A major challenge of the Sustainable Development Goals linked to Agriculture, Food Security, and Nutrition, under the current global crop production paradigm, is that increasing crop yields often have negative environmental impacts. It is therefore urgent to develop and adopt optimal soil-improving cropping systems (SICS) that can allow us to decouple these system parameters. Soil salinization is a major environmental hazard that limits agricultural potential and is closely linked to agricultural mismanagement and water resources overexploitation, especially in arid climates. Here we review literature seeking to ameliorate the negative effect of soil salinization on crop productivity and conduct a global meta-analysis of 128 paired soil quality and yield observations from 30 studies. In this regard, we compared the effectivity of different SICS that aim to cope with soil salinization across 11 countries, in order to reveal those that are the most promising. The analysis shows that besides case-specific optimization of irrigation and drainage management, combinations of soil amendments, conditioners, and residue management can contribute to significant reductions of soil salinity while significantly increasing crop yields. These results highlight that conservation agriculture can also achieve the higher yields required for upscaling and sustaining crop production.

A methodology for elaborating multi-temporal Sentinel-1 and Landsat 8 satellite images for estimating topsoil Soil Moisture Content (SMC) to support hydrological simulation studies is proposed. After pre-processing the remote sensing data, backscattering coefficient, Normalized Difference Vegetation Index (NDVI), thermal infrared temperature and incidence angle parameters are assessed for their potential to infer ground measurements of SMC, collected at the top 5 cm. A non-linear approach using Artificial Neural Networks (ANNs) is tested. The methodology is applied in Western Crete, Greece, where a SMC gauge network was deployed during 2015. The performance of the proposed algorithm is evaluated using leave-one-out cross validation and sensitivity analysis. ANNs prove to be the most efficient in SMC estimation yielding R² values between 0.7 and 0.9. The proposed methodology is used to support a hydrological simulation with the HEC-HMS model, applied at the Keramianos basin which is ungauged for SMC. Results and model sensitivity highlight the contribution of combining Sentinel-1 SAR and Landsat 8 images for improving SMC estimates and supporting hydrological studies.

The development of high spatial resolution digital elevation models takes place via the use of GeoEye-1 stereo-pair imagery, providing highly accurate geometrical representations of complex riverine systems. The combination of geographic information systems with hydraulic models facilitates the exploitation of satellite topographic information throughout the cross-section extraction process. One-dimensional HEC-RAS and combined 1D/2D HEC-RAS models are adjusted by making use of the resulting high-resolution input. Several hydraulic simulations are effectuated in order to test how significantly DEM resolution affects hydraulic modelling results, with regard also to the model dimensionality. The ability of the combined 1D/2D model, based mainly on the high-accuracy input data, provides an accurate estimate of the flood hazard area. Flood-prone areas could take advantage of high-accuracy results and facilitate the effective management of extreme events and sufficient decision making.