GNSS-R interferometric reflectometry (also known as GNSS-IR, or GPS-IR for GPS signals) is a technique that uses data from geodetic-quality GNSS instruments for sensing the near-field environment. In contrast to positioning, atmospheric, and timing applications of GNSS, GNSS-IR uses the signal-to-noise ratio (SNR) data. Software is provided to translate GNSS files, map GNSS-IR reflection zones, calculate GNSS-IR Nyquist frequencies, and estimate changes in the height of a reflecting surface from GNSS SNR data.

ISI Document Delivery No.: 645AI Times Cited: 10 Cited Reference Count: 26 Cited References: [Anonymous], 2009, OSTM JAS 2 PROD HDB Anzenhofer M., 1999, 464 OH STAT U BAMBER JL, 1994, INT J REMOTE SENS, V15, P925 Bao LF, 2009, PROG NAT SCI-MATER, V19, P195, DOI 10.1016/j.pnsc.2008.06.017 BARRICK DE, 1985, ADV GEOPHYS, V27, P61 Bowen MM, 2002, J ATMOS OCEAN TECH, V19, P1665, DOI 10.1175/1520-0426(2002)019<1665:EMSCFS>2.0.CO;2 BROOKS RL, 1997, NASA WFF PUBL BROWN GS, 1977, IEEE T ANTENN PROPAG, V25, P67, DOI 10.1109/TAP.1977.1141536 BROWN S, 2008, COAST ALT WORKSH FEB CHELTON DB, 2001, SATELLITE ALTIMETRY, P1, DOI DOI 10.1016/S0074-6142(01)80146-7 Crocker RI, 2007, IEEE T GEOSCI REMOTE, V45, P435, DOI 10.1109/TGRS.2006.883461 Davis CH, 1997, IEEE T GEOSCI REMOTE, V35, P974, DOI 10.1109/36.602540 Deng X, 2006, J GEOPHYS RES-OCEANS, V111, DOI 10.1029/2005JC003039 Deng X., 2002, MAR GEOD, V25, P249, DOI DOI 10.1080/01490410290051572 GOMMENGINGER C, 2009, COASTAL ALTIMETRY HAYNE GS, 1980, IEEE T ANTENN PROPAG, V28, P687, DOI 10.1109/TAP.1980.1142398 Hwang CW, 2006, J GEODESY, V80, P204, DOI 10.1007/s00190-006-0052-x Labroue S., 2004, MAR GEOD, V27, P453, DOI 10.1080/01490410490902089 Lee H, 2008, J GEODYN, V46, P182, DOI 10.1016/j.jog.2008.05.001 Lee H. K., 2008, TERR ATMOS OCEAN SCI, V19, P38, DOI [10.3319/TAO.2008.19.1-2.37( SA), DOI 10.3319/TAO.2008.19.1-2.37(SA)] MARTIN TV, 1983, J GEOPHYS RES-OC ATM, V88, P1608, DOI 10.1029/JC088iC03p01608 Pavlis N.K., 2008, EARTH GRAVITATIONAL RODRIGUEZ E, 1989, J GEOPHYS RES-OCEANS, V94, P9761, DOI 10.1029/JC094iC07p09761 RODRIGUEZ E, 1988, J GEOPHYS RES-OCEANS, V93, P14107, DOI 10.1029/JC093iC11p14107 Shum C, 2003, MAR GEOD, V26, P335, DOI 10.1080/01490410390253487 U.S. Dept. of Commerce National Oceanic and Atmospheric Administration National Geophysical Data Center, ETOPO2V2 GLOB GRIDD Lee, Hyongki Shum, C. K. Emery, William Calmant, Stephane Deng, Xiaoli Kuo, Chung-Yen Roesler, Carolyn Yi, Yuchan NASA; Ohio State University This research is supported by grants from NASA's Ocean Surface Topography Science Team Program, and from the Ohio State University's Climate, Water, and Carbon Program. We also thank two anonymous reviewers for their constructive comments. 10 TAYLOR & FRANCIS INC PHILADELPHIA MAR GEOD 1We validated Jason-2 satellite altimeter Sensor Geophysical Data Records (SGDR) by retracking 20-Hz radar waveforms over the California coastal ocean using cycles 7-34, corresponding to September 2008-June 2009. The performance of the ocean, ice, threshold, and modified threshold retrackers are examined using a reference geoid based on Earth Gravitational Model 2008 (EGM08). Over the shallow ocean (depth < 200 m), the modified threshold retracker, which is developed for noisy waveforms with preleading edge bump, outperforms the other retrackers. It is also shown that retracking can improve the precision of sea surface heights (SSHs) for areas beyond 2-5 km from the shore. Although the ocean retracker generally performs well over the deep ocean (depth > 200 m), the ocean-ret...

[1] Coastal altimeter waveforms may differ from the ones in the open ocean, either from rapid changes in the sea state or the presence of land within the satellite altimeter footprint. The optimal retracking method for an individual track may turn out to be a combination of several retrackers and may depend on the sea state. The coastal high-frequency radar (HFR) ocean surface currents, hourly interpolated with a resolution up to 2 km and an offshore range up to 150 km, are evaluated to validate the altimeter sea surface height (SSH) measurements. A method to derive HFR SSH mapped, with a varying spatial-scale optimal interpolation, from the HFR velocities has been implemented. Evaluated mainly in the regions farther than 25 km off the U.S. West Coast, the HFR SSH shows good agreement with Jason-1-2 altimetry products over the years 2008 and 2009. Three Jason-2 PISTACH retrackers and one generic open ocean retracker have been analyzed using the traditional 1 Hz sampling rate. Nearshore, an experimental reprocessing of the 20 Hz range measurements is also tested to check for a gain in along-track spatial resolution. Referencing to the HFR SSH indicate the need to have several retrackers available, even over the continental shelf, with Ice3 fitting better during Bloom events and MLE-4 (or Red3) for high sea states. These studies demonstrate the value of HFR as a potential tool to correct coastal altimeter SSH, refine their spatial resolution and provide some insight into the altimeter behavior as a function of ocean conditions.

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We compare microwave GPS and optical-based remote sensing observations of the vegetation response to a recent drought in California, USA. The microwave data are based on reflected GPS signals that were collected by a geodetic network. These data are sensitive to temporal variations in vegetation water content and are made available via the Normalized Microwave Reflection Index (NMRI). NMRI data are complementary to information of plant greenness provided by the Normalized Difference Vegetation Index (NDVI). NMRI data from 146 sites in California are compared to collocated NDVI observations, over the interval of 2007-2016. This period includes a severe, three-year drought (2012)(2013)(2014). We quantify the seasonal variations in vegetation state by calculating a series of phenology metrics at each site, using both NMRI and NDVI. We examine how the phenology metrics vary from year-to-year, as related to the observed fluctuations in accumulated precipitation. The amplitude of seasonal vegetation growth exhibits the greatest sensitivity to prior accumulated precipitation. Above-normal precipitation from 4 to 12 months before peak growth yields a stronger seasonal growth pulse, and vice versa. The amplitude of seasonal growth, as determined from NDVI, varies linearly with precipitation during dry years, but is largely insensitive to precipitation amount in years with above-normal precipitation. In contrast, the amplitude of seasonal growth from NMRI varies approximately linearly with precipitation across the entire range of conditions observed. The length of season is positively correlated with prior accumulated precipitation, more strongly with NDVI than NMRI. The recovery from drought was similar for a one-year ( 2007) and the more severe three-year drought (2012)(2013)(2014). In both cases, the amplitude of growth returned to typical values in the first year with near-normal precipitation. Growing season length, only based on NDVI, was greatly reduced in 2014, the driest and final year of the three-year California drought.