  Products derived from a single multispectral sensor are hampered by a limited
spatial, spectral or temporal resolutions. Image fusion in general and
downscaling/blending in particular allow to combine different multiresolution
datasets. We present here an optimal interpolation approach to generate
smoothed and gap-free time series of Landsat reflectance data. We fuse MODIS
(moderate-resolution imaging spectroradiometer) and Landsat data globally using
the Google Earth Engine (GEE) platform. The optimal interpolator exploits GEE
ability to ingest large amounts of data (Landsat climatologies) and uses simple
linear operations that scale easily in the cloud. The approach shows very good
results in practice, as tested over five sites with different vegetation types
and climatic characteristics in the contiguous US.
