KEYWORDS: Signal to noise ratio, Spatial resolution, Radio propagation, Point spread functions, Near infrared, Monte Carlo methods, Image processing, Sensors, Error analysis, Image sensors
The Sentinel 2 (S2) orthorectification process converts the Level-1B (L1B) radiance data generated at sensor geometry into orthorectified top-of-atmosphere (TOA) reflectance data, which is the Level-1C (L1C) product distributed to users. However, the spatial resampling operations involved by the orthorectification also transform the radiometric quality of the data. In this work, we evaluate the impact of the S2 orthorectification process on the radiometric quality of the data, with focus on the radiometric uncertainty budget. In particular, the study reports the variation of the S2 noise model attached to the S2 L1C metadata and the effects on the L1C uncertainty products produced by an offline processor named Radiometric Uncertainty Tool (RUT). This assessment is divided into three steps, namely noise propagation, interpolation uncertainty, and covariance impact. For the first of them, the results show that noise is reduced by a factor of 0.65 from L1B to L1C data, both by simulations and an empirical approach that estimates noise variance from real L1B and L1C acquisitions at different sites. Regarding the evaluation of the interpolation uncertainty, aerial orthoimages are convolved by the S2 Point Spread Function (PSF) and upsampled into the S2 spatial resolution. The study shows that, from this S2-like image, a distribution of interpolation errors (i.e. uncertainty) can be associated to the standard deviation of the neighbouring pixels. Finally, the covariance change due to the spatial resampling has been simulated with a propagation of the L1B radiance errors in order to understand the increase of correlation between neighbouring pixels
The Radiometric Calibration Network (RadCalNet, www.radcalnet.org) routinely provides top-of-atmosphere (TOA)
reflectance data from instrumented ground sites. The data represents the nadir view of the ground for different sites that
cover areas ranging from 50 m × 50 m to 1 km x 1 km. The smaller sites can only be used with high resolution sensors
(≤ 30 m), but the larger sites, such as Railroad Valley (RRV) in Nevada can also be used for the validation or vicarious
calibration of medium resolution sensors (> 250 m spatial resolution). Prior to utilising RadCalNet data in this manner,
this paper describes the application of a high and a medium resolution sensor to assess potential biases between the
RadCalNet data and satellite data at two different spatial resolutions. Results are shown for initial comparisons over
RRV for the high resolution Sentinel-2 MultiSpectral Instrument (S2-MSI) and the medium resolution Sentinel-3 Ocean
and Land Colour Instrument (S3-OLCI), and indicate the potential for RadCalNet to validate and vicariously calibrate
sensors with differing spatial resolutions. The comparison analysis includes taking into account the temporal differences
between the Sentinel-2 and Sentinel-3 overpasses and the time of RadCalNet data collection, as well as the spectral
response functions (SRF) of the bands for both instruments. The comparison against the RRV site has shown there are
significant biases between the RadCalNet data and S2-MSI and S3-OLCI for non-nadir viewing geometries that may be
due to directional viewing and illumination effects and the non-Lambertian character of the RadCalNet RRV site.
In the framework of the European Copernicus programme, the European Space Agency (ESA) has launched the Sentinel-2 (S2) Earth Observation (EO) mission which provides optical high spatial -resolution imagery over land and coastal areas. As part of this mission, a tool (named S2-RUT, from Sentinel-2 Radiometric Uncertainty Tool) estimates the radiometric uncertainties associated to each pixel using as input the top-of-atmosphere (TOA) reflectance factor images provided by ESA. The initial version of the tool has been implemented — code and user guide available1 — and integrated as part of the Sentinel Toolbox. The tool required the study of several radiometric uncertainty sources as well as the calculation and validation of the combined standard uncertainty in order to estimate the TOA reflectance factor uncertainty per pixel. Here we describe the recent research in order to accommodate novel uncertainty contributions to the TOA reflectance uncertainty estimates in future versions of the tool. The two contributions that we explore are the radiometric impact of the spectral knowledge and the uncertainty propagation of the resampling associated to the orthorectification process. The former is produced by the uncertainty associated to the spectral calibration as well as the spectral variations across the instrument focal plane and the instrument degradation. The latter results of the focal plane image propagation into the provided orthoimage. The uncertainty propagation depends on the radiance levels on the pixel neighbourhood and the pixel correlation in the temporal and spatial dimensions. Special effort has been made studying non-stable scenarios and the comparison with different interpolation methods.
In the framework of the European Union Copernicus programme, the European Space Agency (ESA) has launched the Sentinel-2 (S2) Earth Observation (EO) mission which provides optical high spatial resolution imagery. Here is presented a tool, S2-RUT, (Sentinel-2 Radiometric Uncertainty Tool) allowing estimation of the radiometric uncertainties associated to each pixel using as input the top-of-atmosphere (TOA) reflectance images provided by ESA. The Sentinel-2 radiometric analysis focuses on the review of the pre- and post-launch characterisations in order to specify the uncertainty contributors at a pixel level and allow changes to be proposed in the uncertainty contributors where necessary. The identified uncertainty contributors are combined using a metrological Guide to Expression of Uncertainty in Measurement’ (GUM) model that is validated by comparing the results to a multivariate Monte Carlo Method (MCM). Specific contributors of the TOA reflectance are initially characterised and its future integration in the tool is discussed. The software implementation of the S2-RUT tool relies on the flexibility of the JPEG2000 standard using partial decoding. Auxiliary information for the uncertainty calculation is extracted from the metadata and quality masks integrated in the L1C product. In addition, using the detector footprint mask it is possible to account for parameters dependent on the neighbouring pixels and/or detector module. The L1C uncertainty is coded using 1 byte with an extra optional byte for complementary information. The resulting images and the metadata are directly appended to the original L1C product.
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