Soluble iron nutrients in Saharan dust over the central Amazon rainforest

The intercontinental transport of aerosols from the Sahara desert plays a significant role in nutrient cycles in the Amazon rainforest, since it carries many types of minerals to these otherwise low-fertility lands. Iron is one of the micronutrients essential for plant growth, and its long-range transport might be an important source for the iron-limited Amazon rainforest. This study assesses the bioavailability of iron Fe(II) and Fe(III) in the particulate matter over the Amazon forest, which was transported from the Sahara desert (for the sake of our discussion, this term also includes the Sahel region). The sampling campaign was carried out above and below the forest canopy at the ATTO site (Amazon Tall Tower Observatory), a near-pristine area in the central Amazon Basin, from March to April 2015. Measurements reached peak concentrations for soluble Fe(III) (48 ng m−3), Fe(II) (16 ng m−3), Na (470 ng m−3), Ca (194 ng m−3), K (65 ng m−3), and Mg (89 ng m−3) during a time period of dust transport from the Sahara, as confirmed by ground-based and satellite remote sensing data and air mass backward trajectories. Dust sampled above the Amazon canopy included primary biological aerosols and other coarse particles up to 12 µm in diameter. Atmospheric transport of weathered Saharan dust, followed by surface deposition, resulted in substantial iron bioavailability across the rainforest canopy. The seasonal deposition of dust, rich in soluble iron, and other minerals is likely to assist both bacteria and fungi within the topsoil and on canopy surfaces, and especially benefit highly bioabsorbent species. In this scenario, Saharan dust can provide essential macronutrients and micronutrients to plant roots, and also directly to plant leaves. The influence of this input on the ecology of the forest canopy and topsoil is discussed, and we argue that this influence would likely be different from that of nutrients from the weathered Amazon bedrock, which otherwise provides the main source of soluble mineral nutrients

Land cover data of Upper Parana River Basin, South America, at high spatial resolution

This study presents a new land cover map for the Upper Paraná River Basin (UPRB-2015), with high spatial resolution (30 m), and a high number of calibration and validation sites. To the new map, 50 Landsat-8 scenes were classified with the Support Vector Machine (SVM) algorithm and their level of agreement was assessed using overall accuracy and Kappa coefficient. The generated map was compared by area and by pixel with six global products (MODIS, GlobCover, Globeland30, FROM-GLC, CCI-LC and, GLCNMO). The results of the new classification showed an overall accuracy ranging from 67% to 100%, depending on the sub-basin (80.0% for the entire UPRB). Kappa coefficient was observed ranging from 0.50 to 1.00 (average of 0.73 in the whole basin). Anthropic areas cover more than 70% of the entire UPRB in the new product, with Croplands covering 46.0%. The new mapped areas of croplands are consistent with local socio-economic statistics but don't agree with global products, especially FROM-GLC (14,9%), MODIS (33.8%), GlobCover (71.2%), and CCI (67.8%). In addition, all global products show generalized spatial disagreement, with some sub-basins showing areas of cropland varying by an order of magnitude, compared to UPRB-2015. In the case of Grassland, covering 25.6% of the UPRB, it was observed a strong underestimation by all global products. Even for the Globeland30 and MODIS, which show some significant fraction of pasture areas, there is a high level of disagreement in the spatial distribution. In terms of general agreement, the seven compared mappings (including the new map) agree in only 6.6% of the study area, predominantly areas of forest and agriculture. Finally, the new classification proposed in this study provides better inputs for regional studies, especially for those involving hydrological modeling as well as offers a more refined LU/LC data set for atmospheric numerical models