Abstract:The enormous increase of remote sensing data from airborne and space-borne platforms, as well as ground measurements has directed the attention of scientists towards new and efficient retrieval methodologies. Of particular importance is the consideration of the large extent and the high dimensionality (spectral, temporal and spatial) of remote sensing data. Moreover, the launch of the Sentinel satellite family will increase the availability of data, especially in the temporal domain, at no cost to the users. To analyze these data and to extract relevant features, such as essential climate variables (ECV), specific methodologies need to be exploited. Among these, greater attention is devoted to machine learning methods due to their flexibility and the capability to process large number of inputs and to handle non-linear problems. The main objective of this paper is to provide a review of research that is being carried out to retrieve two critically important terrestrial biophysical quantities (vegetation biomass and soil moisture) from remote sensing data using machine learning methods.
Atmospheric constituents may be deposited to and incorporated into plant leaves, with gases entering via stomata, and gas and particles being sorbed at the surface and in some cases traversing the cuticle, possibly reaching the epidermis. Plants are known to be a sink for atmospheric mercury (Hg), and the current paradigm is that uptake of gaseous elemental Hg occurs by way of the stomata. Four plant species, Rudbeckia hirta, Sorghastrum nutans, Andropogon gerardii, and Populus tremuloides, were exposed to air from different sources and with different Hg and CO2 concentrations in light and dark within a gas exchange chamber at approximately 25% relative humidity. Data showed that Hg concentration and air source had a significant effect (p < 0.001) on leaf-atmosphere Hg flux, with more deposition to the leaf occurring in elevated-Hg air, and in scrubbed air compared to ambient air. Deposition also occurred during dark and elevated CO2 exposures, when stomatal conductance was reduced. These observations and the fact that limited or no Hg emission occurred after deposition of atmospheric Hg suggests that the nonstomatal pathway may be an important route of foliar accumulation of atmospheric Hg.
This paper presents data from experiments that measured Mercury (Hg) flux as a function of water addition and subsequent soil drying, and maintenance of soil water content over time utilizing small dynamic gas exchange chambers and large mesocosms. When soil surfaces were dry and water was added at an amount less than that necessary to saturate the soil an immediate large (relative to dry soil flux) release of Hg occurred. Diel Hg emissions from soils, unenriched (0.02 lg g À1 ) and enriched (3 lg g À1 ) in Hg and wet below saturation, were significantly elevated above that occurring from dry soils (2-5 times depending on soil water content) for weeks to months. Enhancement of emissions from wet soils in direct sunlight were greater than that from soils shaded or in the dark suggesting that a synergism exists between soil moisture and light. When soils were watered to saturation Hg emissions were suppressed or remained the same depending on the degree of saturation. It is hypothesized that the addition of soil water in amounts less than that necessary to saturate the soil surface results in an immediate release of elemental Hg from soil surface as the more polar water molecule out competes Hg for binding sites. As the water moves into the soil, Hg adsorbed to soil particles is desorbed into soil gas and dissolved in the soil water. The process of evaporation facilitates movement of Hg as mass flow to the soil surface where it is made available for subsequent release. The latter is hypothesized to be an important process by which Hg is recharged at the soil-air interface.
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