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In this paper, we report on the performance of a semiempirical algorithm for the retrieval of soil moisture (SM) under dense tropical forests using ultrahigh frequency (UHF) polarimetric synthetic aperture radar (SAR) data. The algorithm is a simplification of a 3-D coherent model of forest canopy based on the distorted Born approximation (DBA). The simplified model reduces the number of parameters and preserves the three dominant scattering mechanisms of volume, volume-surface, and surface for three polarized backscattering coefficients, i.e., σ HH , σ HV , and σ VV , at UHF frequencies. The inversion process uses the Levenberg-Marquardt nonlinear least squares method to estimate the three model parameters: vegetation aboveground biomass, integrated SM up to a certain depth, and surface roughness. The performance of the inversion process is examined by first using simulation data where the initial values of the inversion process vary randomly and then using airborne UHF SAR data acquired in Costa Rica over La Selva Biological Station. The results with simulated data show that the inversion process is not significantly sensitive to initial values considering they are in the range of ±50% of the true value. A root-mean-square error (RMSE) of less than 4% can be achieved in retrieving the SM. The use of an alternate inversion approach without initial conditions using a genetic algorithm is less efficient (> 120 times longer time) and produces larger error with simulated data (RMSE = 11%) than the Levenberg-Marquardt estimation method. The inversion model simultaneously produces a biomass and SM distribution at 100-m spatial resolution. The RMSE of biomass estimation is 38 Mg/ha (15% relative error) when compared with 28 field plots. Over the plots where SM ground measurements are available, but not at the exact same day as the radar flight occurred, the total volumetric RMSE is 13.6%. However, only two ground measurements were very close to the flight day (three days apart), and for those, the SM estimate has about 3% absolute volumetric error. At the P-band, the SM sensing depth is inversely correlated with the SM allowing to map the spatial variations of SM close to the average root zone or hydrological active horizon of soils in tropical ecosystems.

This paper presents the procedure to perform cross-calibration of radar backscatter between the QuikSCAT and Oceansat-2 ocean wind scatterometers. Both QuikSCAT and Oceansat-2 are Ku-band dual pencil beam, rotating antenna scatterometers with similar design. There has been a joint effort by the Indian Space Research Organization, NASA, KNMI, and NOAA to perform calibration and validation of Oceansat-2 in order to extend the climate data record of ocean surface vector winds obtained by QuikSCAT. This has resulted in significant improvement in the quality of the normalized radar cross section (NRCS) data and the quality of the resultant winds produced using the Oceansat-2 NRCS measurements. An important aspect of this calibration is the reduction of the calibration bias between QuikSCAT and Oceansat-2. The nonspinning QuikSCAT scatterometer was repointed to achieve the same incidence angles for its two HH and VV polarized antenna beams as those utilized by Oceansat-2. The magnitudes of the NRCS (backscatter) measurements of the two scatterometers were then compared for two years in order to determine NRCS bias in decibels as a function of time. Biases for both antenna beams were computed. A wind speed/wind-relative azimuth angle histogram-matched method was applied to ocean data from the two scatterometers to determine the time series of the bias between the two. It has been determined that there was an $\sim$ 0.5 dB drop in Oceansat-2 radar backscatter on August 20, 2010. As a result, we compute cross-calibration adjustments to apply to Oceansat-2 data before and after this distinct drop in backscatter.

It has been well known that a few areas on the Earth's surface have a relatively constant backscattering coefficient and can serve as radar calibration targets. Examples include the Amazon rain forest, the Greenland ice sheet, parts of Antarctica, etc. However, there has not been any extensive investigation and quantitative evaluation of these targets in terms of their time variation, isotropy, and spatial variation. Here, we have analyzed a consistent set of Ku-band radar measurements for more than ten years from the QuikSCAT mission. This valuable set of data provides an unprecedented opportunity for us to study the long-term variability of observed backscattering from the Earth's surface at Ku-band. In this paper, we performed a global survey of potential constant land targets and evaluated their variability. Quantitative measurements of temporal and spatial variabilities (homogeneity) and isotropy are used to identify the locations of the best natural calibration targets. We also discuss annual and long-term trends in the data and offer potential explanations for these trends. We examine small regions with the least overall variation. By concentrating on low-variation areas, we can identify useful calibration targets for future radar missions and for intercalibration between existing radars. At the same time, by focusing on regions with little spatial or temporal heterogeneity, we can analyze the temporal variation on diurnal, seasonal, and decadal scales in homogenous natural terrain types including rain forest, dry brushy areas, and ice sheets. We found that rain forest targets in the Amazon and Congo are very stable in time and homogeneous. However, they are subjected to diurnal difference. On the other hand, the Antarctica ice sheet is another good candidate for stable target, but it has seasonal variability. The Greenland ice sheet shows a significant trend in backscatter in recent years, and therefore, may not be a suitable calibration site anymore. Another location for a good stable target is a dry brushy area in the Sahara, which shows comparable stability and isotropy with those of the Amazon, Congo, and Antarctica.

This study examines the potential of using bistatic radar reflections from the Global Navigation Satellite System (GNSS) to classify sea ice types in the Arctic Ocean during the sea ice formation period. For this study, we used data obtained from the United Kingdom (UK) TechDemoSat-1 (TDS-1) satellite mission. The main objective of the TDS-1 mission is to provide L-band radar reflections of the ocean surface in order to infer wind speed through an estimation of the ocean surface roughness. Given the orbit inclination of TDS-1, polar coverage is obtained providing datasets over the Arctic sea ice cover. Recent studies demonstrated the use of TDS-1 data for accurately deriving ocean-ice detection using the shape of the bistatic radar waveforms derived from delay-Doppler maps. We aim to advance this previous research by demonstrating the sensitivity of GNSS bistatic radar signals to sea ice types. The originality of the study presented in this manuscript is the classification of sea ice types, never implemented before with any GNSS-R mission. For this study, we examined the fall period of October 2015 in the Beaufort and Chukchi seas region, when considerable expanses of young ice, first-year ice (FYI), and multi-year ice (MYI) were present. We developed a sea ice multi-step classification approach based on bistatic radar reflections to generate not only an ocean-ice classification but to classify between the three dominant ice types present. The classification was done in multiple steps based on GNSS bistatic radar observations only and includes checks for spatio-temporal consistency with each dominant class. Validation results are based on the comparison of the GNSS classification against SAR-derived sea ice type maps produced at the US National Ice Center. First, we derived classifications of sea ice–open water samples with a success rate of 97%, comparable to previous studies. Next, the sea ice type classification results identified FYI, MYI and young ice with success rates of 70%, 82% and 81%, respectively, indicating a strong sensitivity of the L-band GNSS bistatic radar signals to the different surface scattering properties of these primary ice types. With this manuscript we demonstrate the potential of the GNSS bistatic radar signals to classify ice types during the sea ice formation period. GNSS bistatic radar signals provide unique forward scattered measurements at L-band, potentially on a daily temporal revisit, that can be used to produce enhanced information on sea ice characteristics, such as those currently generated by other sensors. In order to determine the use of GNSS-R measurements on an operational basis, further studies are needed over a year-long period of sea ice growth and melt to determine the full benefit of GNSS for improving and providing complementary information to that generated by both microwave and optical sensors.

Surface soil moisture was retrieved globally by systematically correcting for the effects of vegetation and soil surface roughness. The retrieval is enabled by employing physical-models of radar forward scattering for individual vegetation types to account for vegetation scattering and absorption, and by constraining the surface roughness effect using time-series observations. The L-band SMAP multi-polarized (HH/VV/HV) σ° data acquired globally every three days were used from mid-April to early July, 2015. Assessment was conducted over 13 rigorously-chosen core validation sites covering a wide range of biomass types, biomass amount, and soil conditions. The soil moisture retrieval reached an accuracy of 0.06 m3/m3 RMSE, a bias of 0.003 m3/m3, and a correlation of 0.56. The successful retrieval demonstrates that the physically-based retrieval method is capable of characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation on a global scale.

The Soil Moisture Active Passive (SMAP) mission launched on Jan 31, 2015. The mission employs L-band radar and radiometer measurements to estimate soil moisture with 4\% volumetric accuracy at a resolution of 10 km, and freeze-thaw state at a resolution of 1–3 km [1]. Immediately following launch, there was a three month instrument checkout period, followed by six months of level 1 (L1) calibration and validation. A beta release of L1 radar data was available on July 1, 2015 and a validated release was made available starting on November 1, 2015. Work continued on L1 radar calibration and validation for several more months to improve the quality of the final product due to be released with the L2 validated release. In this presentation, we will discuss the radar processing algorithms and the calibration and validation activities for the L1 radar data.

Imaging of objects through complex environment is important in several applications, including imaging of hidden objects in obscuring media such as atmospheric and ocean turbulence, rough ocean surfaces, rain, fog, snow, and biological tissues. These media are often randomly varying in space and time, and statistical treatments are necessary to obtain images with useful spatial and temporal resolutions. In recent years, there has been increasing interest in using signal processing and correlation techniques to improve resolutions and to distinguish images from clutter. This paper presents several imaging techniques for objects in the presence of random media. Time-reversal MUSIC (multiple signal classification) imaging has excellent resolution when multiple scattering is small or moderate. Modified beamformer imaging has moderately high resolution even at large multiple scattering. We also include time reversal (TR) and synthetic aperture radar (SAR) imaging for comparison. The technique involves stochastic Green's function and mutual coherence function (MCF), eigenvectors of time-reversal matrix and pseudo spectrum. Numerical examples are given to show the effectiveness of these imaging techniques.

A semi-empirical algorithm for the retrieval of soil moisture, root mean square (RMS) height and biomass from polarimetric SAR data is explained and analyzed in this paper. The algorithm is a simplification of the distorted Born model. It takes into account the physical scattering phenomenon and has three major components: volume, double-bounce and surface. This simplified model uses the three backscattering coefficients (σ HH , σ HV and σ VV ) at low-frequency (P-band). The inversion process uses the Levenberg-Marquardt non-linear least-squares method to estimate the structural parameters. The estimation process is entirely explained in this paper, from initialization of the unknowns to retrievals. A sensitivity analysis is also done where the initial values in the inversion process are varying randomly. The results show that the inversion process is not really sensitive to initial values and a major part of the retrievals has a root-mean-square error lower than 5% for soil moisture, 24 Mg/ha for biomass and 0.49 cm for roughness, considering a soil moisture of 40%, roughness equal to 3cm and biomass varying from 0 to 500 Mg/ha with a mean of 161 Mg/ha.

Imaging and detection of objects in the presence of rough surfaces and random media are important current problems with applications in object detection near rough ocean surfaces and terrain, and remote sensing in geophysical and biological media. It has also been important to incorporate signal processing techniques to space-time imaging. This paper presents applications of several signal processing techniques to imaging in complex environments, including Time-Reversal MUSIC imaging in random media. Also discussed are the effects of multiple scattering on several other imaging techniques including Time-Reversal, Modified Beamformer, and SAR. In particular, we discuss the imaging of objects located near rough surfaces.

The thickness of Arctic sea ice plays a critical role in Earth's climate and ocean circulation. An accurate measurement of this parameter on synoptic scales at regular intervals would enable characterization of this important component for the understanding of ocean circulation and the global heat balance. Presented in this paper is a low frequency VHF interferometer technique and associated radar instrument design to measure sea ice thickness based on the use of backscatter correlation functions. The sea ice medium is represented as a multi-layered medium consisting of snow, sea-ice and sea water, with the interfaces between layers characterized as rough surfaces. This technique utilizes the correlation of two radar waves of different frequencies and incident and observation angles, scattered from the sea ice medium. The correlation functions relate information about the sea ice thickness. Inversion techniques such as the genetic algorithm, gradient descent, and least square methods, are used to derive sea ice thickness from the phase information related by the correlation functions. The radar instrument is designed to be implemented on a spacecraft and the initial test-bed will be on a Twin Otter aircraft. Radar system and instrument design and development parameters as well as some measurement requirements are reviewed. The ability to obtain reliable phase information for successful ice thickness retrieval for various thickness and surface interface geometries is examined.