For vibration monitoring applications, the wide-bandwidth low noise MEMS accelerometer with high efficiency is required. Conventionally, the sensor resonant frequency is increased to achieve wide bandwidth, which however trades off the sensor sensitivity and degrades system noise and power efficiency (FoM). To meet this challenge, this paper proposed a Microlevers Assisted Hybrid Damping (MAHD) system. Instead of increasing the resonant frequency, the MAHD system employs critical hybrid damping for bandwidth improvement, in order to avoid degradation of the system noise and efficiency. The critical hybrid damping is implemented with an interface circuit providing tunable electrostatic damping force. Asensor structure with microlever is employed to enhance the merit of the critical damping system. The interface circuit is fabricated by a commercial$0.18\mu $m CMOS process and the sensor is fabricated by a commercial surface micromachining process. The measurement results show that, without increasing the sensor resonant frequency, the proposed method improve the system 3dB-bandwidth and 5%-bandwidth by 454% and 550%, respectively. The noise floor is$68.33\mu $g/$\surd $Hz with$600\mu $A current consumption.

Land surface temperature (LST) products require rigorous validation before widespread application, and the spatial representativeness of ground validation sites plays a critical role in ensuring the reliability of validation results. Therefore, accurately assessing the spatial representativeness of ground sites is essential for credible validation outcomes. However, existing studies often focus on a small number of sites within confined regional observation networks, and generally evaluate representativeness at a single spatial scale. To address these limitations, this study conducts a comprehensive evaluation of 211 sites from five observation networks globally. In order to estimate representativeness across multiple spatial scales corresponding to typical LST products (i.e., 1 km, 3 km, 5 km, and 10 km), a novel spatial representativeness assessment model is proposed. This model, leveraging long-term Landsat LST data and the Random Forest method, quantifies the relationship between spatial representativeness error and spatial scale, enabling seamless spatial representativeness evaluations for each site. Based on this framework, 24 sites that demonstrate consistently high representativeness across all scales and seasons are identified as optimal validation sites. Furthermore, this study proposes two site selection strategies: one prioritizing temporal stability, which identifies 44 sites ensuring representativeness across all seasons, and the other emphasizing spatial coverage, which selects 38 sites to guarantee representativeness at different scales. These findings provide valuable guidance and references for future LST product validation efforts.

Land surface emissivity (LSE) is a key parameter for estimating longwave radiation of land surface, and mounts of the satellite-based LSE products have been released, generally coupled with Land surface temperature (LST) products. However, few research focus on validation of remote sensing LSE products, particularly over complex and heterogeneous mountainous surface. In this study, two-year field experiments designed for the LSE observation was implemented over typical mountainous regions of southwestern China, using a Model 102 hand-portable Fourier-transform Infrared (FTIR) spectrometer. Through an optimized sampling method, mixed pixel emissivity measurements were obtained to systematically evaluate the widely used Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 level-3 daily LSE products, including MOD/MYD11A1 and MOD/MYD21A1 and the Landsat8/9 Collection 2 level 2 LSE products. In this study, the Landsat LSE product shows slightly higher accuracy, with the mean Absolute Bias (Abs_Bias) of 0.0104, compared to MxD11A1 (Abs_Bias = 0.0118) and MxD21A1 (Abs_Bias = 0.0125). Between the two MODIS products, MxD11A1 tends to overestimate LSE with the mean Bias of 0.0115, while MxD21A1 shows a slight underestimation, with the mean Bias of –0.0022. Regarding sensor differences, MxD11A1 shows negligible discrepancies between Terra and Aqua platforms (Bias, Abs_Bias, RMSE < 0.0001) whereas MYD21A1 exhibits larger errors than MOD21A1, with the Abs_Bias and RMSE higher by 0.0029 and 0.0043, respectively, indicating that Terra products generally perform better than Aqua. For daytime and nighttime comparisons, MOD21A1 exhibits minor differences, with Abs_Bias values of 0.0112 and 0.0110, whereas MYD21A1 nighttime product performs better than daytime counterpart, with lower Abs_Bias (0.0122 vs. 0.0157) and RMSE (0.0149 vs. 0.0209). While the Landsat product achieves slightly better overall absolute accuracy, it exhibits spatial artifacts that result in underestimation in affected regions and slight overestimation in unaffected areas. These artifacts also limit its sensitivity to temporal variation. Overall, this study provides a reliable accuracy reference for MODIS and Landsat LSE products over complex and heterogeneous mountainous surfaces, supporting their application and the future improvement of product quality.

Spatiotemporally continuous Land Surface Temperature (LST) is crucial for monitoring extreme weather and providing disaster warnings. It captures abnormal temperature fluctuations, offering timely early warning and response for sudden climate events and natural disasters. However, cloud cover and satellite observation gaps often limit the spatial completeness of LST, while previous reconstruction methods seldom consider the effects of solar radiation and cloud cover on land surface temperature. To address these challenges, this study proposed the All-Weather Real Estimation (AWRE) method, which integrated thermal infrared and passive microwave data with environmental factors to estimate the LST under all-weather conditions. By incorporating deep learning and land surface energy balance models, and analyzing the impact of clouds on temperature fluctuations, the proposed method retrieves all-weather LST. Applied to the 2022 data of China, the AWRE method demonstrated high accuracy in estimating LST. The overall average RMSE and Bias were 2.90 K and 0.56 K, respectively, with daytime and nighttime RMSEs of 2.97 K and 2.83 K, respectively. Specifically, for daytime (nighttime) conditions, the RMSEs under clear sky were 2.94 K (2.58 K), partially cloudy 3.08 K (2.76 K), and fully cloudy 2.9 K (3.14 K). The estimated all-weather LST effectively captured diurnal and seasonal variations, with accuracy comparable to in-situ LST measurements, maintaining temporal continuity. This approach improves the detection of extreme heat events and addresses spatiotemporal coverage gaps, providing more accurate data for climate models, weather monitoring, and public health decisions.

Given the increasing severity of the Surface Urban Heat Island (SUHI) phenomenon, urban residents faced heightened heat stress. Consequently, mitigating the effects of SUHI became critically important to improve urban livability. However, there is a notable deficiency in research pertaining to the effects of SUHI on the health of urban populations. This study analyzed 717 cities globally to examine the interplay between SUHI and heat stress, including potential contributing factors. Our study revealed that arid cities were subjected to more intense thermal stress challenges, while equatorial cities exhibited a higher ratio of heat stress risk. Despite the absence of severe heat stress in cities with snow zone, there was a notable correlation between heat stress and SUHI, indicating a potential exacerbation of this issue in the future. This study provided key insights for precise urban climate adaptation and sustainable planning in cities.

Land surface temperature (LST) is crucial for the energy balance between the Earth’s surface and the atmosphere. Thermal infrared (TIR) and passive microwave (PMW) remote sensing are key methods for acquiring surface temperature globally and regionally. TIR observations have certain limitations due to their inability to penetrate cloud cover. Conversely, PMW measurements partially overcome this drawback to some extent, but their lower retrieval accuracy and coarse resolution limit its wider application. This study developed an artificial intelligence (AI) framework for precise and high-resolution LST estimation from PMW measurements, comprising PMW LST retrieval and downscaling components. Within this framework, high-resolution LST products have been obtained from Advanced Microwave Scanning Radiometer 2 (AMSR2), and the station-based validations and sensitivity analysis have also been conducted on the algorithm. The results were given as follows. First, the GeoFusionNet algorithm achieved higher LST retrieval accuracy than empirical or physical models. The mean absolute error (MAE) was 2.37 K (1.60 K) during daytime (nighttime). Second, the downscaled PMW LST retained high accuracy, with a daytime (nighttime) MAE increase of 0.28 K (0.14 K) compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km product. Station-based validations showed that the coefficient of determination$R^{2}$was above 0.9, with an average root-mean-squared error (RMSE) of 3.4 K (2.4 K) for daytime (nighttime) and an MAE of 2.80 K (1.98 K). Third, sensitivity analysis demonstrated the algorithm’s stable performance, especially in summer and autumn. Spatially, the accuracy remained within 3 K for various land types, including cropland, evergreen forests, and deciduous forests. These results indicate that PMW LST retrieved by this framework has sufficient accuracy and fine-spatial resolution for monitoring dynamic changes in large-scale hydrological, climatic, and agricultural fields.