| Summary: | Analyses of Planet Dove (CubeSat) satellite imagery can prove challenging when radiometric consistency among contiguous scenes is necessary to apply algorithms across mosaics or through time. While temporal resolution is excellent due to the high number of Dove satellites, the radiometric consistency between scenes can be a limiting factor in the utility of the imagery when creating mosaics. To date, methods of addressing the radiometric consistency problem have focused on radiometric correction in a time-series stack, but do not address how to achieve seamless mosaics within that stack. We demonstrate a method to correct Planet Dove radiometric inconsistencies in mosaics using locally estimated scatterplot smoothing (LOESS). LOESS is a non-parametric regression technique that is more flexible to fitting a line to data than ordinary least squares regression (OLS), and better conforms to the nonlinearities in spectral responses among Dove sensors across brightness levels. A straightforward application of LOESS uses overlap areas between contiguous scenes, enabling correction independent of outside data sources. We demonstrate the effectiveness of the method to improve the quality of mosaics by reducing spectral discontinuities between overlapping images in multiple locations across multiple biome and landcover types. Further, the LOESS Radiometric Correction for Contiguous Scenes, or LORACCS, is able to be integrated with methods previously developed to normalize Dove imagery with sensors such as Landsat or Sentinel-2, typically for use in time series analysis. Written in Python, this method offers an open-source solution to a significant challenge when working with Dove data.
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