Near surface air temperature (Ta) is one of the most critical variables in climatology, hydrology, epidemiology, and environmental health. In situ measurements are not efficient for characterizing spatially heterogeneous Ta, while remote sensing is a powerful tool to break this limitation. This study proposes a mapping framework for daily mean Ta using an enhanced empirical regression method based on remote sensing data. It differs from previous studies in three aspects. First, nighttime light data is introduced as a predictor (besides land surface temperature, normalized difference vegetation index, impervious surface area, black sky albedo, normalized difference water index, elevation, and duration of daylight) considering the urbanization-induced Ta increase over a large area. Second, independent components are extracted using principal component analysis considering the correlations among the above predictors. Third, a composite sinusoidal coefficient regression is developed considering the dynamic Ta-predictor relationship. This method was performed at 333 weather stations in China during 2001–2012. Evaluation shows overall mean error of −0.01 K, root mean square error (RMSE) of 2.53 K, correlation coefficient (R²) of 0.96, and average uncertainty of 0.21 K. Model inter-comparison shows that this method outperforms six additional empirical regressions that have not incorporated nighttime light data or considered predictor independence or coefficient dynamics (by 0.18–2.60 K in RMSE and 0.00–0.15 in R²).

Land surface temperature (LST) is crucial to wide varieties of environmental issues, whereas its low tolerance to cloud contamination greatly challenges its applications. Passive microwave (PMW) measurements are employed to retrieve LST due to its great capability of penetrating clouds. Despite of great efforts from previous studies, their further applications are limited by coarse resolution of PMW, uncertainty from cloudy coverage, and less attention on large-scale applications. To address these problems, this paper proposes a method combining adjustment from annual temperature circle to reconstruct land surface temperature based on downscaled microwave observations over mainland China. In order to conduct a comprehensive and solid accuracy evaluation, the proposed method is validated according to three strategies namely comparing with MODIS LST, ground surface temperature (GST), and in-situ observations from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) respectively. In the strategy 1, mean RMSE of predicted LST is 2.39 K and 1.33 K at day and night. According to the strategy 2, mean RMSE of MODIS, predicted and merged LST is 4.97 K, 5.07 K, and 4.93 K at daytime, and 2.32 K, 4.57 K and 3.39 K at nighttime. In the strategy 3, they are 3.75 K, 3.90 K and 2.87 K at day, and 2.80 K, 3.61 K and 2.79 K at night. Furthermore, adjustments from annual temperature cycle are discussed. The proposed method is promising in future research for reconstructing LST with high spatiotemporal resolution at large scales thanks to its simple process, high accuracy, and seamless reconstruction.

Satellite image time series change detection methods have become an effective means of obtaining information on land cover change. However, the temporal, spectral and spatial features and their derived features of land cover objects are of great importance for time series change detection. Existing studies have made insufficient use of these features, which may affect the results of land cover change detection. In order to fully integrate the above features to portray and represent change information, this study proposes a coupled temporal-spectral-spatial multidimensional information change detection framework (TSSF) method. Firstly, the derived index features are calculated to construct intra-annual temporal-spectral information to reduce the underutilization of spectral features. Secondly, the intra-annual temporal spectral information is extended to the spatio-temporal domain by the simple non-iterative clustering (SNIC) method and the SG filtering method to increase the exploitation of spatial features. Then, the value and shape based dynamic time warping method and the change vector analysis in posterior probability space (CVAPS) method are employed to obtain change information from the spectral, index, and class probability perspectives. Finally, the change type in the change region is obtained from the class probability information of the change magnitude according to the Bayesian criterion. Tianjin City was used as the study area to explore the land cover change from 1990 to 2020. The results show that the TSSF method is feasible in expressing temporal-spectral-spatial change information compared with existing methods, and is conducive to the efficient acquisition and identification of change areas and change types.

When employing gyroscope/magnetometer integration for vehicle heading estimation, the challenge of timevarying random magnetic interference in the environment frequently arises, resulting in reduced accuracy in heading estimation. To address this issue, this paper presents an approach to enhance vehicle heading estimation based on adaptive sliding window Factor Graph Optimization (FGO) for gyroscope/magnetometer integration. The method calculates the magnetometer angular velocity by taking the differential of the magnetometer heading. It introduces the difference between the magnetometer and gyroscope angular velocities as a feature to detect random magnetic interference, and applies variance threshold optimization to adaptively adjust the sliding window length. This ultimately achieves an optimal heading estimation within the sliding window. Experimental results demonstrate the effectiveness of the proposed algorithm in improving the accuracy of vehicle heading estimation in complex random magnetic interference environments.

In inertial measurement unit (IMU)/magnetometer integrated pedestrian positioning systems, the absolute heading from the magnetometer is severely degraded by pervasive magnetic interference, introducing uncertainty into the integrated solution and reducing overall positioning accuracy. Conventional ellipsoid-fitting methods for magnetometer calibration typically rely on minimizing the algebraic distance; however, they perform poorly under highly inhomogeneous data distributions. Moreover, the unknown relationship between heading-error statistics and magnetometer orientation in 3-D space can significantly degrade system performance. Addressing these challenges, this study proposes a data-driven progressive ellipsoid-fitting method to mitigate deterministic magnetic interference. In parallel, an adaptive extended Kalman filter (EKF) enhanced by a multilayer perceptron (MLP) models the statistical characteristics of heading errors in various spatial orientations. By dynamically adjusting the observation noise matrix in real time, the system effectively suppresses stochastic magnetic interference. This approach is especially suitable for foot-mounted pedestrian positioning, which typically exhibits limited motion degrees of freedom and only small rotational movements during normal use. Furthermore, it automatically estimates the observation noise level of IMU/magnetometer integrated positioning systems without any prior knowledge. The experimental results confirm that the proposed method delivers notable effectiveness in real-world scenarios.