http://ieeexplore.ieee.org TOC Alert for Publication# 36 2013 May 16 51 6 C1 C1 583 51 6 C2 C2 141 51 6 3205 3206 126 51 6 3207 3216 1308 $75^{circ}06^{prime} hbox{S}$, $123^{circ}21^{prime} hbox{E}$, 3233 m amsl) during the first summertime campaign for 12 days in January–February 2009. The radiometer has been continuously running since January 2010, hosted within a dedicated shelter. We have used the very first set of HAMSTRAD data, recorded when the instrument was outdoors, to assess its potential to sound the troposphere over Dome C, from the planetary boundary layer (PBL) up to the tropopause ($sim$ 6 km above surface, $sim$9 km amsl). We have compared the HAMSTRAD measurements to several sets of measurements performed at the Dome-C station or in its vicinity: meteorological radiosondes, in situ PT100 and Humicap sondes along the vertical extent of a 45-m tower, meteorological sensor attached to the HAMSTRAD instrument, and the spaceborne Infrared Atmospheric Sounding Interferometer (IASI) instrument onboard the EUMETSAT MetOp-A satellite in polar orbit. The variability of integrated water vapor (IWV) observed by HAMSTRAD with extremely low values of 0.5 $hbox{kg} cdot hbox{m}^{-2}$ was also measured by the radiosondes (very high HAMSTRAD versus radiosonde correlation of 0.98), whereas IASI cloud-free measurements did not reproduce well the HAMSTRAD IWV variation (weak HAMSTRAD versus IASI correlation of 0.58). The measurements of absolute humidity $(hbox{H}_{2}hbox{O})$ from HAMSTRAD at Dome C cover a large vertical extent from the surface to about 6 km above surface with a high sensitivity in the free troposphere. The strong diurnal variation of $hbox{H}_{2}hbox{O}$ observed by the in situ sensors in the PBL is not well detected by the radiometer. In the free troposphere, the HAMSTRAD versus radiosonde $hbox{H}_{2}hbox{O}$ correlation can reach 0.8–0.9. Around the tropopause, HAMSTRAD shows the same variability as IASI and radiosondes but with a dry bias of 0.01 $ hbox{g} cdot hbox{m}^{-3}$. HAMSTRAD tends to show a wetter atmosphere by 0.1–0.3 $hbox{g} cdot hbox{m}^{-3}$ compared with radiosondes from the surface to $sim$2-km altitude and a drier atmosphere above by $sim!! hbox{0.1} hbox{g} cdot hbox{m}^{-3}$. The sensitivity of the temperature profiles from HAMSTRAD is very high in the PBL and in the free troposphere but degrades around the tropopause. The strong diurnal signal measured above the surface by HAMSTRAD (3–6 K) is consistent with all the other in situ data sets. The temporal evolution over the 12-day period in the PBL is also consistent with all other data sets (radiosondes, IASI, in situ sondes, and meteorological sensors). In the free troposphere and around the tropopause, the HAMSTRAD temporal evolution is consistent with that observed by radiosondes and IASI, although a cold bias exists compared with IASI and radiosondes around the tropopause. For heights less than 4 km above surface, HAMSTRAD correlates very well with radiosondes a]]> 51 6 3217 3239 2923 51 6 3240 3249 592 51 6 3250 3258 1401 51 6 3259 3272 4637 $>10,000$ concurrent in situ SST measurements from buoys, images processed with the hybrid algorithm showed increases in data capture and improved accuracy statistics over most existing algorithms. In particular, while keeping the same accuracy, the hybrid algorithm resulted in nearly 20% more SST retrievals than the most accurate algorithm (Quality SST) currently being used for operational processing. The increases in both data coverage and SST range should improve MODIS data products for more reliable SST retrievals in near real time, thus enhancing the ocean observing capacity to detect anomaly events and study short- and long-term SST changes in coastal environments.]]> 51 6 3273 3285 2157 $hbox{root mean square error (RMSE)} = 0.115 hbox{m}^{-1}$ and $R^{2} = 0.73$) in the Atchafalaya River plume area. QAA-CDOM is also evaluated for scenarios in three additional study sites, namely, the Mississippi River, Amazon River, and Moreton Bay, where $a_{g}(440)$ was in the wide range of 0.01–15 $hbox{m}^{-1}$. It resulted in expected CDOM distribution patterns along the river salinity gradient. This study improves the high-resolution observation of CDOM dynamics in river-dominated coastal margins and other coastal environments for the study of land–ocean interactive processes.]]> 51 6 3286 3298 1609 51 6 3299 3305 883 $(T_{s})$ and NCEP/DOE Reanalysis II in the Laptev Sea for two winter seasons. Improvements of the algorithm mainly concern the implementation of an iterative approach to calculate the atmospheric flux components taking the atmospheric stratification into account. Furthermore, a sensitivity study is performed to analyze the errors of the ice thickness. The results are the following: 1) 2-m air temperatures $(T_{a})$ and $T_{s}$ have the highest impact on the retrieved ice thickness; 2) an overestimation of $T_{a}$ yields smaller ice thickness errors as an underestimation of $T_{a}$; 3) NCEP $T_{a}$ shows often a warm bias; and 4) the mean absolute error for ice thicknesses up to 20 cm is $pm$4.7 cm. Based on these results, we conclude that, despite the shortcomings of the NCEP data (coarse spatial resolution and no polynyas), this data set is appropriate in combination with MODIS $T_{s}$ for the retrieval of TITs up to 20 cm in the Laptev Sea region. The TIT algorithm can be applied to other polynya regions and to past and future time periods. Our - IT product is a valuable data set for verification of other model and remote sensing ice thickness data.]]> 51 6 3306 3318 1588 51 6 3319 3327 1028 51 6 3328 3335 1184 $sigma^{circ}$ agrees within 1 dB with nearly simultaneous Envisat Advanced Synthetic Aperture Radar measurements and literature values. Sea ice was classified using a Bayesian maximum likelihood approach. By including information from simultaneous infrared and visible video imagery of sea ice, four different surface types of sea ice could be identified in the resulting $sigma^{circ}$: old ice, gray ice, nilas, and open water. The most reliable classification was obtained through combination of copolarized C-, X-, and Ku-band data. The results degraded by only 7% in the case where the X-band information was dropped. On the other hand, a combination of the C- and X-bands or the X- and Ku-bands yielded a degradation of 13%. Given the remaining uncertainties in the approach, for sea ice classification during summer/fall conditions, our results suggest the complementary use of two of these three frequency bands instead of relying on just one frequency band.]]> 51 6 3336 3353 2769 51 6 3354 3370 3669 $r^{2}$) and an rmse of 28.68 t/ha using the combination of texture parameters of both polarizations (C-HV and C-HH). However, goodness of fit could be improved to 0.91 (adjusted $r^{2}$) and an rmse of 26.95 t/ha for biomass levels up to 532 t/ha using the ratio of texture parameters of C-HV/C-HH. The result is very encouraging and indicates that the dual-polarization C-band SAR sensor has a potential for the estimation of forest biomass, particularly using the polarization ratio of texture measurements, and biomass estimation can be improved substantially beyond the previously stated saturation level for C-band SAR.]]> 51 6 3371 3384 1437 $hbox{m}^{2}$ outdoor experimental burn. We compare mapped information on FRP obtained from simultaneous nadir and off-nadir views, where we find differences that are in part controlled by flame structure and/or view angle. In the large open fire case, we compare the mapped fire radiative energy and ROS to simultaneously acquired aerial photography that provides the position of fuel and flames in high detail, and we demonstrate how these data sets can be used to explore various aspects of fire behavior.]]> 51 6 3385 3399 2440 51 6 3400 3409 753 51 6 3410 3423 1245 51 6 3424 3430 483 51 6 3431 3438 1377 $1/R^{6}$ drop-off with range, and a deep UXO may not produce a strong response while still being potentially hazardous. To address this problem, we propose in this paper an inversion method based on Kalman and extended Kalman filters meant to do the following: 1) work with dynamic data; 2) provide both position and polarizability estimates; and 3) operate in real time (less than 100 ms in our case). Such full characterization of the target, albeit limited to within the 2.7-ms interrogation time window of the dynamic mode associated with the sensors studied here, provides useful information when deciding whether to continue with the cued interrogation. We validate the method for two popular EMI sensors, the second-version Man Portable Vector (MPV-II) and the MetalMapper, operated in very different settings: The MPV-II is used to interrogate a limited region of space atop a single target, whereas the MetalMapper is driven over long lanes along which several targets and clutter items are present.]]> 51 6 3439 3451 1996 51 6 3452 3460 1721 $(lambda = 0.24 hbox{m})$. For homogeneous soils with rough surfaces, the results of FEM are compared with the predictions of the small perturbation method. In the case of heterogeneous substrates, soil moisture (and, hence, soil permittivity) is assumed to vary as a function of depth. In this case, the results of FEM are compared with those of the transfer matrix method for flat soil surfaces. In both cases, a good agreement is found. For homogeneous rough soils, it is found that polarimetric radar backscatter and copolarized phase difference have a nonlinear relationship with soil moisture. Finally, it is found that the nature of the soil moisture variation in the top few centimeters of the soil has a strong influence on the backscatter and, hence, on the inferred soil moisture content.]]> 51 6 3461 3469 800 51 6 3470 3470 296 51 6 3471 3472 1484 51 6 C3 C3 107 51 6 C4 C4 197