Search

In the global precipitation measurement (GPM) era, the integrated multisatellite retrievals for GPM (IMERG) stands as a pivotal precipitation algorithm. This study aims to evaluate the accuracy of IMERG in capturing precipitation frequency and volume in the Yellow River Basin, China. Four satellite-based precipitation estimates (SPEs) from three algorithmic versions of IMERG were analyzed on an hourly scale using gauge observations. An improved error component procedure was employed to identify error sources. Results showed that all four IMERG products effectively captured the spatial distribution patterns and seasonal changes of precipitation, IMERG_F demonstrated the best overall performance, followed by IMERG_L, while IMERG_E performed the worst. However, they tended to overestimate precipitation. IMERG_F_Cal performed the best for precipitation frequency detection, with the highest probability of detection (POD = 52.7%) and the lowest missed events (MIS = 47.3%). Error component analysis highlights false bias as the main source of error, followed by missed bias. In winter, missed bias was the primary error source. Notably, a significant overestimation of precipitation was observed along the Yellow River. In detail, false bias dominated IMERG_E, IMERG_L, and IMERG_F_UnCal below 800 m in spring, summer, and autumn. However, in winter, missed bias became the primary error source for these three products at elevations above 200m and for IMERG_F_Cal above 500 m. IMERG_F_Cal exhibited false bias as the primary error source in all seasons. Suggested algorithm developers focus on improving IMERG SPEs' identification capabilities for light precipitation events and rainstorms. Findings can provide a reference for improving the IMERG product algorithms and enhancing users’ understanding of the error characteristics and sources of IMERG products.

Land surface emissivity (LSE) has been roughly treated in the current split-window (SW) land surface temperature (LST) retrieval algorithms. This article extended the National Oceanic and Atmospheric Administration Joint Polar Satellite System enterprise algorithm to Feng Yun-4A/Advanced Geostationary Radiation Imager (AGRI) thermal infrared data by incorporating a daily LSE database for high-temporal-resolution LST retrieval. To improve the retrieval accuracy, day/night SW algorithm coefficients were calculated for different total water vapor content and view zenith angle conditions using a simulation database constructed by moderate spectral resolution atmospheric transmittance model version 5.2 and SeeBor V5.0 atmospheric profiles. The validation results show that the daily AGRI LSE has better accuracy than the LSE retrieved from the vegetation cover method (VCM), with average biases of −1.1×10−3 and −6×10−3 for channels 12 and 13. The accuracy of the AGRI LST retrieved using the daily AGRI LSE is slightly better than that retrieved using the VCM-retrieved LSE. The overall bias, MAE, and root mean square error of the AGRI LST retrieved using the daily AGRI LSE at 14 in situ sites are 0.11, 2.55, and 2.55 K, respectively, whereas these values are −0.11, 2.70, and 2.70 K, respectively, for the LST using the VCM-retrieved LSE. This study demonstrates that the daily LSE constructed from physically retrieved LSE can improve the accuracy of LST retrieved with the SW algorithm. The constructed daily LSE has high spatial coverage and dynamic emissivity information and can provide nearly complete spatial coverage if supplemented by the constructed eight-day or monthly AGRI LSE. It can also be applied to other LST retrieval algorithms that need LSE a priori.

Having a good knowledge of the uncertainty in the land surface temperature (LST) product will help to encourage its use in a wide number of applications, including urban heat islands, geothermal detection, and surface energy balance. Landsat 9 was launched on 27 September 2021 and provides an LST product, which is generated by the radiative transfer equation algorithm and has a spatial resolution of 30 m. In this article, we evaluated the performance of the Landsat 9 LST product by using a temperature-based (T-based) method and cross-validation. The T-based validation results showed that the average bias at the surface radiation budget network and baseline surface radiation network sites was 0.24 K and that the corresponding root mean square error (RMSE) was 3.42 K. The Landsat 9 LST product was in good agreement with the Landsat 7/8 LSTs, with an average bias of 0.25/0.08 K, an RMSE of 0.51/1.04 K, and a mean absolute error of 0.38/0.64 K. The comparable performance of the Landsat 7/8/9 LST products can be explained by the consistent LST retrieval algorithm. The absolute differences in the LST between Landsat 9 LST and MOD11 (MOD21) LST images were between 0.01 (0.65) and 2.50 K (1.76 K), whereas the RMSE values were between 1.40 (1.80) and 3.65 K (3.26 K). The specific heat capacity and thermal inertia of the different land surface covers can explain the significant biases. The above evaluation results are consistent with the initial performance testing of thermal infrared sensor-2 (TIRS-2) by the National Aeronautics and Space Administration and the U.S. Geological Survey. Although the released Landsat 9 LST product showed good performance in the preliminary evaluation, the split-window algorithm may be a better option for Landsat 9 LST retrieval, as the TIRS-2 data addressed stray light incursion. Since there are no official validation results that have been published, this article provides a third-party performance evaluation of the Landsat 9 LST product and will benefit research fields that require Landsat series LST products.

First-order Marcum Q -function is observed in various problem formulations. However, it is not an easy-to-handle function. For this reason, in this article, we first present a semi-linear approximation of the Marcum Q -function. Our proposed approximation is useful because it simplifies, e.g., various integral calculations including Marcum Q -function as well as different operations such as parameter optimization. Then, as an example of interest, we apply our proposed approximation approach to the performance analysis of predictor antenna (PA) systems. Here, the PA system is referred to as a system with two sets of antennas on the roof of a vehicle. Then, the PA positioned in the front of the vehicle can be used to improve the channel state estimation for data transmission of the receive antenna that is aligned behind the PA. Considering spatial mismatch due to the mobility, we derive closed-form expressions for the instantaneous and average throughput as well as the throughput-optimized rate allocation. As we show, our proposed approximation scheme enables us to analyze PA systems with high accuracy. Moreover, our results show that rate adaptation can improve the performance of PA systems with different levels of spatial mismatch.

Incorporation of connected vehicle (CV) data into real-time traffic management systems presents a host of new challenges resulting from the current lack of data integrity and data privacy in traffic networks. Over the past few years, blockchain technologies have been inspiring extensive innovations in the transportation field. However, due to the transparency property, sensitive data stored on the blockchain would be accessible to anyone, resulting in a lack of privacy. In this paper, we propose a decentralized and location-aware architecture to address the data integrity along with the privacy-preserving issues in blockchain-based traffic management systems. Our proposed architecture integrates with permissioned and modular blockchain network and non-interactive zero-knowledge range proof (ZKRP) protocol. We develop the prototype system on the Hyperledger Fabric platform and Hyperledger Ursa cryptographic library. The performance results show that our approach is effective and feasible for real-time traffic management while preserving the data privacy requirements.

Autonomous vehicles are capable of sensing their environment and navigating without any human inputs. However, when autonomous vehicles are involved in accidents between themselves or with human subjects, liability must be indubitably decided based on accident forensics. This paper proposes a blockchain-inspired event recording system for autonomous vehicles. Specifically, we design the mechanism of “Proof-of-Event” with a dynamic federation consensus to achieve indisputable accident forensics by providing trustable and verifiable event information. We propose a dynamic federation consensus scheme to verify and confirm the new block of event data in an efficient way without any central authority. We conduct numerical analyses and prototyped experiments based on the proposed fast leader election algorithm and the Hyperledger Fabric blockchain network. The results show that our system is effective and feasible in generating and storing accident records in blockchain-based vehicular networks. The security capability of the proposed scheme is also discussed against multiple threats and attack scenarios.

The accurate state of charge (SoC) online estimation is a significant indicator that relates to driving ranges of electric vehicles (EV). The relationship between open circuit voltage (OCV) and SoC plays an important role in SoC estimation for lithium-ion batteries. To compare with the traditional incremental OCV (IO) test and the low current OCV (LO) test, a novel OCV test which combines IO test with LO test (CIL) is proposed in this paper. Based on the reliable parameters online identification of the dual polarization (DP) battery model, two SoC estimation algorithms are compared on the accuracy, robustness and convergence speed for the entire SoC region. Meanwhile, the comparative study of the three OCV-SoC relationships fits by the corresponding OCV tests is discussed in terms of the SoC online estimation under various temperatures. The results show that the adaptive extended Kalman filter (AEKF) algorithm can better improve the accuracy and robustness of SoC estimation than that of the extended Kalman filter (EKF) algorithm. Most importantly, the OCV-SoC relationship obtains from the CIL OCV test method is applied to the AEKF algorithm, which has higher accuracy and better statistical indices of SoC estimation, especially suitable for the low temperature.

Deep learning-based methods based on convolutional neural networks (CNNs) have demonstrated remarkable performance in hyperspectral image (HSI) classification. Most of these approaches are only based on 2-D CNN or 3-D CNN. It is dramatic from the literature that using just 2-D CNN may result in missing channel relationship information, and using just 3-D CNN may make the model very complex. Moreover, the existing network models do not pay enough attention to extracting spectral-spatial correlation information. To address these issues, we propose a deep collaborative attention network for HSI classification by combining 2-D CNN, and 3-D CNN (CACNN). Specifically, we first extract spectral-spatial features by using 2-D CNN, and 3-D CNN, respectively, and then use a “NonLocalBlock” to combine these two kinds of features. This block serves as a typical spatial attention mechanism, and makes salient features be emphasized. Then, we propose a “Conv_Block” that is similar to the lightweight dense block to extract correlation information contained in the feature maps. Finally, we consider a deep multilayer feature fusion strategy, and thereby combine the features of different hierarchical layers to extract the strong correlated spectral-spatial information among them. To test the performance of CACNN approach, several experiments are performed on four well-known HSIs. The results are compared with the state-of-the-art approaches, and satisfactory performance is obtained by our proposed method. The code of CACNN method is available on Dr. J. Liu's GitHub.

To solve the problem of trajectory tracking control for the underactuated unmanned underwater vehicle (UUV) in external current disturbance, a back-stepping sliding mode control method with double-loop structure is proposed in this paper. The outer-loop controller constructs the position error sliding surface through desired position error, obtains the kinematics control law by combining with power reaching law, and gets the error of virtual velocity after completing the design of trajectory tracking kinematics controller. The inner-loop controller constructs the sliding surface of longitudinal, horizontal, and vertical velocity error, respectively, with the help of the UUV speed error. Also, the trajectory tracking dynamic controller is designed by combining with the exponential reaching law, where the control moment is almost chattering-free. The stability of the double-loop control system is proved by combining with the Lyapunov stability theory. Finally, the simulation experiments verify the high effectiveness of the algorithm.