Large-Scale Inverse Microwave Backscatter Modeling of Sea Ice

Polar sea ice characteristic provide important inputs to models of several geophysical processes. Many forward electromagnetic scattering models have been proposed to predict the normalized radar cross section, oo, from sea ice characteristics. These models are based on very small scale ice features and generally assume that the region of interest is spatially homogeneous. Unfortunately, spaceborne scatterometer footprints are very large (5-50 km) and usually contain very heterogeneous mixtures of sea ice surface parameters. In this paper, we apply scatterometer data to large scale inverse modeling. Given the limited resolution, we adopt a simple geometric optics forward scattering model to analyze surface and volume scattering contributions to observed Ku-band signatures. A model inversion technique based on recursive optimization of an objective function is developed. Simulations demonstrate the performance of the method in the presence of noise. The inverse model is implemented using Ku-band image reconstructed data collected by the NASA scatterometer. The results are used to analyze and interpret a oo phenomenon occurring in the Arctic

Master of Science

thesisRecent accelerated mass loss offset by increased Arctic precipitation highlights the importance of a comprehensive understanding of the mechanisms controlling mass balance on the Greenland ice sheet. Knowledge of the spatiotemporal variability of snow accumulation is critical to accurately quantify mass balance, yet, considerable uncertainty remains in current snow accumulation estimates. Previous studies have shown the potential for large-scale retrievals of snow accumulation rates in regions that experience seasonal melt-refreeze metamorphosis using active microwave remote sensing. Theoretical backscatter models used in these studies to validate the hypothesis that observed decreasing freezing season backscatter signatures are linked to snow accumulation rates suggest the relationship is inverse and linear (dB). The net backscatter measurement is dominated by a Mie scattering response from the underlying ice-facie. Two-way attenuation resulting from a Raleigh scattering response within the overlying layer of snow accumulation forces a decrease in the backscatter measurement over time with increased snow accumulation rates. Backscatter measurements acquired from NASA's Ku-band SeaWinds scatterometer on the QuikSCAT satellite together with spatially calibrated snow accumulation rates acquired from the Polar MM5 mesoscale climate model are used to evaluate this relationship. Regions that experienced seasonal melt-refreeze metamorphosis and potentially formed dominant scattering layers are delineated, iv freeze-up and melt-onset dates identifying the freezing season are detected on a pixel-by-pixel basis, freezing season backscatter time series are linearly regressed, and a microwave snow accumulation metric is retrieved. A simple empirical relationship between the retrieved microwave snow accumulation metric (dB), , and spatially calibrated Polar MM5 snow accumulation rates (m w. e.), , is derived with a negative correlation coefficient of R=-.82 and a least squares linear fit equation of . Results indicate that an inverse relationship exists between decreasing freezing season backscatter decreases and snow accumulation rates; however, this technique fails to retrieve accurate snow accumulation estimates. An alternate geometric relationship is suggested between decreasing freezing season backscatter signatures, snow accumulation rates, and snowpack stratigraphy in the underlying ice-facie, which significantly influences the microwave scattering mechanism. To understand this complex relationship, additional research is required

Microwave backscatter modeling of erg surfaces in the Sahara Desert

The Sahara Desert includes large expanses of sand dunes called ergs. These dunes are formed and constantly reshaped by prevailing winds. Previous study shows that Saharan ergs exhibit significant radar backscatter (σ°) modulation with azimuth angle (f). We use σ° measurements observed at various incidence angles and f from the NASA Scatterometer (NSCAT), the SeaWinds scatterometer, the ERS scatterometer (ESCAT), and the Tropical Rainfall Measuring Mission\u27s Precipitation Radar to model the σ° response from sand dunes. Observations reveal a characteristic relationship between the backscatter modulation and the dune type, i.e., the number and orientation of the dune slopes. Sand dunes are modeled as a composite of tilted rough facets, which are characterized by a probability distribution of tilt with a mean value, and small ripples on the facet surface. The small ripples are modeled as cosinusoidal surface waves that contribute to the return signal at Bragg angles only. Longitudinal and transverse dunes are modeled with rough facets having Gaussian tilt distributions. The model results in a σ° response similar to NSCAT and ESCAT observations over areas of known dune types in the Sahara. The response is high at look angles equal to the mean tilts of the rough facets and is lower elsewhere. This analysis provides a unique insight into scattering by large-scale sand bedforms

Review Article: Global Monitoring of Snow Water Equivalent Using High-Frequency Radar Remote Sensing

Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth\u27s surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth\u27s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world\u27s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth\u27s cold regions\u27 ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements

Antarctic surface melting dynamics : enhanced perspectives from radar scatterometer data

Author Posting. © American Geophysical Union, 2012. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 117 (2012): F02023, doi:10.1029/2011JF002126.Antarctic ice sheet surface melting can regionally influence ice shelf stability, mass balance, and glacier dynamics, in addition to modulating near-surface physical and chemical properties over wide areas. Here, we investigate variability in surface melting from 1999 to 2009 using radar backscatter time series from the SeaWinds scatterometer aboard the QuikSCAT satellite. These daily, continent-wide observations are explored in concert with in situ meteorological records to validate a threshold-based melt detection method. Radar backscatter decreases during melting are significantly correlated with in situ positive degree-days as well as meltwater production determined from energy balance modeling at Neumayer Station, East Antarctica. These results support the use of scatterometer data as a diagnostic indicator of melt intensity (i.e., the relative liquid water production during melting). Greater spatial and temporal melting detected relative to previous passive microwave-based studies is attributed to a higher sensitivity of the scatterometer instrument. Continental melt intensity variability can be explained in part by the dynamics of the Southern Annular Mode and the Southern Oscillation Index, and extreme melting events across the Ross Ice Shelf region may be associated with El Niño conditions. Furthermore, we find that the Antarctic Peninsula accounts for only 20% of Antarctic melt extent but greater than 50% of the total Antarctic melt intensity. Over most areas, annual melt duration and intensity are proportional. However, regional and localized distinctions exist where the melt intensity metric provides greater insight into melting dynamics than previously obtainable with other remote sensing techniques.Support for this research was provided by NASA grant NNX10AP09G and NSF grant ANT-063203.2012-11-1

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

Development and Evaluation of a Multi-Year Fractional Surface Water Data Set Derived from Active/Passive Microwave Remote Sensing Data

abstract: The sensitivity of Earth’s wetlands to observed shifts in global precipitation and temperature patterns and their ability to produce large quantities of methane gas are key global change questions. We present a microwave satellite-based approach for mapping fractional surface water (FW) globally at 25-km resolution. The approach employs a land cover-supported, atmospherically-corrected dynamic mixture model applied to 20+ years (1992–2013) of combined, daily, passive/active microwave remote sensing data. The resulting product, known as Surface WAter Microwave Product Series (SWAMPS), shows strong microwave sensitivity to sub-grid scale open water and inundated wetlands comprising open plant canopies. SWAMPS’ FW compares favorably (R[superscript 2] = 91%–94%) with higher-resolution, global-scale maps of open water from MODIS and SRTM-MOD44W. Correspondence of SWAMPS with open water and wetland products from satellite SAR in Alaska and the Amazon deteriorates when exposed wetlands or inundated forests captured by the SAR products were added to the open water fraction reflecting SWAMPS’ inability to detect water underneath the soil surface or beneath closed forest canopies. Except for a brief period of drying during the first 4 years of observation, the inundation extent for the global domain excluding the coast was largely stable. Regionally, inundation in North America is advancing while inundation is on the retreat in Tropical Africa and North Eurasia. SWAMPS provides a consistent and long-term global record of daily FW dynamics, with documented accuracies suitable for hydrologic assessment and global change-related investigations.The final version of this article, as published in Remote Sensing, can be viewed online at: http://www.mdpi.com/2072-4292/7/12/1584

Theoretical modeling of dual-frequency scatterometer response: improving ocean wind and rainfall effects

Ocean surface wind is a key parameter of the Earth’s climate system. Occurring at the interface between the ocean and the atmosphere, ocean winds modulate fluxes of heat, moisture and gas exchanges. They reflect the lower branch of the atmospheric circulation and represent a major driver of the ocean circulation. Studying the long-term trends and variability of the ocean surface winds is of key importance in our effort to understand the Earth’s climate system and the causes of its changes. More than three decades of surface wind data are available from spaceborne scatterometer/radiometer missions and there is an ongoing effort to inter-calibrate all these measurements with the aim of building a complete and continuous picture of the ocean wind variability. Currently, spaceborne scatterometer wind retrievals are obtained by inversion algorithms of empirical Geophysical Model Functions (GMFs), which represent the relationship between ocean surface backscattering coefficient and the wind parameters. However, by being measurement-dependent, the GMFs are sensor-specific and, in addition, they may be not properly defined in all weather conditions. This may reduce the accuracy of the wind retrievals in presence of rain and it may also lead to inconsistencies amongst winds retrieved by different sensors. Theoretical models of ocean backscatter have the big potential of providing a more general and understandable relation between the measured microwave backscatter and the surface wind field than empirical models. Therefore, the goal of our research is to understand and address the limitations of the theoretical modeling, in order to propose a new strategy towards the definition of a unified theoretical model able to account for the effects of both wind and rain. In this work, it is described our approach to improve the theoretical modeling of the ocean response, starting from the Ku-band (13.4 GHz) frequency and then broadening the analysis at C-band (5.3 GHz) frequency. This research has revealed the need for new understanding of the frequency-dependent modeling of the surface backscatter in response to the wind-forced surface wave spectrum. Moreover, our ocean wave spectrum modification introduced to include the influences of the surface rain, allows the interpretation/investigation of the scatterometer observations in terms not only of the surface winds but also of the surface rain, defining an additional step needed to improve the wind retrievals algorithms as well as the possibility to jointly estimate wind and rain from scatterometer observations

Radar sounding using the Cassini altimeter waveform modeling and Monte Carlo approach for data inversion observations of Titan's seas

Recently, the Cassini RADAR has been used as a sounder to probe the depth and constrain the composition of hydrocarbon seas on Saturn's largest moon, Titan. Altimetry waveforms from observations over the seas are generally composed of two main reflections: the first from the surface of the liquid and the second from the seafloor. The time interval between these two peaks is a measure of sea depth, and the attenuation from the propagation through the liquid is a measure of the dielectric properties, which is a sensitive property of liquid composition. Radar measurements are affected by uncertainties that can include saturation effects, possible receiver distortion, and processing artifacts, in addition to thermal noise and speckle. To rigorously treat these problems, we simulate the Ku-band altimetry echo received from Titan's seas using a two-layer model, where the surface is represented by a specular reflection and the seafloor is modeled using a facet-based synthetic surface. The simulation accounts for the thermal noise, speckle, analog-to-digital conversion, and block adaptive quantization and allows for possible receiver saturation. We use a Monte Carlo method to compare simulated and observed waveforms and retrieve the probability distributions of depth, surface/subsurface intensity ratio, and subsurface roughness for the individual double-peaked waveform of Ligeia Mare acquired by the Cassini spacecraft in May 2013. This new analysis provides an update to the Ku-band attenuation and results in a new estimate for its loss tangent and composition. We also demonstrate the ability to retrieve bathymetric information from saturated altimetry echoes acquired over Ontario Lacus in December 2008

Fundamental remote sensing science research program. Part 1: Scene radiation and atmospheric effects characterization project

Brief articles summarizing the status of research in the scene radiation and atmospheric effect characterization (SRAEC) project are presented. Research conducted within the SRAEC program is focused on the development of empirical characterizations and mathematical process models which relate the electromagnetic energy reflected or emitted from a scene to the biophysical parameters of interest