As a PI on the NASA Scatterometer (NSCAT) Science Working Team and as the former Project Engineer for the NSCAT Project, Dr. Long was heavily involved in the calibration of the NSCAT instrument and in science data analysis. He and his students developed new processing techniques, science algorithms, and calibration approaches.

NSCAT picture (59K)

NSCAT OCEAN WIND MEASUREMENT

NSCAT was designed to measure near-surface winds over the ocean. This image [Low-Res (74kb) Hi-Res (810kb)] shows ocean surface wind speeds and directions over the Pacific Ocean on 21 September 1996 as they were measured by the NASA Scatterometer (NSCAT) onboard Japan's Advanced Earth Observing Satellite (ADEOS). The background color indicates wind speed and the white arrows show the direction of the wind. The basin-wide wind field is representative of near-Equinox atmospheric circulation. The strong Trade Winds (red) blow steadily from the cooler subtropical ocean to the warm water of the Intertropical Convergence Zone (ITCZ) located just north the Equator. Instead of blowing in the north-south direction, the winds are deflected westward by the Corriolis Force due to the Earth's rotation. The air rises over the warm water of ITCZ and sinks in the subtropics at the Horse Latitudes, forming the Hadley Circulation. Both the convergence area at the ITCZ and the divergence area at the Horse Latitudes are indicated by low wind speed of blue color. In the mid-latitudes, the high vorticity due to the Corriolis Force generates cyclones (yellow spirals) moving in the eastward direction. Two typhoons are observed in the western Pacific. Typhoon Violet is just south of Japan. After these data were taken, Typhoon Violet struck the East Coast of Japan causing damage and deaths. Typhoon Tom is located further east and did not land.

The image is based on preliminary processing of the first set of NSCAT observations, using prelaunch model function and calibration. Improvement is expected after the standard calibration and beam balancing procedures. The image is produced by objective interpolation as described by Tang and Liu [JPL Publication 96-19, 1996] based entirely on NSCAT data. This preliminary analysis clearly demonstrates that the high spatial resolution of NSCAT data improves the monitoring of sever storms, such as typhoons, which are usually grossed over by conventional methods. It also shows that the repeated global coverage provides a better description of atmospheric circulation over ocean that is not adequately sampled in the past.

NSCAT LAND, ICE, AND VEGETATION APPLICATIONS

In addition to their primary role of measuring oceanic winds, spaceborne scatterometers can significantly contribute to non-ocean studies in vegetation and polar ice. The recently launched NASA Scatterometer (NSCAT) is an important asset in such studies. Several applications are highlighted below.

The polar regions play a central role in regulating global climate, and it is important to accurately record and monitor the extent and surface conditions of the earth's major ice masses. Such monitoring can only be done using spaceborne sensors. Spaceborne radar remote sensors are uniquely well-suited for mapping the polar regions since the radar can image the surface through clouds and both day and night. Active radar instruments are useful for studying snow and ice. Similarly, radars are also useful for vegetation studies because different vegetation types and densities have different radar responses.

A radar scatterometer measures the radar backscattering cross-section (termed "sigma-0" by scientists) of the Earth's surface. Measurements of sigma-0 over the ocean are used to infer the near-surface wind speed and velocity. Measurements of sigma-0 in the polar regions and over land can be used to study ice and vegetation.

BYU developed a technique for making high resolution sigma-0 images of the Earth's surface. Areas which reflect more microwaves are typically rougher and appear brighter in the images than smoother areas which reflect less. The electrical properties of the surface also affect the image brightness.

Figure 1 (gif 356K) illustrates an image formed from NSCAT sigma-0 measurements and demonstrate the wealth of information contained in the scatterometer data. This image shows Antarctica and the surrounding sea ice constructed from 6 days of scatterometer data in Sept. 1996. The black circle in the center of the image is where no data was collected owing to NSCAT's orbital and sampling geometry. The dark band around the continent is sea-ice pack surrounding Antarctica. The variations in sea ice show the circulation patterns and are due to the snow cover, thickness, and history of the ice since formation. This information is essential to understand the effects of the ice pack on the ocean and climate systems. The white, rectangular object in the ice pack on the lower left of the image is a 50 km x 100 km "super-berg" which broke off the Thwaites ice tongue in 1995 and circulated in the sea ice pack for many years. in the sea-ice pack. Other large icebergs are also visible in the image. A time-sequence (movie) of these images is helping scientists to understand more about how the ice is formed and circulates.

Antarctica is overed with a thick ice sheet which appears very bright in the image due to snow crust and refrozen ice in the snow cover. Details visible in the glacial ice cover show the locations of ice "hills" and "valleys" which reveal information about the flow of the ice over the subsurface topography. The relative brightness can be useful for determining the annual snowfall. For reference Figure 2 (gif 358K) shows a view of the Arctic ice pack.

Antarctic Images and Movies

Ice masked NSCAT images of the polar regions are animated in the following movies. The images have been down sampled from original sizes. These movies are available in avi and Animated gif formats.
 

NSCAT Antarctic Ice Masked Animated Gif -Small(885KB) Animated Gif -Large (5.449 MB) AVI movie (4.434 MB) Mpeg movie (2.137 MB) QuickTime (1.594 MB)

NSCAT Arctic Ice Masked Animated Gif (8.192 MB) AVI movie (4.209 MB) Mpeg movie (1.14 MB) QuickTime (990 KB)

A full global image as produced from NSCAT data is shown in Figure 3 (b/w gif 402K) (color gif 383K) . The brightest regions are glacial ice sheets in Greenland and Antarctica as described above. For other regions, the brightness of the image is related to the vegetation cover and soil moisture. Tropical rainforests along the equator in South America, Africa, and Southeast Asia are relatively bright while desert regions are dark. Very dry, sandy deserts show up as black in this image. Some examples are the Empty Quarter in Saudia Arbia, the Gobi desert in Western China, and the great erg (sand dune) seas in Sahara desert in North Africa. The light area just below the wide, dark band in Africa is known as the Sahel. This area lightens and darkens with the changing season and drought conditions in Africa. The seasonal radar response of the Sahel is thought to be a sensitive indicator of desertification due to global warming and climate change.

Tropical rainforests are critical to the climate health of the world and are thought to contain 1/2 of all the world's species. Figure 4 [hires b/w gif 523K] [lores b/w gif 99K] [color gif 492K) shows the Amazon rainforest over South America as observed by NSCAT. Because the radar response is sensitive to the type and density of vegetation, the scatterometer data can provide information useful for discriminating and mapping vegetation. A false color image helps discriminate general areas of tropical rainforest (blues and purples) from woodlands and savannah (greens and yellows). Mountains and degraded farm lands show up as black. [Note: data is not calibrated so this IS NOT a classified image.] The NSCAT data is able to delineate the extent of the tropical rainforest. Comparison of this image taken in 1996 with images made from Seasat scatterometer data collected in 1978 may help reveal the extent of tropical deforestation in this sensitive area.

Statistical Modeling

• Sensor calibration
• Signal processing design and performance
• Probability distribution of NSCAT sigma-0 measurements
• Geophysical modeling uncertainty
• Cramer-Rao accuracy bound for wind estimation
• Improved wind retrieval techniques
• Ambiguity removal algorithm and accuracy assessment techniques
• Distribution of negative NSCAT sigma-0 measurements.
• Automated sea ice extent mapping
• Azimuth modulation of backscatter in Antarctica
• Sea ice classification

NSCAT NASA Press Releases

• NSCAT Land/Ice Imaging Nov. 1996
• Greenland Melting Dec. 1997

Selected NSCAT Related Papers

• Q.P. Remund and D.G. Long, "Large-scale Inverse Ku-band Backscatter Modeling of Sea Ice," IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 8, pp. 1821-1832, doi:10.1109/TGRS.2003.813495, 2003.
• D.G. Long and M.R. Drinkwater, "Azimuth Variation in Microwave Scatterometer and Radiometer Data Over Antarctica," IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 4, pp. 1857-1870, do:10.1109/36.851769, 2000.
• J. Zec, W.L. Jones, and D.G. Long, "NSCAT Normalized Radar Backscattering Coefficient Biases Using Homogenous Land Targets," Journal of Geophysical Research, Vol. 104, No. C5, pp. 11557-11568, doi:10.1029/1998JC900098, 1999.
• W-Y Tsai, J.E. Graf, C. Winn, J.N. Huddleston, S. Dunbar, M.H. Freilich, F.J. Wentz, D.G. Long, and W.L. Jones, "Postlaunch Sensor Verification and Calibration of the NASA Scatterometer," IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 3, pp. 1517-1542, doi:10.1109/36.763264, 1999.
• T. Oliphant and D.G. Long, "Accuracy of Scatterometer-Derived Winds Using the Cramer-Rao Bound," IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 6, pp. 2642-2652, doi:10.1109/36.803412, 1999.
• D.G. Long and M.R. Drinkwater, "Cryosphere Applications of NSCAT Data," invited paper, IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 3, pp 1671-1684, doi:10.1109/36.763287, 1999.
• Q.P. Remund and D.G. Long, "Sea Ice Extent Mapping Using Ku band Scatterometer Data,"  Journal of Geophysical Research, Vol. 104, No. C5, pp. 11515-11527, doi:10.1029/98JC02373, 1999.
• A.E. Gonzales and D.G. Long, "An Assessment of NSCAT Ambiguity Removal," Journal of Geophysical Research, Vol. 104, No. C5, pp. 1149-11457, doi:10.1029/98JC01943, 1999.
• P.E. Johnson and D.G. Long, "The Probability Density of Spectral Estimates Based on Modified Periodogram Averages,"  IEEE Transactions on Signal Processing, Vol. 47, No. 5, pp 1255-1261, doi:10.1109/78.757213, 1999.
• J. Graf, C. Sasaki, C. Winn, W.T. Liu, W. Tsai, M. Freilich and D.G. Long, "NASA Scatterometer Experiment," Acta Astronautica, Vol. 43, No. 7-8, pp. 377-407, doi:10.1016/S0094-5765(97)00180-X, 1998.
• D.G. Long, "Comparison of TRMM and NSCAT Observations of Surface Backscatter Over the Amazon Rainforest," Proceedings of the International Geoscience and Remote Sensing Symposium, pp. 1879-1881, Seattle, Washington, doi:10.1109/IGARSS.1998.703682, 6-10 July, 1998.
• F. Naderi, M. H. Freilich, and D. G. Long, "Spaceborne Radar Measurement of Wind Velocity Over the Ocean--An Overview of the NSCAT Scatterometer System", invited paper, Proceedings of the IEEE, pp. 850-866, Vol. 79, No. 6, doi:10.1109/5.90163, June 1991.
• D. G. Long and J. M. Mendel, "Identifiability in Wind Estimation from Wind Scatterometer Measurements," IEEE Transactions on Geoscience and Remote Sensing, Vol. 29, No. 2, pp. 268-276, doi:10.1109/36.73668, 1991.
• S. J. Shaffer, R.S. Dunbar, S. V. Hsiao, and D.G. Long, "A Median-Filter-Based Ambiguity Removal Algorithm for NSCAT," IEEE Transactions on Geoscience and Remote Sensing, Vol. 29, No. 1, pp. 167-174, doi:10.1109/36.103307, Jan. 1991.
• D. G. Long, C-Y Chi, and F. K. Li, "The Design of an Onboard Digital Doppler Processor for a Spaceborne Scatterometer," IEEE Transactions on Geoscience and Remote Sensing, Vol. 26, No. 6, pp. 869-878, doi:10.1109/36.7718, Nov. 1988.
• C-Y Chi, D. G. Long and F. K. Li, "Radar Backscatter Measurement Accuracies Using Digital Doppler Processors in Spaceborne Scatterometers," IEEE Transactions on Geoscience and Remote Sensing, Vol. GE-24, No. 3, pp. 426-437, doi:10.1109/TGRS.1986.289602, May 1986.