Engineering Commons^™

Observation And Characterization Of Radar Backscatter Over Greenland, David G. Long, Ivan S. Ashcraft

Faculty Publications

Characterization of the microwave signature of the Greenland snow surface enables delineation of the different snow facies and is a tool for tracking the effects of climate change. A new empirical observation model is introduced that uses a limited number of parameters to characterize the snow surface based on the dependence of radar backscatter on incidence angle, azimuth angle, spatial gradient, and temporal rate of change. The individual model parameters are discussed in depth with examples using data from the NASA Scatterometer (NSCAT) and from the C-band European Remote Sensing (ERS) satellite Advanced Microwave Instrument in scatterometer mode. The contribution …

A Field-Wise Retrieval Algorithm For Seawinds, Stephen L. Richards

Theses and Dissertations

In the spring of 1999 NASA will launch the scatterometer SeaWinds, beginning a 3 year mission to measure the ocean winds. SeaWinds is different from previous spaceborne scatterometers in that it employs a rotating pencil-beam antenna as opposed to fixed fan-beam antennas. The scanning beam provides greater coverage but causes the wind retrieval accuracy to vary across the swath. This thesis develops a filed-wise wind retrieval algorithm to improve the overall wind retrieval accuracy for use with SeaWinds data.

In order to test the field-wise wind retrieval algorithm, methods for simulating wind fields are developed. A realistic approach interpolates the …

Calibration Of And Attitude Error Estimation For A Spaceborne Scatterometer Using Measurements Over Land, Clarence J. Wilson Iii

Theses and Dissertations

The NASA Scatterometer (NSCAT) was launched August 20, 1996 aboard the National Space Development Agency of Japan's Advanced Earth Observing Spacecraft (ADEOS). NSCAT's primary mission was to measure radar backscatter over the world's oceans. These measurements are used to generate estimates of ocean wind speed and direction. Scatterometers must be calibrated before their measurements are scientifically useful. However, the calibration of NSCAT must be done in orbit. A new methodology for selecting land regions for use in extended target spaceborne scatterometer calibration is first developed. Next, a summary of the calibration technique used in this thesis is presented. While the …

Uncertainties In Oceanic Microwave Remote Sensing: The Radar Footprint, The Wind-Backscatter Relationship, And The Measurement Probability Density Function, Paul E. Johnson

Theses and Dissertations

Oceanic microwave remote sensing provides the data necessary for the estimation of significant geophysical parameters such as the near-surface vector wind. To obtain accurate estimates, a precise understanding of the measurements is critical. This work clarifies and quantifies specific uncertainties in the scattered power measured by an active radar instrument.

While there are many sources of uncertainty in remote sensing measurements, this work concentrates on three significant, yet largely unstudied effects. With a theoretical derivation of the backscatter from an ocean-like surface, results from this dissertation demonstrate that the backscatter decays with surface roughness with two distinct modes of behavior, …

The Probability Distribution Of Nscat Measurements, Paul E. Johnson, David G. Long

Faculty Publications

NSCAT makes only indirect measurements of wind. The direct measurement is of the backscattered radar power. The signal power is contaminated by radiometric noise so a separate measurement of the noise power is subtracted from the signal-plus-noise measurement to estimate the backscattered power. Using the radar equation, sigma-0 is computed from the measured signal power. From multiple sigma-0 mesaurements made at different azimuth angles, the wind is estimated. In wind retrieval, the NSCAT sigma-0 measurements are assumed to have a Gaussian probability distribution with a variance which depends on the mean. Given this distribution model, the maximum-likelihood estimtor is formed …