Mean and variance estimations with different pixel sizes : case study in a small water quality monitoring area in southern Finland

Spatial variation of water quality parameters produces inaccuracy in freshwater monitoring programs.Using spatially continuous measurements, such as remote sensing observations, this variation can be estimated.However, spaceborne remote sensing data that are usable for the operative monitoring of lake water quality suffer from coarse spatial resolution.Observations in small monitoring regimes are often restricted to only a few satellite image pixels that are not disturbed by the adjacent land areas.Consequently, it is often assumed that a medium-to coarse-resolution remote sensing does not contribute additional information for monitoring programs in these areas.The usability of pixel-type observations in a small monitoring area was assessed using a flow-through fluorometer in a moving boat.Nine spatially extensive data sets were collected from the Enonselk basin of Vesijrvi in southern Finland during the summers of 2005-2007.The effect of spatial resolution on the observed mean and standard deviation of the chlorophyll-a concentration was studied.The Getis-Ord Gi* analysis and spatial interpolation were used to define surface areas of locations, where chlorophyll-a concentration varied from the mean concentration.Our results suggest that the mean value can be estimated with reasonable accuracy even with a single pixel observation.The information of the variation is, however, lost with the coarser resolution observations.