  Many earth science applications require data at both high spatial and
temporal resolution for effective monitoring of various ecosystem resources.
Due to practical limitations in sensor design, there is often a trade-off in
different resolutions of spatio-temporal datasets and hence a single sensor
alone cannot provide the required information. Various data fusion methods have
been proposed in the literature that mainly rely on individual timesteps when
both datasets are available to learn a mapping between features values at
different resolutions using local relationships between pixels. Earth
observation data is often plagued with spatially and temporally correlated
noise, outliers and missing data due to atmospheric disturbances which pose a
challenge in learning the mapping from a local neighborhood at individual
timesteps. In this paper, we aim to exploit time-independent global
relationships between pixels for robust transfer of information across
different scales. Specifically, we propose a new framework, ORBIT (Ordering
Based Information Transfer) that uses relative ordering constraint among pixels
to transfer information across both time and scales. The effectiveness of the
framework is demonstrated for global surface water monitoring using both
synthetic and real-world datasets.
