Comparison of dissolved and particulate arsenic distributions in shallow aquifers of Chakdaha, India, and Araihazar, Bangladesh

Background  

The origin of the spatial variability of dissolved As concentrations in shallow aquifers of the Bengal Basin remains poorly understood. To address this, we compare here transects of simultaneously-collected groundwater and aquifer solids perpendicular to the banks of the Hooghly River in Chakdaha, India, and the Old Brahmaputra River in Araihazar, Bangladesh.     

Observations of the sodium emission from Jupiter,region C

Measurements made when testing an interferometric system for imaging the sodium emission indicate that, between 3 and 7.5 arc min (9 and 23R_J) from Jupiter, the observed emissions can probably be attributed to terrestrial airglow.     

Evidence linking coronal transients to the evolution of coronal holes

The positions of X-ray coronal transients outside of active regions observed during Skylab were superposed on H synoptic charts and coronal hole boundaries for seven solar rotations. We confirmed a detailed spatial association between the transients and neutral lines. We found that most of the transients were related to large-scale changes in coronal hole area and tended to occur on the borders of evolving equatorial holes.Skylab Solar Workshop Post-Doctoral Appointee, 1975–1977.     

Landsat-8 vs. Sentinel-2: examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso

The availability of freely available moderate-to-high spatial resolution (10–30 m) satellite imagery received a major boost with the recent launch of the Sentinel-2 sensor by the European Space Agency. Together with Landsat, these sensors provide the scientific community with a wide range of spatial, spectral, and temporal properties. This study compared and explored the synergistic use of Landsat-8 and Sentinel-2 data in mapping land use and land cover (LULC) in rural Burkina Faso. Specifically, contribution of the red-edge bands of Sentinel-2 in improving LULC mapping was examined. Three machine-learning algorithms – random forest, stochastic gradient boosting, and support vector machines – were employed to classify different data configurations. Classification of all Sentinel-2 bands as well as Sentinel-2 bands common to Landsat-8 produced an overall accuracy, that is 5% and 4% better than Landsat-8. The combination of Landsat-8 and Sentinel-2 red-edge bands resulted in a 4% accuracy improvement over that of Landsat-8. It was found that classification of the Sentinel-2 red-edge bands alone produced better and comparable results to Landsat-8 and the other Sentinel-2 bands, respectively. Results of this study demonstrate the added value of the Sentinel-2 red-edge bands and encourage multi-sensoral approaches to LULC mapping in West Africa.