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On the one hand, unemployment is known to have detrimental effects on individuals' mental health. On the other hand, poor mental health reduces re‐employment chances quite drastically, creating a vicious cycle. Active labour market policies (ALMPs) such as training programs or wage subsidies have been shown to ameliorate negative effects on mental health and improve labour market integration on average for the general unemployed population. In the context of individuals with severe mental health issues, however, it is unclear whether these interventions can be expected to deliver similar positive effects. In fact, one may argue that they have the potential to worsen employment prospects of individuals by adding additional stress to their pre‐existing mental health problems. Hence, this paper estimates the long‐term causal effects of ALMPs on the labour market integration of individuals with self‐reported severe mental health issues and compares estimates to individuals without such issues using unique combined survey and administrative data. Effects are estimated using the innovative double machine learning method and show that ALMPs do not only improve labour market integration of unemployed individuals with severe mental health issues, but they do so more effectively than for other unemployed individuals. [ABSTRACT FROM AUTHOR]

• 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]