Abstract
Earth Observation (EO) via remote sensing is rapidly growing in terms of satellite missions, complexity of applications and number of datasets. This situation demands that data has associated with it a quality indicator that describes the compatibility between different sensor data and suitability for particular applications. This work describes a full end-to-end analysis of the uncertainty at a pixel level of the Top-Of-Atmosphere (TOA) radiance/reflectance factor products. It develops a methodology framework that can be adapted and reproduced by several EO missions to provide TOA radiometric uncertainty. The method is not only described but implemented as a software tool named Radiometric Uncertainty Tool (RUT) using as an example the Sentinel-2 (S2) mission. The uncertainty methodology starts from a radiometric model, where a set of uncertainty contributors are identified and specified at a pixel level, by reviewing the pre- and post-launch sensor radiometric characterisations. These contributors are assessed using the metadata and quality information associated to the satellite products where possible. As a consequence, the uncertainty contributions are specified for the specific satellite acquisition time, scene and processing. Some of the uncertainty contributions required the use of novel estimation methods that have been specifically applied to the assessment of the uncertainty propagation produced by the image orthorectification and the radiometric impact of the spectral knowledge. The study proposes an uncertainty combination model with an important effort in using the best metrological practices as described in the ‘Guide to Expression of Uncertainty in Measurement’ (GUM) model. The assumptions in the model have been validated by comparing the results to a Monte Carlo Method (MCM), the correlation among the different uncertainty contributions has been studied, and the impact of simplifications in the combination model has been assessed. As an extension of the work towards its larger application, a methodology has been proposed and implemented to estimate the uncertainty associated to the mean of the pixels in a Region of Interest (ROI). The study considers the correlation of the pixels in the spatial, temporal and spectral dimension. As a result, the TOA radiometric uncertainty estimates can be of direct use for applications as the radiometric validation activities or product spatial binning. Further extension of the uncertainty concepts has resulted in a set of tools, algorithms and methodologies that have been used in order to estimate the radiometric uncertainty achievable for an indicative target sensor through in-flight cross-calibration using a well-calibrated hyperspectral SI-traceable reference sensor with observational characteristics such as TRUTHS (Traceable Radiometry Underpinning Terrestrial and Helio-Studies) mission. This study considers the criticality of the instrumental and observational characteristics on pixel level reflectance factors, within a defined spatial ROI within the target site. It quantifies the main uncertainty contributors in the spectral, spatial, and temporal dimension.