/pdf/remote-sensing-of-the-environment-4yfxjaaswe.pdf

Remote sensing of the environment

Thank you for reading electrodynamics of continuous media. Maybe you have knowledge that, people have look numerous times for their chosen books like this electrodynamics of continuous media, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some malicious bugs inside their computer. electrodynamics of continuous media is available in our book collection an online access to it is set as public so you can get it instantly. Our book servers saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the electrodynamics of continuous media is universally compatible with any devices to read.

Electrodynamics Of Continuous Media

SnowModel is a spatially distributed snow-evolution modeling system designed for application in landscapes, climates, and conditions where snow occurs. It is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind. Since each of these submodels was originally developed and tested for nonforested conditions, details describing modifications made to the submodels for forested areas are provided. SnowModel was created to run on grid increments of 1 to 200 m and temporal increments of 10 min to 1 day. It can also be applied using much larger grid increments, if the inherent loss in high-resolution (subgrid) information is acceptable. Simulated processes include snow accumulation; blowing-snow redistribution and sublimation; forest canopy interception, unloading, and sublimation; snow-density evolution; and snowp...

A Distributed Snow Evolution Modeling System (SnowModel)

The first issue of WRR appeared eight years after the launch of Sputnik, but by WRR's 25th anniversary, only seven papers that used remote sensing had appeared. Over the journal's second 25 years, that changed remarkably, and remote sensing is now widely used in hydrology and other geophysical sciences. We attribute this evolution to production of data sets that scientists not well versed in remote sensing can use, and to educational initiatives like NASA's Earth System Science Fellowship program that has supported over a thousand scientists, many in hydrology. We review progress in remote sensing in hydrology from a water balance perspective. We argue that progress is primarily attributable to a creative use of existing and past satellite sensors to estimate such variables as evapotranspiration rates or water storage in lakes and reservoirs and to new and planned missions. Recent transforming technologies include the Gravity Recovery and Climate Experiment (GRACE), the European Soil Moisture and Ocean Salinity (SMOS) and U.S. Soil Moisture Active Passive (SMAP) missions, and the Global Precipitation Measurement (GPM) mission. Future missions include Surface Water and Ocean Topography (SWOT) to measure river discharge and lake, reservoir, and wetland storage. Measurement of some important hydrologic variables remains problematic: retrieval of snow water equivalent (SWE) from space remains elusive especially in mountain areas, even though snow cover extent is well observed, and was the topic of 4 of the first 5 remote sensing papers published in WRR. We argue that this area deserves more strategic thinking from the hydrology community.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2015WR017616

Inroads of remote sensing into hydrologic science during the WRR era

Because heterogeneous materials can be designed to
exhibit the best characteristics of their individual
constituents, they are ideally suited to modern
technologies that require materials with an unusual
combination of properties. The study presented in
the current work is aimed to approach effective
electrical conductivity of various random
heterogeneous materials. It starts with the
description of a type of modern material with
specific morphology. Percolation theory and finite
element analysis are used in computations and
comparison with experimental results is provided. To
understand difficulties with classical approaches,
next a two-component composite material on a lattice
is studied, followed by discoveries in the
electrical field properties. Finally, a new
algorithm is suggested, which can be used to find
effective properties for any arbitrary morphology.
Material covered in this book will be of interest to
graduate students and researchers who work on the
front edge of physics, materials science,
engineering and applied mathematics. All necessary
derivations and definitions are included to make the
book self-contained and approachable.

Random Heterogeneous Materials

Precise measurements of snow structural parameters are crucial to understand the formation of snowpacks by deposition and metamorphism and to characterize the stratigraphy for many applications and remote sensing in particular. The area-wide acquisition of structural parameters at high spatial resolution from state-of-the-art methods is, however, still cumbersome, since the time required for a single profile is a serious practical limitation. As a remedy we have developed a statistical model to extract three major snow structural parameters: density, correlation length, and specific surface area (SSA) solely from the SnowMicroPen (SMP), a high-resolution penetrometer, which allows a meter profile to be measured with millimeter resolution in less than 1 min. The model was calibrated by combining SMP data with 3-D microstructural data from microcomputed tomography which was used to reconstruct full-depth snow profiles from different snow climates (Alpine, Arctic, and Antarctic). Density, correlation length, and SSA were derived from the SMP with a mean relative error of 10.6%, 16.4%, and 23.1%, respectively. For validation, we compared the density and SSA derived from the SMP to traditional measurements and near-infrared profiles. We demonstrate the potential of our method by the retrieval of a two-dimensional stratigraphy at Kohnen Station, Antarctica, from a 46 m long SMP transect. The result clearly reveals past depositional and metamorphic events, and our findings show that the SMP can be used as an objective, high-resolution tool to retrieve essential snow structural parameters efficiently in the field.

/pdf/density-specific-surface-area-and-correlation-length-of-snow-3tq4lueouj.pdf

Density, specific surface area, and correlation length of snow measured by high‐resolution penetrometry

. Density is a fundamental property of porous media such as snow. A wide range of snow properties and physical processes are linked to density, but few studies have addressed the uncertainty in snow density measurements. No study has yet quantitatively considered the recent advances in snow measurement methods such as micro-computed tomography (μCT) in alpine snow. During the MicroSnow Davos 2014 workshop, different approaches to measure snow density were applied in a controlled laboratory environment and in the field. Overall, the agreement between μCT and gravimetric methods (density cutters) was 5 to 9 %, with a bias of −5 to 2 %, expressed as percentage of the mean μCT density. In the field, density cutters overestimate (1 to 6 %) densities below and underestimate (1 to 6 %) densities above a threshold between 296 to 350 kg m−3, dependent on cutter type. Using the mean density per layer of all measurement methods applied in the field (μCT, box, wedge, and cylinder cutters) and ignoring ice layers, the variation between the methods was 2 to 5 % with a bias of −1 to 1 %. In general, our result suggests that snow densities measured by different methods agree within 9 %. However, the density profiles resolved by the measurement methods differed considerably. In particular, the millimeter-scale density variations revealed by the high-resolution μCT contrasted the thick layers with sharp boundaries introduced by the observer. In this respect, the unresolved variation, i.e., the density variation within a layer which is lost by lower resolution sampling or layer aggregation, is critical when snow density measurements are used in numerical simulations.

/pdf/intercomparison-of-snow-density-measurements-bias-precision-3y2ltlzp80.pdf

Intercomparison of snow density measurements: bias, precision, and vertical resolution

In this paper, we incorporate the cyclical terms in dense media radiative transfer (DMRT) theory to model combined active and passive microwave remote sensing of snow over the same scene. The inclusion of cyclical terms is crucial if the DMRT is used to model both the active and passive contributions with the same model parameters. This is a necessity when setting out on a joint active/passive retrieval. Previously, the DMRT model has been applied to active and passive separately, and in each case with a separate set of model parameters. The traditional DMRT theory only includes the ladder terms of the Feynman diagrams. The cyclical terms are important in multiple volume scattering and volume–surface interactions. This leads to backscattering enhancement which represents itself as a narrow peak centered at backward direction. This effect is of less significance in passive remote sensing since emissivity is relating to the angular integral of bistatic scattering coefficients. The inclusion of cyclical terms in first-order radiative transfer (RT) accounts for the enhancement of the double bounce contribution and makes the results the same as that of distorted Born approximation in volume–surface interactions. In this paper, we develop the methodology of cyclical corrections within the framework of DMRT beyond first order to all orders of multiple scattering. The active DMRT equation is solved using a numerical iterative approach followed by cyclical corrections. Both quasi-crystalline approximation (QCA)–Mie theory with sticky spheres and bicontinuous media scattering model are used to illustrate the results. The cyclical correlation introduces around 1 dB increase in backscatter with a moderate snowpack optical thickness of ${\sim}\text{0.2}$ . The bicontinuous/DMRT model is next applied to compare with data acquired in the Nordic Snow Radar Experiment (NoSREx) campaign in the snow season of 2010–2011. The model results are validated against coincidental active and passive measurements using the same set of physical parameters of snow in all frequency and polarization channels. Results show good agreement in multiple active and passive channels.

Modeling Both Active and Passive Microwave Remote Sensing of Snow Using Dense Media Radiative Transfer (DMRT) Theory With Multiple Scattering and Backscattering Enhancement

. The Microwave Emission Model of Layered Snowpacks (MEMLS) was originally developed for microwave emissions of snowpacks in the frequency range 5–100 GHz. It is based on six-flux theory to describe radiative transfer in snow including absorption, multiple volume scattering, radiation trapping due to internal reflection and a combination of coherent and incoherent superposition of reflections between horizontal layer interfaces. Here we introduce MEMLS3&a, an extension of MEMLS, which includes a backscatter model for active microwave remote sensing of snow. The reflectivity is decomposed into diffuse and specular components. Slight undulations of the snow surface are taken into account. The treatment of like- and cross-polarization is accomplished by an empirical splitting parameter q. MEMLS3&a (as well as MEMLS) is set up in a way that snow input parameters can be derived by objective measurement methods which avoid fitting procedures of the scattering efficiency of snow, required by several other models. For the validation of the model we have used a combination of active and passive measurements from the NoSREx (Nordic Snow Radar Experiment) campaign in Sodankyla, Finland. We find a reasonable agreement between the measurements and simulations, subject to uncertainties in hitherto unmeasured input parameters of the backscatter model. The model is written in Matlab and the code is publicly available for download through the following website: http://www.iapmw.unibe.ch/research/projects/snowtools/memls.html .

/pdf/memls3-a-microwave-emission-model-of-layered-snowpacks-4yuwktd9uq.pdf

MEMLS3&a: Microwave Emission Model of Layered Snowpacks adapted to include backscattering

. The snow microstructure, i.e., the spatial distribution of ice and pores, generally shows an anisotropy which is driven by gravity and temperature gradients and commonly determined from stereology or computer tomography. This structural anisotropy induces anisotropic mechanical, thermal, and dielectric properties. We present a method based on radio-wave birefringence to determine the depth-averaged, dielectric anisotropy of seasonal snow with radar instruments from space, air, or ground. For known snow depth and density, the birefringence allows determination of the dielectric anisotropy by measuring the copolar phase difference (CPD) between linearly polarized microwaves propagating obliquely through the snowpack. The dielectric and structural anisotropy are linked by Maxwell–Garnett-type mixing formulas. The anisotropy evolution of a natural snowpack in Northern Finland was observed over four winters (2009–2013) with the ground-based radar instrument "SnowScat". The radar measurements indicate horizontal structures for fresh snow and vertical structures in old snow which is confirmed by computer tomographic in situ measurements. The temporal evolution of the CPD agreed in ground-based data compared to space-borne measurements from the satellite TerraSAR-X. The presented dataset provides a valuable basis for the development of new snow metamorphism models which include the anisotropy of the snow microstructure.

/pdf/anisotropy-of-seasonal-snow-measured-by-polarimetric-phase-4ui9bweysb.pdf

Martin Proksch

Papers

Density, specific surface area, and correlation length of snow measured by high‐resolution penetrometry

Intercomparison of snow density measurements: bias, precision, and vertical resolution

Modeling Both Active and Passive Microwave Remote Sensing of Snow Using Dense Media Radiative Transfer (DMRT) Theory With Multiple Scattering and Backscattering Enhancement

MEMLS3&a: Microwave Emission Model of Layered Snowpacks adapted to include backscattering

Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series