Sea surface temperature (SST) is a fundamental physical variable for understanding, quantifying and predicting complex interactions between the ocean and the atmosphere. Such processes determine how heat from the sun is redistributed across the global oceans, directly impacting large-and small-scale weather and climate patterns. The provision of daily maps of global SST for operational systems, climate modeling and the broader scientific community is now a mature and sustained service coordinated by the Group for High Resolution Sea Surface Temperature (GHRSST) and the CEOS SST Virtual Constellation (CEOS SST-VC). Data streams are shared, indexed, processed, quality controlled, analyzed, and documented within a Regional/Global Task Sharing (R/GTS) framework, which is implemented internationally in a distributed manner. Products rely on a combination of low-Earth orbit infrared and microwave satellite imagery, geostationary orbit infrared satellite imagery, and in situ data from moored and drifting buoys, Argo floats, and a suite of independent, fully characterized and traceable in situ measurements for product validation (Fiducial Reference Measurements, FRM). Research and development continues to tackle problems such as instrument calibration, algorithm development, diurnal variability, derivation of high-quality skin and depth temperatures, and areas of specific interest such as the high latitudes and coastal areas. In this white paper, we review progress versus the challenges we set out 10 years ago in a previous paper, highlight remaining and new research and development challenges for the next 10 years

Assessing landform vulnerability to soil erosion is crucial for improved sustainable land use planning and management. In the Loess Plateau of the Northern Shaanxi Province of China, soil erosion has been reported as a major threat to sustainable land management and impacts on driving the socio-economic benefits that can be accrued from the landforms. Several studies especially on Erosion Potential Mapping (EPM) in the region have been conducted but the role of the fractal dimension (FD) of the terrain features has been limited. In this study, the paper assessed the role of fractal terrain features on the overall EPM. The Analytical Hierarchical Process (AHP) was adopted using 6 criteria, FD of the terrain, Land Use Land Cover, Slope, Elevation, Geomorphology and Flow Accumulation. These were developed in a Geographic Information System (GIS) framework. Eight Scales (8) were evaluated in order to select the best Scale with the lowest Consistency Ratio (CR) and the Minimum Relative Error (MRE). The results from this study shows that fractal features of terrain when integrated with the rest of the criteria produced a reliable EPM for the study area. The absence of the FD also gives unrealistic results for the EPM. The EPM with FD distribution recorded 29.4% for low erosion potential whereas EPM without FD recorded 46.7%. A larger portion of the Shaanxi province (70%) is found to be at a higher risk of erosion. Therefore, it is hoped that the findings from this research would further boost the integration of fractals into EPM in China and similar regions across the World. The study further recommends that sustainable soil management measures are put in place to reduce the erosion risk in the province to protect the natural ecological habitat.

No abstract

No abstract