Preface 1. Introduction 2. Electromagnetic waves in free space 3. Interaction of electromagnetic radiation with matter 4. Interaction of electromagnetic radiation with the Earth's atmosphere 5. Photographic systems 6. Electro-optical systems 7. Passive microwave systems 8. Ranging systems 9. Scattering systems 10. Platforms for remote sensing 11. Data processing Appendix 1. The Global Positioning System Appendix 2. Data Bibliography Answers and hints to numerical problems Index.

Physical Principles of Remote Sensing

The scatterometers on board the European remote sensing (ERS) satellites have proven their usefulness for weather analyses and forecasting, and operational programmes are being developed to provide routine scatterometer observations. The advanced scatterometer (ASCAT) on board the meteorological operational (MetOp) platforms is the follow on for European scatterometers. This paper describes the ASCAT system and its capabilities, including the instrument, calibration, ground processing, and products, and reviews the science associated with scatterometer measurements.

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The advanced scatterometer (ASCAT) on the meteorological operational (MetOp) platform: A follow on for European wind scatterometers

Technical Note / Note technique The advanced scatterometer (ASCAT) on the meteorological operational (MetOp) platform: A follow on for European wind scatterometers

Snow on Antarctic sea ice plays a complex and highly variable role in air-sea-ice interaction processes and the global climate system. This paper presents snow data collected during the past ten years, and reviews major findings. These include: differences in regional and seasonal snow properties and thicknesses; the unique consequences of snow on Antarctic pack ice relative to the Arctic (e.g. the importance of flooding and snow-ice formation); the potential impact if global change increases snowfall; lower observed values of snow thermal conductivity than those used in models; periodic large-scale melt in winter; and the contrast in summer melt in the Antarctic and Arctic. The new findings have significant implications for modelling and remote-sensing studies. Different snow properties from Arctic conditions are recommended for use in Antarctic models; similar differences could affect the interpretation of remote-sensing data over sea ice.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2000RG000085

Snow on Antarctic sea ice

Wind measurements by the National Aeronautics and Space Administration (NASA) scatterometer (NSCAT) and the SeaWinds scatterometer on the NASA QuikSCAT satellite are compared with buoy observations to establish that the accuracies of both scatterometers are essentially the same. The scatterometer measurement errors are best characterized in terms of random component errors, which are about 0.75 and 1.5 m s 1 for the along-wind and crosswind components, respectively. The NSCAT and QuikSCAT datasets provide a consistent baseline from which recent changes in the accuracies of 10-m wind analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the U.S. National Centers for Environmental Prediction (NCEP) operational numerical weather prediction (NWP) models are assessed from consideration of three time periods: September 1996–June 1997, August 1999–July 2000, and February 2002–January 2003. These correspond, respectively, to the 9.5-month duration of the NSCAT mission, the first 12 months of the QuikSCAT mission, and the first year after both ECMWF and NCEP began assimilating QuikSCAT observations. There were large improvements in the accuracies of both NWP models between the 1997 and 2000 time periods. Though modest in comparison, there were further improvements in 2002, at least partly attributable to the assimilation of QuikSCAT observations in both models. There is no evidence of bias in the 10-m wind speeds in the NCEP model. The 10-m wind speeds in the ECMWF model, however, are shown to be biased low by about 0.4 m s 1 . While it is difficult to eliminate systematic errors this small, a bias of 0.4 m s 1 corresponds to a typical wind stress bias of more than 10%. This wind stress bias increases to nearly 20% if atmospheric stability effects are not taken into account. Biases of these magnitudes will result in significant systematic errors in ocean general circulation models that are forced by ECMWF winds.

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Scatterometer-Based Assessment of 10-m Wind Analyses from the Operational ECMWF and NCEP Numerical Weather Prediction Models

Although spaceborne scatterometers such as the NASA scatterometer have inherently low spatial resolution, resolution enhancement techniques can be used to increase the utility of scatterometer data in monitoring sea-ice extent in the polar regions, a key parameter in the global climate. The resolution enhancement algorithm produces images of A and B, where A is the normalized radar backscatter coefficient σO at 40° incidence and B is the incidence angle dependence of σO. Dual-polarization A and B parameters are used to identify sea ice and ocean pixels in composite images. The A copolarization ratio and vertically polarized B are used as primary classification parameters to discriminate between sea ice and open ocean. Estimates of the sea-ice extent are obtained using linear and quadratic (Mahalanobis distance) discriminant boundaries. The distribution parameters needed for the quadratic estimate are taken from the linear estimate. The σO error variance is used to reduce errors in the linear and Mahalanobis ice/ocean classifications. Noise reduction is performed through binary image region growing and erosion/dilation techniques. The resulting edge closely matches the NASA Team algorithm special sensor microwave imager derived 30% ice concentration edge. A 9-month data set of global sea-ice extent maps is produced with one 6-day average map every 3 days.

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Sea ice extent mapping using Ku band scatterometer data

Characterizing the variability in sea ice in the polar regions is fundamental to an understanding of global climate and the geophysical processes governing climate changes. Sea ice can be grouped into a number of general classes with different characteristics. Multisensor data from NSCAT, ERS-2, and SSM/I are reconstructed into enhanced resolution imagery for use in ice-type classification. The resulting twelve-dimensional data set is linearly transformed through principal component analysis to reduce data dimensionality and noise levels. An iterative statistical data segmentation algorithm is developed using maximum likelihood (ML) and maximum a posteriori (MAP) techniques. For a given ice type, the conditional probability distributions of observed vectors are assumed to be Gaussian. The cluster centroids, covariance matrices, and a priori distributions are estimated from the classification of a previous temporal image set. An initial classification is produced using centroid training data and a weighted nearest-neighbor classifier. Though validation is limited, the algorithm results in an ice classification that is judged to be superior to a conventional k-means approach.

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An iterative approach to multisensor sea ice classification

Polar sea ice is an important input to global climate models and is considered to be a sensitive indicator of climate change. While originally designed only for wind estimation, radar backscatter measurements collected by wind scatterometers have proven useful for estimating the extent of sea ice. During the Quick Scatterometer (QuikSCAT) mission, SeaWinds data were used to operationally map the sea ice extent. The resulting sea ice maps were used to mask near-surface winds to support SeaWinds' primary mission of measuring near-surface winds over the ocean. This paper describes the operational SeaWinds sea ice extent mapping algorithm, provides validation comparisons, and presents results from the ten-year data product. Starting with enhanced resolution horizontal polarization and vertical polarization backscatter images, the algorithm employs an iterative maximum-likelihood classifier with fixed thresholds to segment sea ice and open ocean pixels. Residual classification errors are reduced through binary image processing techniques and sea ice growth/retreat constraint methods. The algorithm results are compared with sea ice concentrations derived from Special Sensor Microwave/Imager data and with RADARSAT synthetic aperture radar imagery. The results suggest differences in the sensitivities of active and passive products given their channel sets and specific algorithms. Derived sea ice extents over the full decade-long QuikSCAT mission data set are analyzed to show important trends in sea ice extent for the Antarctic and Arctic regions.

https://www.mers.byu.edu/long/papers/TGARS2014_Remund.pdf

A Decade of QuikSCAT Scatterometer Sea Ice Extent Data

Microwave radiometers, while traditionally utilized in atmospheric and oceanic studies, can also be used in land surface applications. However, the problem of undesirable atmospheric effects caused by clouds and precipitation must be addressed. In this paper, temporal composite surface brightness images are generated from special sensor microwave/imager (SSM/I) data with the aid of new algorithms to eliminate small-scale distortion caused by clouds or precipitation. Mean, second-highest value, modified maximum average (MMA), and hybrid compositing algorithms are compared. The effectiveness of each algorithm is illustrated through simulation and real data distribution analysis. The results show that the second-highest value algorithm is biased high. MMA provides a more accurate brightness temperature estimate in areas of atmospheric distortion, while the mean is superior in regions with little or no distortion. A hybrid algorithm is developed that is a combination of MMA and mean. It utilizes the strengths of both to create a superior algorithm for regions with varying levels of distortion. Uses of composite images produced by these algorithms include studies of vegetation change, land cover classification, and surface parameter extraction.

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A cloud-removal algorithm for SSM/I data

Polar sea ice characteristics provide important inputs to models of several geophysical processes. Microwave scatterometers are ideal for monitoring these regions due to their sensitivity to ice properties and insensitivity to atmospheric distortions. Many forward electromagnetic scattering models have been proposed to predict the normalized radar cross section (/spl sigma//spl deg/) 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 use scatterometer data in a large-scale inverse modeling experiment. Given the limited data 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. The result is a least squares estimate of three surface parameters: the power reflection coefficient at nadir, the rms surface slope, and the volume scattering albedo. 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 National Aeronautics and Space Administration scatterometer. The results are used to analyze and interpret /spl sigma//spl deg/ phenomena occurring in the Antarctic and the Arctic.

/pdf/large-scale-inverse-ku-band-backscatter-modeling-of-sea-ice-3i3rv5lmtf.pdf

Q.P. Remund

Papers

Sea ice extent mapping using Ku band scatterometer data

An iterative approach to multisensor sea ice classification

A Decade of QuikSCAT Scatterometer Sea Ice Extent Data

A cloud-removal algorithm for SSM/I data

Large-scale inverse Ku-band backscatter modeling of sea ice