Abstract
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•The absolute mean error of the SuperDove surface reflectance were 6%.•The SuperDove surface reflectance was on average almost identical to Sentinel-2.•The probability was 90% of acquiring stable multi-satellite surface reflectance.•68% of single-satellite data were more likely to acquire stable surface reflectance.•The SuperDove time series was strongly correlated to Sentinel-2 on vegetation.
Advances in the capabilities of commercial CubeSat constellations have enabled the retrieval of multi-spectral surface reflectance data over the Earth’s terrestrial surfaces on an almost daily basis. For example, while the earliest versions of Planet’s CubeSats provided tri-band and then quad-band optical image data, the most recent iterations deliver imaging capabilities with eight unique spectral bands for Earth system monitoring. To determine the utility of these rich geospatial data collections for a range of applications, it is necessary to characterise their radiometric accuracy. Leveraging on-ground spectroradiometer measurements of radiometrically pseudo-invariant features, we assess the absolute accuracy of an annual sequence of Planet SuperDove surface reflectance data. Date-coincident and spectrally overlapping Sentinel-2 image data were also used to assess the relative radiometric accuracy of the CubeSat reflectance data. Additionally, confidence levels for acquiring consistent SuperDove surface reflectance data were calculated, and the multi-temporal patterns of surface reflectance between the SuperDove and Sentinel-2 surface reflectance products were evaluated by examining their rank correlation. Our findings demonstrate that (1) the average accuracy of the Coastal-Blue, Blue, Green I, and Green II SuperDove surface reflectance bands was 5% higher than for the Yellow, Red, Red-Edge, and Near-Infrared bands; (2) the SuperDove surface reflectance data was on average almost identical to the surface reflectance derived from the coincident Sentinel-2 data; and (3) the radiometric accuracies (i.e., mean errors) can be improved by using band combinations (e.g. normalised difference vegetation index). However, due to the different radiometric performance between the spectral bands, some vegetation indices (e.g. the Yellowness Index) did not provide a linear relationship between the SuperDove and reference data. The probability of acquiring surface reflectance data with less than 5% reflectance variation was approximately 90% for the annual multi-satellite dataset. On average, 68% of the data derived from a single satellite had at least 95% probability of acquiring surface reflectance with less than 5% variation. The time series patterns of the SuperDove reflectance data had an average rank correlation coefficient of 0.67 for all types of surface units and 0.77 for vegetated surfaces when compared with the spectrally overlapping bands of the Sentinel-2 surface reflectance data. In addition to providing confidence levels for users to directly assess the radiometric accuracies of SuperDove surface reflectance products, we also provide suggestions to improve the data quality from an end-user’s perspective.