MEASUREMENT AGGREGATION EFFECTS ON THE SPATIAL STATISTICS OF SEA SURFACE TEMPERATURE AND CHLOROPHYLL-a OVER THE NORTH AEGEAN

摘要

The effects of measurement scale (spatial resolution) on the statistics derived from environmental data constitute a critical factor in spatial pattern investigation. In this work, we introduce a geostatistical approach to analytically predict the effects of spatial aggregation (upscaling) on spatial statistics (correlation or semivariogram functions) of geospatial data pertaining to environmental variables. More precisely, fine and coarse resolution data are linked through an aggregation function (linear in this work) quantifying the contribution of the former to the latter. The sample semivariogram of the coarse data is then analytically approximated based on a known or estimated semivariogram model for the fine resolution data and that aggregation function.We illustrate the application of the proposed analytical approach using monthly and seasonal measurements of sea surface temperature (SST) and chlorophyll-a (CHL) concentrations for a decay (period 2005-2014), obtained from the MODIS Aqua sensor over the North Aegean Sea, Greece. More precisely, we study the effect of upscaling on the empirical semivariogram functions of SST and CHL for different pixel sizes. The results show that the proposed geostatistical approach can accurately and consistently predict the effects of spatial aggregation (upscaling) on the semivariogram functions derived at coarser spatial resolutions.