Description: Quantitative structural analysis is a useful approach for studying geologic structures. It is particularly important in remote and complex fold-thrust belts where outcrop data and high-quality seismic reflection images are challenging to obtain. In this study, we integrated terrestrial light detection and ranging (LiDAR), unmanned aerial vehicle (UAV), and Landsat 8 (L8) data to extract high-resolution topographic and surface geologic information and constrain interpretations of three-dimensional (3-D) seismic reflection data in the Hero Range of the Qaidam Basin (QB) in northwestern China. UAV images were used to obtain a digital elevation model (DEM) and to measure the orientation of sedimentary bedding. Terrestrial LiDAR data were used to generate high-resolution digital outcrops and to evaluate the accuracy of the UAV-based DEM. L8 images were used to distinguish different stratigraphic units. The random sample consensus (RANSAC) algorithm was adopted to ascertain the best-fit plane of bedding. The results show that UAV images can be used to construct a DEM with ${〈} 1 ,{text m}$ resolution and orthophotos with 0.15-m resolution. Collectively, these data improve the ability to identify and measure small exposures of bedding surfaces. The RANSAC algorithm improves the accuracy of measuring bedding orientations by removing erroneous selection points and facilitating the recognition of second-order variations in bedding orientation. The integrated analysis of remotely sensed and 3-D seismic data indicates that, of the three anticlines within the Hero Range, two are fault-propagation folds (the Shizigou and Youshashan anticlines) and one is associated with a pop-up structure (Ganchaigou anticline).

Description: Precipitation data at high spatio-temporal resolution are essential for hydrological, meteorological, and ecological research in local basins and regions. The coarse spatial resolution (0.25°) of Tropical Rainfall Measuring Mission (TRMM) 3B43 product is insufficient for practical requirements. In this paper, a multivariable geographically weighted regression (GWR) downscaling method was developed to obtain 1 km precipitation. The GWR method was compared with two other downscaling methods [univariate regression (UR) and multivariate regression (MR)] in terms of the performance of downscaled annual precipitation. Variables selection procedures were proposed for selecting appropriate auxiliary factors in all three downscaling methods. To obtain the monthly 1 km precipitation, two monthly downscaling strategies (annual-based fraction disaggregation method and monthly based GWR method) were evaluated. All analysis was tested in Gansu province, China with a semiarid to arid climate for three typical years. Validation with measurements from 24 rain gauge stations showed that the proposed GWR method performed consistently better than the UR and MR methods. Two monthly downscaling methods were efficient in deriving the monthly precipitation at 1 km. The former method faces the challenge of precipitation spatial heterogeneity and the derived monthly precipitation heavily depends on the annual downscaled results, which could lead to the accumulation of errors. The monthly based GWR method is suitable for downscaling monthly precipitation, but the accuracy of original TRMM 3B43 data would have large influence on downscaling results. It was demonstrated that the proposed method was effective for obtaining both annual and monthly TRMM 1 km precipitation with high accuracy.

Description: The spatiotemporal characteristics of the Asian monsoon over the Indus River basin are studied using the latest near real-time satellite precipitation estimates from the tropical rainfall measuring mission (TRMM) multisatellite precipitation analysis (TMPA). The TMPA data product (3B42RT V7) is used to analyze diurnal variability of the Asian monsoon over the study domain during January 2005 to December 2010. First, the spatiotemporal uncertainty of satellite estimates is systematically characterized by comparison to rain gauge observations using four standard error metrics, i.e., the Pearson correlation coefficient (CC), root-mean-square error (RMSE), mean absolute error (MAE), and relative bias (BIAS). Second, diurnal rainfall variability over selected regions is investigated by comparing rainfall patterns during premonsoon and monsoon seasons. The comparison and evaluation of satellite-based estimates and rain gauge data revealed significant correlation of 0.87 for the stations in the southwest and 0.63 in the northeast monsoon region. The results indicated TMPA underestimates over the intense monsoon region from $- bf{8}% $ to $- bf{25}% $ , while there is an overestimation over the southern region from 7% to 35%. This study improves understanding on the rainfall diurnal variations captured by the three hourly TMPA products during the April–June (premonsoon) and June–August (monsoon) over the extreme monsoon year 2010 versus the regular periods of 2005–2009 by investigating precipitation mean, frequency, and intensity, as well as the diurnal and semidiurnal cycles. A noticeable bimodal variation during the 2010 season showed an increase in rainfall associated with anomalous atmospheric conditions, causing catastrophic floods in Pakistan.

Description: A coarse resolution soil moisture product is downscaled to 1, 5, and 10 km using synthetic aperture radar (SAR) observations acquired over the east of the Netherlands. The combination of phased array L-band SAR (PALSAR) backscatter and VUA-NASA C-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture product is adopted to mimic the radar/radiometer setup as will be available from NASA’s soil moisture active passive (SMAP) mission. The validation of retrievals is based on measurements collected by a sparse network of 20 stations distributed across $50 times 75$ km study area selected as one of the key validation sites for the SMAP soil moisture products. Reasonable agreements between the measurements and soil moisture retrieved at 1-, 5-, and 10-km resolution are found that lead to coefficients of determination of 0.37, 0.36, and 0.36, respectively. The retrievals, however, severely overestimate the measured soil moisture, which is attributed to the well-known positive bias of the selected AMSR-E product. After bias-correction, root mean squared differences reach as low as ${bf 0.046};{bf m}^{bf 3};{bf m}^{bf - 3}$ for individual locations and are 0.067, 0.068, and ${bf 0.069};{bf m}^{bf 3};{bf m}^{bf - 3}$ on average for the soil moisture retrieved at 1-, 5-, and 10-km resolutions, respectively. These error levels do not satisfy SMAP’s targeted accuracy of ${bf 0.04};{bf m}^{bf 3};{bf m}^{bf - 3}$ , but the radar/radiometer setup as well as the characterization of the soil moisture conditions representative are not optimal. On the other hand, it is demonstrated that the sequence of soil moisture maps does captu- e valuable hydrological and hydrometeorological information.

Description: The estimation of the path delay due to water vapor is a crucial aspect for the calibration of the Doppler observables of a deep space probe. The advanced water vapor radiometer (AWVR) developed by the Jet Propulsion Laboratory (JPL, NASA) already proved its capability to accurately estimate the path delay during the entire Cassini mission. Here, from the AWVR measurements, a scalar sky status indicator ( SSI ) was developed as a criterion for selecting the radiometric path delay estimations in the orbit determination process. Results indicate that the use of such index allows a reduction of the range rate residual root mean square (rms).

Description: Recent interest by the research community in investigating Antarctica at low microwave frequency is stimulated by the availability of new satellite-borne radiometers. Special attention has been paid to the Dome C region of the East Antarctic Plateau, which was selected by the European Space Agency (ESA) as a calibration and validation test site for the soil moisture and ocean salinity (SMOS) mission. In order to support this mission and better characterize the site, several surface and airborne campaigns were conducted. Analysis of microwave measurements collected by ground-based radiometers at Dome C during the DOMEX experiments reveals that ice-sheet parameter-profiles have a significant impact on the microwave emission even at low frequencies. In order to assess this observation, a theoretical analysis of microwave emission was carried out using the multilayer dense medium radiative transfer theory under the quasi-crystalline approximation with coherent potentials and ice-sheet geophysical-parameter profiles (i.e., temperature, density, layering, and grain size) collected in the Dome C area. The electromagnetic model was used to fit the angular distribution of microwave observations collected at C- and L-bands at Dome C. The analysis identifies the variability in the snow density vertical profile as a major factor in determining the microwave signature of the snow emission at both L- and C-bands. A secondary role is played by the snow grain radius profile that appreciably influences C-band.

Description: A support vector machine (SVM), a machine learning technique developed from statistical learning theory, is employed for the purpose of estimating passive microwave (PMW) brightness temperatures over snow-covered land in North America as observed by the advanced microwave scanning radiometer (AMSR-E) satellite sensor. The capability of the trained SVM is compared relative to the artificial neural network (ANN) estimates originally presented in [16] . The results suggest that the SVM outperforms the ANN at 10.65, 18.7, and 36.5 GHz for both vertically and horizontally polarized PMW radiation. When compared against daily AMSR-E measurements not used during the training procedure and subsequently averaged across the North American domain over the 9-year study period, the root-mean-squared error (RMSE) in the SVM output is 8 K or less, while the anomaly correlation coefficient is 0.7 or greater. When compared relative to the results from the ANN at any of the six frequency and polarization combinations tested, the RMSE was reduced by more than 18%, while the anomaly correlation coefficient was increased by more than 52%. Furthermore, the temporal and spatial variability in the modeled brightness temperatures via the SVM more closely agrees with that found in the original AMSR-E measurements. These findings suggest that the SVM is a superior alternative to the ANN for eventual use as a measurement operator within a data assimilation framework.

Description: In this paper, we incorporate the cyclical terms in dense media radiative transfer (DMRT) theory to model combined active and passive microwave remote sensing of snow over the same scene. The inclusion of cyclical terms is crucial if the DMRT is used to model both the active and passive contributions with the same model parameters. This is a necessity when setting out on a joint active/passive retrieval. Previously, the DMRT model has been applied to active and passive separately, and in each case with a separate set of model parameters. The traditional DMRT theory only includes the ladder terms of the Feynman diagrams. The cyclical terms are important in multiple volume scattering and volume–surface interactions. This leads to backscattering enhancement which represents itself as a narrow peak centered at backward direction. This effect is of less significance in passive remote sensing since emissivity is relating to the angular integral of bistatic scattering coefficients. The inclusion of cyclical terms in first-order radiative transfer (RT) accounts for the enhancement of the double bounce contribution and makes the results the same as that of distorted Born approximation in volume–surface interactions. In this paper, we develop the methodology of cyclical corrections within the framework of DMRT beyond first order to all orders of multiple scattering. The active DMRT equation is solved using a numerical iterative approach followed by cyclical corrections. Both quasi-crystalline approximation (QCA)–Mie theory with sticky spheres and bicontinuous media scattering model are used to illustrate the results. The cyclical correlation introduces around 1 dB increase in backscatter with a moderate snowpack optical thickness of ${sim}text{0.2}$ . The bicontinuous/DMRT model is next applied to compare with data acquired in the Nordic Snow Radar Experiment (NoSREx) campaign- in the snow season of 2010–2011. The model results are validated against coincidental active and passive measurements using the same set of physical parameters of snow in all frequency and polarization channels. Results show good agreement in multiple active and passive channels.

Description: A single-frequency dielectric model for thawed and frozen Arctic organic-rich (80%–90% organic matter) soil was developed. The model is based on soil dielectric data that were measured over the ranges of volumetric moisture from 0.007 to ${0.573};text{cm}^{{3}}/text{cm}^{{3}}$ , dry soil density from 0.564 to $0.666;text{g/cm}^{3}$ , and temperature from 25 °C to $-mathbf{30}^{circ}mathbf{C}$ (cooling run), at the frequency of 1.4 GHz. The refractive mixing model was applied to fit the measurements of the soil’s complex refractive index (CRI) as a function of soil moisture, with the values of temperature being fixed. Using the results of this fitting, the parameters of the refractive mixing model were derived as a function of temperature. These parameters involve the CRIs of soil solids as well as bound, transient, and free soil water components. The error of the dielectric model was evaluated by correlating the predicted complex relative permittivity (CRP) values of the soil samples with the measured ones. The coefficient of determination ( $mathbf{R}^{mathbf{2}}$ ) and the root-mean-square error (RMSE) were estimated to be $mathbf{R}^{mathbf{2}}= mathbf{0.999}$ , $mathbf{RMSE} = mathbf{0.27}$ and $mathbf{R}^{mathbf{2}}= mathbf{0.993}$ , $mathbf{RMSE} = mathbf{0.18}$ for the real and imaginary parts of the CRP, respectively. These values are in the order of t- e dielectric measurement error itself. The proposed dielectric model can be applied in active and passive remote-sensing techniques used in the areas with organic-rich soil covers, mainly for the SMOS, SMAP, and Aquarius missions.