Description: Merged satellite ocean color data were used to examine trends in the timing and magnitude of phytoplankton blooms. Special emphasis was placed on the peak shift of spring and autumn/winter blooms in the North Pacific and North Atlantic Oceans with bimodal seasonal cycles. Ensemble empirical mode decomposition and Holo-Hilbert spectral analysis were combined to extract seasonal signals and investigate their modulation at multiple time-scales. In the temperate North Atlantic Ocean, earlier and decreasing spring blooms were detected with delayed and increased autumn blooms. The temperate North Pacific Ocean presented delayed and increased spring and autumn blooms, with the delay in fall blooms was larger than spring ones. The separation between two bloom peaks was increasing in both temperate North Atlantic and Pacific regions. The intrinsic variation in bloom timing and magnitude in these selected regions could be clearly extracted by ensemble empirical mode decomposition. Further Holo-Hilbert spectral results showed that changes in the annual cycle and bloom characteristics were modulated by interannual variability. These results suggest the critical role of interannual variability in the modulation of phytoplankton seasonality.

Description: Leaf area index (LAI) is an important descriptor of many biological and physical processes of vegetation. However, the challenges associated with differentiating tree and shrub LAI (tsLAI) have hindered research in mixed forests. Being the first spaceborne LiDAR system, geoscience laser altimeter system (GLAS) has demonstrated its advantage in collecting extensive forest structure information. In this study, we aimed to estimate tsLAI in a mixed forest with GLAS. The refined Levenberg–Marquardt algorithm for Gaussian decomposition was implemented to decompose GLAS data into ground and multiple vegetation signals, within which it is hypothesized that each vegetation signal corresponds to a particular height layer. Subsequently, the height of each layer was extracted through the decomposed GLAS signals, and a height threshold method to distinguish trees from shrubs was developed. Then, a tsLAI-specific ratio defined as ground-to-total energy return of the GLAS signal was calculated, and tsLAI was predicted by a linear regression model established from field measurements and the ratio. Finally, a study site in Ejina, China, where the dominant species are Populus euphratica (tree) and Tamarix ramosissima (shrub) was used to calibrate and validate the methods. Compared with the field measurement LAI, GLAS-predicted LAI presented a high agreement in which R 2 , RMSE, and %RMSE of trees were determined to be 0.797, 0.087, and 19.176, respectively. In contrast, R 2 , RMSE, and %RMSE of shrubs were found to be 0.676, 0.081, and 21.825, respectively. Overall, our study provided a feasible and effective approach for estimating tsLAI with GLAS over a flat region.

Description: Many studies have demonstrated the efficient extraction of the spatial extent of urban areas from Defense Meteorological Satellite Program/Operational Linescan System imagery using a fixed thresholding technique. These studies may underestimate and overestimate the extents of small and large cities, respectively. To overcome this problem, a new intensity gradient (IG) and vegetation fractional coverage (VFC) method is developed for identifying cities or towns, principally based on the assumption that there is a border around a city at which the nighttime light intensity decreases sharply. Using this method, the spatial extents of urban areas for two of the biggest countries in the world, namely China and the United States, were extracted in 2010. The urban areas thus identified are compared with the urban areas interpreted from Landsat Thematic Mapper imagery, and the results show that there is a significant linear relationship between the former and latter areas. This demonstrates that the IG/VFC model is effective for efficiently extracting the extent of urban areas from nighttime light imagery.

Description: Maximum light use efficiency ( ${text{LUE}}_{rm{max}}$ ) is an important parameter in biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable crop ${text{LUE}}_{rm{max}}$ from satellite remote sensing data. ${text{LUE}}_{rm{max}}$ at the plot level was retrieved first by assimilating field measured green leaf area index and biomass into a crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. ${text{LUE}}_{rm{max}}$ was then mapped using the best regression model from a VI. The influence factors on ${text{LUE}}_{rm{max}}$ variability were also assessed. Contrary to a fixed ${text{LUE}}_{rm{max}}$ , our results suggest that ${text{LUE}}_{rm{max}}$ is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level ${text{LUE}}_{rm{max}}$ and VIs, particularly the two-band enhanced vegetation index for winter wheat ( Triticum aestivum ) and the green chlorophyll index for maize ( Zea mays ) at the milk stage, provided a potential to deri- e ${text{LUE}}_{rm{max}}$ from remote sensing observations. To evaluate the quality of ${text{LUE}}_{rm{max}}$ derived from remote sensing data, biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant ${text{LUE}}_{rm{max}}$ and the derived variable ${text{LUE}}_{rm{max}}$ . Significant improvements in biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable ${text{LUE}}_{rm{max}}$ . This study offers a new way to derive ${text{LUE}}_{rm{max}}$ for a specific PEM and to improve the accuracy of biomass estimation using remote sensing.

Description: We propose a strategy to evaluate the performance of a radar sounder for the subsurface exploration of the Europa icy crust and, in particular, the possibility to detect liquid water at the base of the ice shell. The approach integrates the information coming from experimental measurements of the dielectric properties of icy materials, thermal models related to different crustal scenarios, and numerical simulations of radar signal propagation. The radar response has been evaluated in terms of cumulative attenuation, signal-to-noise ratio (SNR), and reflectivity. Our simulations indicate that a subsurface radar operating at 9 MHz can identify shallow-buried targets and to detect the ice/water interface in various thermal scenarios. Under our assumptions the ice/water interface can be detected almost down to a depth of 15 km under a conductive ice shell, whereas for a convective ice shell, the maximum depth is about 12 km (in the cold downwelling plume). We also discuss the possibility to detect shallow targets associated with interfaces between pure water ice and $text{MgSO}_{4} cdot 11 text{H}_{2}{rm O}$ ice mixtures at various salt contents, using the data of laboratory dielectric measurements.

Description: Features are of great importance for synthetic aperture radar (SAR) imagery terrain classification, but low-level features usually readily suffer from the speckle noise and they are incapable or inaccurate to capture some complex and irregular texture structure. In this paper, a novel feature learning framework is proposed to address this problem, in which some mid-level and high-level features are simultaneously learned by exploiting the spatial context constraints and sparse priors. More specifically, the mid-level features served as the intermediates are extracted from several initialized low-level features by the spatial constraints to reduce the influence of the speckle noise. Then, more abstract and discriminative high-level features are learned with an effective dictionary learning algorithm so as to represent the complex structures in SAR imagery. Finally, both artificial synthesis and real SAR imagery are utilized to verify the effectiveness of the proposed framework. It is demonstrated from both quantitative evaluations and visual results that the proposed algorithm performs better than other compared algorithms and the learned high-level feature is robust to the speckle noise and can improve the classification performance.

Description: Due to Earth’s rotation and an elliptical satellite orbit, large Doppler centroids along satellite orbit inevitably occur in geosynchronous Earth orbit synthetic aperture radar (GEOSAR). Nonzero Doppler centroid causes a large range migration, which complicates the data acquisition and design of imaging algorithms. Thus, beam steering is used to decrease the centroid. At the same time, the ground observation of interest is prerequisite for applications. Thus, a unique two-dimensional (2-D) beam-steering method to simultaneously consider the reduction of Doppler centroid and ground observation for the GEOSAR is studied. The minimum-Doppler plane is proposed to minimize the centroid and to guarantee the beams that illuminate the area of interest. Subsequently, to achieve required ground coverage, beam directions determined by the minimum-Doppler plane are slightly adjusted. The method has been validated through the simulation of two types of orbits.

Description: Quantitative understanding of stress transfer between major fault systems can elucidate the kinematics of large-scale plate interactions. This study analyzed right-lateral strike-slip motion on the North Tabriz fault (NTF) in an area where this structure appears to transition into a thrust fault known as the North Mishu fault (NMF). These faults play an important but cryptic role in accommodating stress related to the Arabia-Eurasia plate collision. We analyzed regional velocity vectors from permanent and temporary GPS arrays to estimate changes in fault-parallel and fault-normal slip rates in the transition zone between the NTF and NMF. Independent of its compressional motion, the NMF exhibits a dextral strike-slip rate of ∼2.62 mm/yr. Along the NTF, the right-lateral slip rate decreases and the vertical slip rate on increases at rates of 0.08 and 0.38 mm/yr km, respectively, as the NTF approaches the NMF. This study also used small baseline (SBAS) PS-InSAR results to reveal a NE-SW-striking reverse fault and a developing syncline hidden beneath the Tabriz Basin. Additionally, we calculated the vertical displacement rates using horizontal vectors from the GPS data and mean line-of-sight rate estimates from the SBAS data. While the study area does not express large-scale extrusion, such as that observed in the Anatolian Plate, the transformation of strike-slip motion into thrusting and crustal shortening along the NMF-NTF fault zone accommodates most of the N–S compression affecting the northwestern Iranian Plateau. In this region, small-sized, right-lateral strike-slip faults, and other folded structures form horsetail features. These dispersed structures accommodate eastward extrusion of the northwestern Iranian Plateau.

Description: Conventional motion compensation (MOCO) under beam-center approximation is usually insufficient to correct severe track deviations for high-resolution synthetic aperture radar imaging. In this paper, a novel MOCO approach is developed for correction of the azimuth-variant motion errors by exploiting a precise angle-to-Doppler relationship within subapertures. The corruption from the residual motion errors to the angle-to-Doppler mapping is investigated and overcome by a compensation scheme of the scaled Fourier transform. Inheriting the high efficiency, the proposed azimuth MOCO approach has dramatically improved precision over the conventional subaperture MOCO method by reducing high side-lobe peaks of the point spread function. Extensive comparisons with other MOCO algorithms are given to show the superiority of the proposed algorithm. Moreover, real-data experiments are provided for a clear demonstration of our proposed approach.

Description: The development of interferometric methodologies for deformation monitoring that are able to deal with long time series of synthetic aperture radar (SAR) images made the detection of seasonal effects possible by decomposing the differential SAR phase. In the case of monitoring of man-made structures, particularly bridges, the use of high-resolution X-band SAR data allows the determination of three major components with significant influence on the SAR phase: the linear deformation trend, the height of structures over terrain, and the thermal expansion. In the case of stable metallic or (reinforced) concrete structures, this last effect can reach a magnitude comparable to or even exceeding the other phase components. In this review, we present two case studies that confirm the feasibility of InSAR techniques for bridge deformation monitoring and our original approach to refine the thermal expansion component.