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• Soil reflectance composites were generated with Sentinel-2 L2A data. • A multi-channel CNN learning framework for soil organic carbon prediction is proposed. • A dual input deep learning architecture improves the predictions. • The framework produced results that can be interpreted. The Copernicus Sentinel-2 multispectral imagery data may be aggregated to extract large-scale, bare soil, reflectance composites, which enable soil mapping applications. In this paper, this approach was tested in the German federal state of Bavaria, to provide estimations for soil organic carbon (SOC). Different temporal ranges were considered for the generation of the composites, including multi-annual and seasonal ranges. A novel multi-channel convolutional neural network (CNN) is proposed. By leveraging the advantages of deep learning techniques, it utilizes complementary information from different spectral pre-treatment techniques. The SOC predictions indicated little dissimilarity amongst the different composites, with the best performance attained for the six-year composite containing only spring months (RMSE = 12.03 g C · kg−1, R2 = 0.64, RPIQ = 0.89). It has been demonstrated that these outcomes outperform other well-known machine learning techniques. An ablation analysis was accordingly performed to evaluate the interplay of the CNN's different components to disentangle the advantages of each aspect of the proposed framework. Finally, a DUal inPut deep LearnIng architecture, named DUPLICITE, is proposed, which concatenates deep spectral features derived from the CNN mentioned earlier, as well as topographical and environmental covariates through an artificial neural network (ANN) to exploit their complementarity. The proposed approach was demonstrated to provide an improvement in the overall prediction performance (RMSE = 11.60 gC · kg−1, R2 = 0.67, RPIQ = 0.92). [ABSTRACT FROM AUTHOR]

Vb cyclones are major drivers of extreme precipitation and floods in the study area of hydrological Bavaria (Germany). When assessing climate change impacts on Vb cyclones, internal variability of the climate system is an important underlying uncertainty. Here, we employ a 50‐member single‐model initial‐condition large ensemble of a regional climate model to study climate variability and forced change on Vb cyclones. An artificial neural network detects cutoff lows over central Europe, which are associated with extreme precipitation Vb cyclones. Thus, machine learning filters the large ensemble prior to cyclone tracking. Our results show a striking change in Vb seasonality with a strong decrease of Vb cyclones in summer (−52%) and a large increase in spring (+73%) under the Representative Concentration Pathway 8.5. This change exceeds the noise of internal variability and leads to a peak shift from summer to spring. Additionally, we show significant increases in the daily precipitation intensity during Vb cyclones in all seasons. Plain Language Summary: Bavaria, a state in the southeast of Germany, has been hit by several devastating floods in recent decades triggered by a storm type called Vb. For future flood risk in Bavaria it is crucial to understand how climate change affects Vb storms. This study uses high‐resolution climate simulations over Europe to study changes in the frequency of Vb storms, their seasonal occurrence, and their rainfall intensity under a high greenhouse gas concentration scenario. However, Vb storms are rare events and a single simulation may not provide enough events to distinguish between climate change and random, natural variations. Therefore, we employ a large database of 50 climate simulations with the same settings and greenhouse gas concentration scenario, but slightly different starting conditions, in order to robustly estimate climate change effects on Vb storms. The drawback of using 50 simulations is the high amount of data. Therefore, we apply machine learning for pattern recognition to detect the low‐pressure systems related to extreme precipitation Vb storms in the climate simulations. Our results show that climate change considerably affects the seasonal occurrence of Vb storms with a shift from summer to spring. Furthermore, the daily rainfall intensity in Bavaria increases during Vb storms significantly with climate change. Key Points: Machine learning is employed to filter a single‐model ensemble for cutoff low related Vb cyclones with high accuracyThe seasonality of Vb cyclones changes considerably under the RCP8.5 scenario with a peak shift from summer to springThe daily precipitation intensity of Vb cyclones significantly increases in all seasons [ABSTRACT FROM AUTHOR]