentinel-2 images by applying spatio-temporal fusion to Sentinel-2 MSI and Sentinel-3 OLCI images. Then, to take full advantage of all 21 available OLCI bands of the Sentinel-3 images, the extended image pair-based spatio-spectral fusion (EIPSSF) method is proposed in this paper to downscale the other 17 bands. EIPSSF is performed based on the new concept of the extended image pair (EIP) and by exploiting existing spatio-temporal fusion approaches. The framework consisting of spatio-temporal and spatio-spectral fusion is entirely general, which provides a practical solution for comprehensive downscaling of Sentinel-3 OLCI images for fine spatial, temporal and spectral resolution monitoring. [ABSTRACT FROM AUTHOR]

The point spread function (PSF) is ubiquitous in remote sensing. This paper investigated the effect of the PSF on the downscaling of continua. Geostatistical approaches were adopted to incorporate explicitly, and reduce the influence of, the PSF effect in downscaling. Two general cases were considered: univariate and multivariate. In the univariate case, the input coarse spatial resolution image is the only image available for downscaling. Area-to-point kriging was demonstrated to be a suitable solution in this case. For the multivariate case, a finer spatial resolution image (or images) observed under different conditions (e.g., at a different wavelength) is available as auxiliary data for downscaling. Area-to-point regression kriging was shown to be a suitable solution for this case. Moreover, a new solution was developed for estimating the PSF in image scale transformation. The experiments show that the PSF effect influences downscaling greatly and that downscaling can be enhanced obviously by considering the PSF effect through the geostatistical approaches and the PSF estimation solution proposed. [ABSTRACT FROM AUTHOR]

The Terra/Aqua MODerate resolution Imaging Spectroradiometer (MODIS) data have been used widely for global monitoring of the Earth's surface due to their daily fine temporal resolution. The spatial resolution of MODIS time-series (i.e., 500 m), however, is too coarse for local monitoring. A feasible solution to this problem is to downscale the coarse MODIS images, thus creating time-series images with both fine spatial and temporal resolutions. Generally, the downscaling of MODIS images can be achieved by fusing them with fine spatial resolution images (e.g., Landsat images) using spatio-temporal fusion methods. Among the families of spatio-temporal fusion methods, spatial unmixing-based methods have been applied widely owing to their lighter dependence on the available fine spatial resolution images. However, all techniques within this class of method suffer from the same serious problem, that is, the block effect, which reduces the prediction accuracy of spatio-temporal fusion. To our knowledge, almost no solution has been developed to tackle this issue directly. To address this need, this paper proposes a blocks-removed spatial unmixing (SU-BR) method, which removes the blocky artifacts by including a new constraint constructed based on spatial continuity. SU-BR provides a flexible framework suitable for any existing spatial unmixing-based spatio-temporal fusion method. Experimental results on a heterogeneous region, a homogeneous region and a region experiencing land cover changes show that SU-BR removes the blocks effectively and increases the prediction accuracy obviously in all three regions. SU-BR also outperforms two popular spatio-temporal fusion methods. SU-BR, thus, provides a crucial solution to overcome one of the longest standing challenges in spatio-temporal fusion. • All spatial unmixing-based fusion methods suffer from the block effect problem. • A blocks-removed spatial unmixing (SU-BR) method is proposed. • SU-BR improves existing spatial unmixing-based methods. • SU-BR is suitable for the existing spatial unmixing-based fusion method. [ABSTRACT FROM AUTHOR]