Time series of optical imagery allow to derive land surface phenology metrics. These metrics are only complete with a statement about their uncertainty. A source of uncertainty is the radiometry of the sensor. We propagated radiometric uncertainties within a Monte-Carlo framework into phenological metrics using the TIMESAT approach based on time series of the Normalized Difference Vegetation Index (NDVI), three-band Enhanced Vegetation Index (EVI) and Green Leaf Area Index (GLAI) derived from radiative transfer modelling. Additionally, we studied the effect of propagated uncertainties on scene pre-classification. We focused on Sentinel-2 MSI TOA data since quantitative estimates of radiometric uncertainties are available. Propagation was carried out for a growing season over an agricultural region in Switzerland. Propagated uncertainties had little impact on the classification except for spectrally mixed pixels. Effects on the spectral indices and GLAI were more pronounced. In detail, GLAI was more uncertain due to the ill-posedness of radiative transfer model inversion (median relative uncertainty for all crop pixels and Sentinel-2 scenes: 4.4%) than EVI (2.7%) and NDVI (1.1%). Regarding phenology, metrics exhibited largest uncertainties in the case of GLAI. The magnitude of uncertainty in the metrics depends on the inter-scene error correlation, which we assumed to be either zero (uncorrelated) or one (fully correlated) since the actual correlation is unknown. If uncertainties are fully correlated, uncertainties in metrics are small (2 to 3 days) but take values up to greater 10 days under the uncorrelated assumption. Thus, our work provides guidance for the interpretation of phenological metrics.

The Copernicus Sentinel-2 (S2) satellite mission acquires high spatial resolution optical imagery over land and coastal areas. Delivering uncertainty estimates and spectral error correlation alongside S2 data products facilitates the constrain of retrieval algorithms, propagates further downstream the retrieval uncertainty, and finally makes informed decisions to end-users. This study presents a framework to produce uncertainty estimates and spectral error correlation associated to the S2 L2A data products (i.e. surface reflectance). This framework has been implemented in a prototype code available at [1]. The uncertainty considers both the Level-1 (L1) uncertainty estimates for top-of-atmosphere (TOA) reflectance factor and the atmospheric correction. The L2A error distribution cannot be systematically described as a normal distribution, the transformation can be non-linear and without an explicit mathematical model. Thus, a Multivariate MonteCarlo model (MCM) rather than the law of propagation of uncertainty (LPU) is selected for uncertainty propagation. We show results for surface reflectance uncertainty over the Amazon forest and Libya4 desert site. It illustrates the large uncertainty and spectral error correlation variations depending on the scene. The comparison of an multivariate MCM against an LPU propagation methodology indicate the limitations of the latter for scenes dominated by the atmospheric path. Its implementation as an operational per-pixel processing and dissemination of both the uncertainty and spectral error correlation becomes challenging. Therefore, this methodology is not expected to run at an operational level but serve as the basis to define a strategy for an operational one.

Digital terrain models (DTMs) are a fundamental source of information in Earth sciences. DTM-based studies, however, can contain remarkable biases if limitations and inaccuracies in these models are disregarded. In this work, four freely available datasets, including Shuttle Radar Topography Mission C-Band Synthetic Aperture Radar (SRTM C-SAR V3 DEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Map (ASTER GDEM V2), and two nationwide airborne light detection and ranging (LiDAR)-derived DTMs (at 5-m and 1-m spatial resolution, respectively) were analysed in three geomorphologically contrasting, small (3–5 km²) catchments located in Mediterranean landscapes under intensive human influence (Mallorca Island, Spain). Vertical accuracy as well as the influence of each dataset’s characteristics on hydrological and geomorphological modelling applicability were assessed by using ground-truth data, classic geometric and morphometric parameters, and a recently proposed index of sediment connectivity. Overall vertical accuracy—expressed as the root mean squared error (RMSE) and normalised median deviation (NMAD)—revealed the highest accuracy for the 1-m (RMSE = 1.55 m; NMAD = 0.44 m) and 5-m LiDAR DTMs (RMSE = 1.73 m; NMAD = 0.84 m). Vertical accuracy of the SRTM data was lower (RMSE = 6.98 m; NMAD = 5.27 m), but considerably higher than for the ASTER data (RMSE = 16.10 m; NMAD = 11.23 m). All datasets were affected by systematic distortions. Propagation of these errors and coarse horizontal resolution caused negative impacts on flow routing, stream network, and catchment delineation, and to a lower extent, on the distribution of slope values. These limitations should be carefully considered when applying DTMs for catchment hydrogeomorphological modelling.