Search

Infrared (IR) observations from geostationary satellites have been widely used in estimating the precipitation with high spatiotemporal resolutions. Currently, it is acknowledged that single-channel IR observations are capable to extract the properties of cloud tops, while are insufficient for deeper vertical information. However, based on developments of multispectral IR imagers onboard geostationary satellites, distinguishing different precipitation regions with different vertical structures becomes possible, which has not been fully explored yet. Therefore, combining FengYun-4A/advanced geosynchronous radiation imager (FY-4A/AGRI) and global precipitation measurement/dual-frequency precipitation radar (GPM/DPR), we innovatively proposed frontal precipitation cloud index (FPCI) and saturated precipitation cloud index (SPCI) for IR signals by exploiting the characteristic responses of channel combinations to different precipitation systems. It is proven that multispectral IR observations are capable of responding to certain precipitation systems with different vertical structures. Meanwhile, IR imagers onboard geostationary satellites have significant advantages on tracking developing processes of precipitation systems with high spatiotemporal resolutions than microwave detectors onboard low-earth-orbit (LEO) satellites. In addition, this study is beneficial for impelling IR precipitation estimation to consider more radiation-based physical theory, especially for FY-4A official precipitation product. [ABSTRACT FROM AUTHOR]

The research of semantic segmentation based on unsupervised domain adaptation greatly alleviates the high-cost bottleneck of manual annotation in deep learning. Inevitably domain gap limits the ability of target domain to learn knowledge from source domain. Previous domain alignment strategies aim to explore maximum domain-invariant space to enlarge knowledge that target domain learns. They based on the assumption that images are symmetrical in categories covering all predefined categories. However, in practical training, there is class asymmetry in image samples inter domains. This will lead to outlier categories and negative transfer, because it lacks the guidance of the corresponding category of the source domain. To tackle this issue, we propose a partial domain adaptive method on semantic segmentation to guide target model to learn category-level knowledge selectively. PAM (Partial Adaptive Map) module is introduced to motivate the target model to obtain more knowledge from non-outliers and less knowledge from outliers to avoid negative transfer. We further analyze the effectiveness of this method from the perspective of JS divergence. Our method has achieved significant improvement without additional discriminators. Experiments on general datasets GTA5 and SYNTHIA can compare with SOTA methods. [ABSTRACT FROM AUTHOR]

Accurate and near-real-time rain information at fine scales is critical for forecasting local weather and floods. Traditional classical infrared (IR) cloud-top brightness temperature data alone do not contain sufficient precipitation-related information, and the introduction of visible (VIS) observations limits their applications to daytime. Methods for the efficient and comprehensive utilization of multichannel IR observations for accurately retrieving all-day near-real-time rain rates with consistent high quality warrant further exploration. In this study, we propose an all-day near-real-time quantitative precipitation estimation framework based on multispectral analysis (MSA) from the Advanced Geosynchronous Radiation Imager (AGRI) onboard Chinese FY-4 series satellites; the proposed framework is called FY4QPE- MSA. Multiple IR bands are comprehensively and efficiently considered by adopting the principal component analysis technique to reduce the dimensionality to a few independent features while preserving most of the variations. The main conclusions include, but are not limited to, the following aspects: 1) the MSA from IR channels provides valuable information that facilitates the more accurate delineations of the precipitation occurrences; 2) FY4AQPE -MSA outperforms FY4AQPE- Offical [ $\sim 20$ % gain in the Pearson correlation coefficient (CC), $\sim 25$ % gain in the root mean square error (RMSE), and $\sim 15$ % gain in the critical success index (CSI)], FY4AQPE-Single ($\sim 10$ % gain in CC, $\sim 15$ % gain in RMSE, and $\sim 15$ % gain in CSI), and Precipitation Estimation from Remotely Sensed Information using artificial neural network-cloud classification system (PERSIANN-CCS) ($\sim 30$ % gain in CC, $\sim 20$ % gain in RMSE, and $\sim 25$ % gain in CSI); and 3) compared with the international IR-based baseline precipitation product, i.e., PERSIANN-CCS, FY4AQPE-MSA demonstrates no spatial gaps over the Tibetan Plateau. In addition, the results of this study suggest that the proposed general framework is promising and applicable for Chinese FY-4 series satellites in generating all-day near-real-time rain-rate information. [ABSTRACT FROM AUTHOR]

In this article, we have comprehensively investigated the electrical properties of split-gate trench (SGT) power MOSFETs under mechanical strains. The static and dynamic electrical properties of SGT power MOSFETs under both uniaxial and biaxial strains are examined thoroughly. It is found that both uniaxial and biaxial compressive strains improve the drain current (${I}_{d}$), while uniaxial and biaxial tensile strains do the opposite. The influence of strain is correlated with drain voltage (${V}_{d}$), and biaxial strains are slightly more effective than uniaxial strains. The mechanical strains do not change the threshold voltage (${V}_{{\mathrm {th}}}$), breakdown voltage (BV), capacitances, and switching characteristics. The piezoresistance coefficients of the SGT power MOSFETs are evaluated. It is also proved that both biaxial and uniaxial compressive strains can effectively decrease the figure-of-merit (FOM) of SGT power MOSFETs. [ABSTRACT FROM AUTHOR]

Gridded precipitation products with fine resolutions and qualities are of great importance for understanding the global water–carbon-energy cycles at various spatiotemporal scales. Though continuous developments in Satellite Remote Sensing fields have been providing great strengths for measuring the precipitation from space, merging precipitation products from different sources, especially the gauge observations, is still the optimal way for obtaining high-quality precipitation data. Currently, the mainstream merging methods mainly focus on merging the rain rates without the considerations of rain events. In this study, we propose a new assumption that both rain events and rain rates should be considered in the merging procedures rather than only the rain rates. To meet our assumption, a morphology-based adaptive spatio-temporal merging algorithm (MASTMA) for combining various precipitation products is proposed, in which the morphology theory is first introduced to comprehensively consider the influences from both rain events and rain rates. The multisource and multiscale precipitation products including the gauge-based data (CPC-U, 0.5°, daily), the satellite-based data [Global Satellite Mapping of Precipitation by Moving Vector with Kalman (GSMaP-MVK), 0.1°, hourly; integrated multisatellite retrievals for global precipitation measurement late run (IMERG-LR), 0.1°, half-hourly], and the reanalysis data (ERA5-land, 0.1°, hourly), have been comprehensively considered in MASTMA for generating the final estimates (MASTMA-F, 0.1°, hourly) over the southeastern regions of the Mainland China in the periods from 2016 to 2019. The main conclusions include but are not limited to: 1) considerations on rain events contribute significantly to the final merged results, especially when eliminating false extreme values over the regions where precipitation is greatly overestimated; 2) the MASTMA could optimally integrate the advantages from multisource precipitation products with different resolutions, particularly from the perspective of the spatial distributions; and 3) the final merged estimates using MASTMA outperform the contemporary state-of-the-art precipitation products especially in terms of modified Kling–Gupta Efficiency (mKGE) and critical success index (CSI). Additionally, the results of this study suggest that MASMTA is a new promising merging approach with great robustness and applicability, and has the foreseeable potentials for the operational run to generate the optimal global merged precipitation products. [ABSTRACT FROM AUTHOR]

Based on a statistical analysis of the currently available 3-year on-orbit Cyclone Global Navigation Satellite System (CYGNSS) data (2017–2019), this study proposes a two-step calibration method to improve the accuracy of the CYGNSS-derived land surface reflectivity (SR) and the resulting soil moisture (SM) estimates. The method is designed for two purposes: the one is to correct the system errors of the SR estimates induced by the calibration of the CYGNSS Version 2.1 L1B data, and the other is to eliminate vegetation attenuation in the SR of the soil layer. Mean SR corrections of ~ −0.9 and 2.2 dB are achieved through the first and second steps of calibration, respectively. This resulted in better SM estimates compared with the Soil Moisture Active Passive (SMAP) product and the in situ measurements, i.e., improved correlations between the CYGNSS SR and the SMAP SM (from R = 0.46 to R = 0.74), improved correlations between the CYGNSS SR and the in situ SM (from R = 0.47 to R = 0.62), and between the CYGNSS SM and the in situ SM with the best R = 0.64. [ABSTRACT FROM AUTHOR]

The increasing scale of Structure-from-Motion is fundamentally limited by the conventional optimization framework for the all-in-one global bundle adjustment. In this paper, we propose a distributed approach to coping with this global bundle adjustment for very large scale Structure-from-Motion computation. First, we derive the distributed formulation from the classical optimization algorithm ADMM, Alternating Direction Method of Multipliers, based on the global camera consensus. Then, we analyze the conditions under which the convergence of this distributed optimization would be guaranteed. In particular, we adopt over-relaxation and self-adaption schemes to improve the convergence rate. After that, we propose to split the large scale camera-point visibility graph in order to reduce the communication overheads of the distributed computing. The experiments on both public large scale SfM data-sets and our very large scale aerial photo sets demonstrate that the proposed distributed method clearly outperforms the state-of-the-art method in efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Man-made reservoirs are key components of terrestrial hydrological systems. Identifying the location and number of reservoirs is the premise for studying the impact of human activities on water resources and environmental changes. While complete bottom-up censuses can provide a comprehensive view of the reservoir landscape, they are time-consuming and laborious and are thus infeasible on a global scale. Moreover, it is challenging to distinguish man-made reservoirs from natural lakes in remote sensing images. This study proposes a convolutional neural network (CNN)-based framework to recognize global reservoirs from Landsat 8 imageries. On the basis of the HydroLAKES dataset, a Landsat 8 cloud-free mosaic of 2017 was clipped for each feature (reservoir or lake) and was resized into 224 × 224 patches, which were collected as training and testing samples. Compared to other deep learning methods (Alexnet and VGG) and state-of-the-art traditional machine learning methods (support vector machine, random forest, gradient boosting, and bag-of-visual-words), we found that fine-tuning the pretrained CNN model, ResNet-50, could reach the highest accuracy (91.45%). Application cases in Kansas (USA, North America), Mpumalanga (South Africa, Africa), and Kostanay (Kazakhstan, Asia) resulted in classification accuracies of better than 99%, which showed the applicability of the proposed ResNet-50 model to the extraction of reservoirs from a vast amount of moderate resolution images. The framework that was developed in this paper is the first attempt to combine remote sensing big data and the deep learning technique to the recognition of reservoirs at a global scale. [ABSTRACT FROM AUTHOR]

An efficient algorithm is proposed for the stochastic responses of vehicle–bridge systems. The pseudo-excitation method is combined with the self-adaptive Gauss integration to handle the stochastic responses of vehicle–bridge systems, for which the statistic characteristics are calculated. The applicability and accuracy of the proposed hybrid numerical method are demonstrated by comparing the results with those from Monte Carlo simulation. Besides, nondimensional time is introduced to evaluate the stochastic responses of vehicles. [ABSTRACT FROM AUTHOR]