Each GOES satellite hovers over a fixed location on the globe. GOES-16 is positioned at (0°N, 75.2°W) and GOES-17 at (0°N, 137.2°W). The further these satellites scan from their position, the further apart their sample points will be. This means that there will be a high density of readings directly under each satellite and a low density further away.
We can visualize this by drawing a raster of a GOES scan where each cell is colored by the total number of sample points that fall within its bounds:
library(MazamaSatelliteUtils) library(MazamaSpatialUtils) setSatelliteDataDir("~/Data/Satellite") setSpatialDataDir("~/Data/Spatial") loadSpatialData("NaturalEarthAdm1") knitr::opts_chunk$set(echo = TRUE) goesaodc_downloadScanFiles( satID = "G16", datetime = "2021-05-12 09:00", timezone = "America/Chicago" ) goesaodc_downloadScanFiles( satID = "G17", datetime = "2021-05-12 09:00", timezone = "America/Chicago" ) scanFileG16 <- goesaodc_listScanFiles( satID = "G16", datetime = "2021-05-12 09:00", timezone = "America/Chicago" ) scanFileG17 <- goesaodc_listScanFiles( satID = "G17", datetime = "2021-05-12 09:00", timezone = "America/Chicago" ) scanRasterG16 <- goesaodc_createScanRaster( filename = scanFileG16, cellSize = 0.1, dqfLevel = 3, fun = "count" ) scanRasterG17 <- goesaodc_createScanRaster( filename = scanFileG17, cellSize = 0.1, dqfLevel = 3, fun = "count" ) goesaodc_plotScanRaster( raster = scanRasterG16, legendLimits = c(0, 25), stateCodes = datasets::state.abb, legendTitle = "# readings", title = "GOES-16 Readings Per .1°x.1° Cell" ) goesaodc_plotScanRaster( raster = scanRasterG17, legendLimits = c(0, 25), stateCodes = datasets::state.abb, legendTitle = "# readings", title = "GOES-17 Readings Per .1°x.1° Cell" )
It's important to keep this distortion in mind when choosing which satellite to use for analyzing a region's AOD data. The GOES-16 satellite has a scan range covering the entire CONUS at DQF levels of 2 or higher, but it has fewer sample points over the west coast states compared to GOES-17.
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