A large portion of the human population lives in dryland areas, and these biomes also offer a variety of ecosystem services to the local people. In spite of their importance, systematically mapping and characterizing them has been somewhat neglected by the remote sensing community. This is in part due to their bio-complexity and the diffuse scattering of satellite signals in open and sparsely-vegetated areas. Moreover, drylands exhibit large temporal dynamics because of their dependence on rainfall and are, thus, very sensitive to climate variability and human-driven land degradation. Capturing these intricate spatial and temporal land change processes requires multi-source and multi-scale data sets and fusion algorithms that intelligently integrate in situ data, remote sensing observations and modelling results. To reflect their intra-annual and inter-annual variations, the use of well-processed time series data is imperative. Specifically, monitoring dryland phenology from space plays an important role in assessing the anthropogenic pressures and drivers in drylands. Further combining remote sensing with process-based models offer the opportunity to unravel land change effects and consequences in drylands.
This Special Issue, therefore, calls for manuscripts that deal with assessing environmental issues in drylands using multi-scale and multi-source data in an integrated way. Specifically, manuscripts are encouraged that illustrate the possibilities of how multi-source data sets in terms of better dealing with land degradation in drylands, invasive species encroachment and land management issues and policy and decision support.