The radiometric accuracy of the 8-band multi-spectral surface reflectance from the planet SuperDove constellation

https://doi.org/10.1016/j.jag.2022.103035Get rights and content
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Highlights

  • 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.

Abstract

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.

Keywords

CubeSat
PlanetScope constellation
SuperDove
Surface reflectance
Radiometric accuracy

Abbreviations

RGB
red-greenblue
NIR
near-infrared
VIS-NIR
visible and near-infrared
MODIS
moderate resolution imaging spectroradiometer
OLI
operational land imager
AERONET
aerosol robotic network
RadCalNet
radiometric calibration network
MAE
mean absolute error
NPOESS
national polar-orbiting operational environmental satellite system
APU
accuracy, precision, and uncertainty
VI
vegetation index
NDVI
normalised difference vegetation index
RENDVI
normalised difference red-edge vegetation index
YNDVI
normalised difference yellow vegetation index
GNDVI
normalised difference green vegetation index
YI
yellowness index
Chl550
green chlorophyll reflectance index
Chl700
red-edge chlorophyll reflectance index
CRI550
green carotenoid reflectance index
CRI700
red-edge carotenoid reflectance index
ARI
anthocyanin reflectance index
BRDF
bi-directional reflectance distribution function
IQR
interquartile range
AOD
aerosol optical depth
CESTEM
CubeSat-enabled spatio-temporal enhancement method
FORCE
framework for operational radiometric correction for environmental monitoring

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