This work demonstrated that 75 fold-enhanced photocatalytic hydrogen production over SrTiO 3 /TiO 2 heterostructures by Au plasmon-enhanced electron-phonon decoupling to generate more amounts of energetic electrons for solar water splitting. Such Au modified SrTiO 3 /TiO 2 heterostructures were synthesized by a facile hydrothermal post-photoreduction method, consequently the hydrogen evolution rate is 467.3 μmol g À 1 h À 1 , which is 187 and 75 folds enhancement compared with TiO 2 and SrTiO 3 /TiO 2 samples, respectively. Based on systematic investigations, it is proposed that the internal electric field (IEF) between the interfaces of SrTiO 3 /TiO 2 and the enhanced nearfield amplitudes of localized surface plasmon (LSP) inhibit the recombination of photogenerated electrons and holes in the bulk and accelerate the interfacial transfer of charge carriers. Simultaneously, electron spin resonance (ESR) showed the change of Ti 3 + species in SrTiO 3 /TiO 2 microspheres, mirroring the energetic electron transfer process from Au NPs to SrTiO 3 / TiO 2 microspheres.

In order to realize the automatic and accurate recognition of shipwreck targets in side-scan sonar (SSS) waterfall images, a pipeline that contains feature extraction, selection, and shipwreck recognition, an AdaBoost model was constructed by sample images. Shipwreck targets are detected quickly by a nonlinear matching model, and a shipwreck recognition in SSS waterfall images are given, and according to a wide set of combinations of different types of these individual procedures, the model is able to recognize the shipwrecks accurately. Firstly, two feature-extraction methods suitable for recognizing SSS shipwreck targets from natural sea bottom images were studied. In addition to these two typical features, some commonly used features were extracted and combined as comprehensive features to characterize shipwrecks from various feature spaces. Based on Independent Component Analysis (ICA), the preferred features were selected from the comprehensive features, which avoid dimension disaster and improved the correct recognition rate. Then, the Gentle AdaBoost algorithm was studied and used for constructing the shipwreck target recognition model using sample images. Finally, a shipwreck target recognition process for the SSS waterfall image was given, and the process contains shipwreck target fast detection by a nonlinear matching model and accurate recognition by the Gentle AdaBoost recognition model. The results show that the correct recognition rate of the model for the sample image is 97.44%, while the false positive rate is 3.13% and the missing detection rate is 0. This study of a measured SSS waterfall image confirms the correctness of the recognition process and model.

Spatial and temporal variations in the vertical stratification of the troposphere introduce significant propagation delays in interferometric synthetic aperture radar (InSAR) observations. Observations of small amplitude surface deformations and regional subsidence rates are plagued by tropospheric delays, and strongly correlated with topographic height variations. Phase-based tropospheric correction techniques assuming a linear relationship between interferometric phase and topography have been exploited and developed, with mixed success. Producing robust estimates of tropospheric phase delay however plays a critical role in increasing the accuracy of InSAR measurements. Meanwhile, few phase-based correction methods account for the spatially variable tropospheric delay over lager study regions. Here, we present a robust and multi-weighted approach to estimate the correlation between phase and topography that is relatively insensitive to confounding processes such as regional subsidence over larger regions as well as under varying tropospheric conditions. An expanded form of robust least squares is introduced to estimate the spatially variable correlation between phase and topography by splitting the interferograms into multiple blocks. Within each block, correlation is robustly estimated from the band-filtered phase and topography. Phase-elevation ratios are multiply- weighted and extrapolated to each persistent scatter (PS) pixel. We applied the proposed method to Envisat ASAR images over the Southern California area, USA, and found that our method mitigated the atmospheric noise better than the conventional phase-based method. The corrected ground surface deformation agreed better with those measured from GPS.

The Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technique is an effective method to monitor snow depth. The detrended signal-to-noise ratio (dSNR) series is analyzed by Lomb–Scargle periodogram (LSP) to extract the characteristic frequency, which can be converted to the snow depth. However, the dSNR data are greatly affected by noise in the observation environment, which leads to the abnormal characteristic frequency and low accuracy of snow depth retrieval. In order to reduce the influence of noise and to ensure the correct extraction of the characteristic frequency, we present an improved adaptive retrieval method for multi-constellation retrieval scenario. Firstly, the dSNR sequences are decomposed adaptively into several Singular Spectrum Components (SSCs) with different frequency scales by Singular Spectrum Decomposition (SSD). Then, the corresponding SSCs are selected, according to the empirical scope of snow depth, to reconstruct the “pure” dSNR series. Finally, the reconstructed signals are analyzed by LSP to derive the characteristic frequency, in order to obtain the snow depth. The multi-GNSS observations of site SG27 (Alaska, USA) and site P351 from Plate Boundary Observation network in a representative period from winter 2019 to spring 2020 were used to validate the proposed method. The snow depths were estimated from individual signals, individual constellations and multi-GNSS combination using both the traditional and the improved methods. The experimental results show that compared with the traditional method, the snow depth trend of the improved method is more consistent with the measured snow depth trend, especially in the early stage of snowfall. Furthermore, the proposed method shows a universal applicability to various signals of GPS, GLONASS, Galileo and BDS and the retrieval accuracy of all signals are improved in different degrees. When using multi-GNSS combination signals, the mean bias and RMSE of multi-GNSS snow depth retrieval at site SG27 are improved from 4.6 and 6.2 cm to 4.2 and 5.4 cm, respectively. The mean bias and RMSE at site P351 are improved from 10.5 and 12.4 cm to 9.5 and 11.5 cm, respectively.

Land subsidence in Changzhou City in the central Yangtze River Delta of China poses a serious threat to the safety of the environment and infrastructures. Excessive groundwater withdrawal, rapid urbanisation and industrial activities contribute to land subsidence in this area. In this study, we used the multi-temporal InSAR (MT-InSAR) technique to describe the spatiotemporal characteristics of land subsidence in Changzhou. Twenty-five ENVISAT ASAR and 29 TerraSAR-X images acquired from 2004 to 2013 were used to determine the rate and temporal evolution of land subsidence. We used the ERA-Interim model instead of spatiotemporal filtering in traditional MT-InSAR to mitigate the atmospheric phase screen. The InSAR-derived results were evaluated by comparing data from three time series methods and different bands (C and X bands), and accuracy was validated through levelling surveys and GPS measurements. For three regions, a distinct subsidence pattern was observed in major industrial areas with a maximum subsidence rate of up to − 39.9 mm/year. We also characterised the spatiotemporal variations of land subsidence in major industrial areas in Changzhou. The deformation of large-scale man-made linear features, namely high-speed railways, highway networks and a bridge, was analysed. The spatiotemporal characteristics and possible reasons for the observed subsidence were discussed to provide a reference for future urban development planning in Changzhou.