Optimisation of global grids for high-resolution remote sensing data

• High resolution satellite remote sensing will deliver unprecedented data volumes.
• Efficient spatial data handling is dependent on suitable map projections.
• The introduced Grid Oversampling Factor (GOF) estimates local data oversampling.
• Remarkably, equidistant projections are more suitable than equal-area.
• A Global system of continental subgrids oversamples with only 2% (the Equi7 Grid).

Upcoming remote sensing systems onboard satellites will generate unprecedented volumes of spatial data, hence challenging processing facilities in terms of storage and processing capacities. Thus, an efficient handling of remote sensing data is of vital importance, demanding a well-suited definition of spatial grids for the data׳s storage and manipulation. For high-resolution image data, regular grids defined by map projections have been identified as practicable, cognisant of their drawbacks due to geometric distortions. To this end, we defined a new metric named grid oversampling factor (GOF) that estimates local data oversampling appearing during projection of generic satellite images to a regular raster grid. Based on common map projections, we defined sets of spatial grids optimised to minimise data oversampling. Moreover, they ensure that data undersampling cannot occur at any location. From the resulting GOF-values we concluded that equidistant projections are most suitable, with a global mean oversampling of 2% when using a system of seven continental grids (introduced under the name Equi7 Grid). Opposed to previous studies that suggested equal-area projections, we recommend the Plate Carrée, the Equidistant Conic and the Equidistant Azimuthal projection for global, hemispherical and continental grids, respectively.