Land surface temperature (LST) is a crucial variable in the Earth’s surface system, playing a key role in understanding the exchanges of material and energy between the surface and the atmosphere. Hyperspectral thermal infrared (TIR) data provide new opportunities for developing methods to retrieve LST from satellite observations. However, the typical physical hyperspectral TIR LST retrieval methods are limited by their reliance on accurate atmospheric correction and specific assumptions, which would introduce complexity and reduce applicability. To address these challenges, this study presents a novel LST retrieval framework that combines a Deep Residual Regression Network (DR2N) with the Iterative Spectrally Smooth Temperature and Emissivity Separation (ISSTES) algorithm, refined by Hampel filtering. In this framework, DR2N is trained on simulated data to efficiently retrieve atmospheric parameters, including upwelling radiance, downwelling radiance and transmissivity, and after that the refined ISSTES algorithm is applied to simulated data covering various surface types, yielding an overall RMSE of 1.89 K and a bias of -0.19 K. Subsequently, to further verify the performance of the proposed algorithm, LSTs over four study areas-Spain, North Africa, Hulunbuir, and the Yellow Sea are retrieved, and are compared with IASI L2 surface temperature products originating from the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), showing an RMSE of 0.51 K and a bias of -0.25 K. This confirms the efficacy of the proposed LST retrieval approach.

Land surface temperature (LST) is a critical indicator of the interactions between the Earth’s surface and atmosphere and has long been available from satellite observations in the thermal infrared (TIR) region. Recognized as a primary way to evaluate the accuracy of LSTs, in situ validation is still a challenging task because of uncertainties in ground measurements, spatial scale mismatch between ground and satellite-based measurements, the heterogeneity of natural land surfaces, etc., leading to a lack of consistency among sets of validation results; therefore, to improve robustness against uncertainties, an optimized approach for LST validation via the Kalman filter is presented, and prediction of comprehensive validation estimate (CVE) which is close to “true” value, and more precise than those based on a single measurement alone is obtained. After the uncertainties involved in the validation process are constrained, this method is applied to FengYun-3D (FY-3D)/Medium Resolution Spectral Imager II (MERSI-II) LSTs with ground measurements from four sites in China. The results indicate that the CVE is 1.11 K, with an uncertainty of 0.07 K. Additionally, a comparison is performed with the weighted average method, and the efficacy of the Kalman filter approach in enhancing the validation accuracy is confirmed.

The surface reflectance product is the most crucial and fundamental quantitative product in optical remote sensing, serving as the source for various land surface parameter products. This study initially conducts the retrieval of aerosol optical depth (AOD) from GF1/WFV data. The 6SV model, combined with dark object method and histogram matching, is utilized to achieve AOD inversion at a 16-meter resolution on a per-pixel basis. Subsequently, atmospheric correction is performed using the radiative transfer equation to obtain surface reflectance. The culmination of our efforts involved a comprehensive quantification of uncertainties throughout the production process of the proposed surface reflectance product, allowing for a robust assessment of its reliability, accuracy, and applicability. A thorough evaluation was conducted to ascertain the efficacy and potential of the proposed methodology.

Surface temperature (ST) is a vital physical parameter influencing surface-atmosphere interactions. This study presents an uncertainty-based validation approach applied to Sentinel-3/SLSTR land surface temperature (LST) and sea surface temperature (SST) products.In situmeasurements were obtained from various sites in China, namely, the Dunhuang Gobi site (DHGS), Huailai Guanting Reservoir site (HGRS), Wuliangsuhai Lake site (WLSLS) and Yantai Ocean site (YTOS). The spatial representativeness ofin situmeasurements at each site was assessed using available clear-sky and high-quality ASTER LST products from April 2000 to June 2023. The four sites exhibited high spatial homogeneity, demonstrating suitability for validating STs. Therefore,in situmeasurements from these homogeneous sites were used to validate the Sentinel-3/SLSTR ST products during the daytime and nighttime using a temperature-based method. The results showed that the root mean square error (RMSE) values are lower than 1.6 K, except for those at DHGS. Furthermore, since ground-based ST validation is affected by the coupled effects of surface and atmospheric characteristics, the validation results are different under different atmospheric and surface conditions. Consequently, assessing the consistency among multiple validation results becomes challenging. To address this issue, by assuming the independence of the validation samples, we propose a method for obtaining the key comparison reference value (KCRV) from multiple validation results based on Sentinel-3/SLSTR ST products. The KCRV is close to the ‘true’ value, indicating the high quality of the validation results. For the Sentinel-3A/SLSTR and Sentinel-3B/SLSTR LST products, the KCRVs are 1.91 K and 1.71 K, respectively, with corresponding uncertainties of 0.08 K and 0.08 K, respectively. Similarly, for the Sentinel-3A/SLSTR and Sentinel-3B/SLSTR SST products, the KCRVs are 0.78 K and 0.71 K, respectively, with uncertainties of 0.08 K and 0.07 K, respectively.

The Chinese Gaofen-1(GF-1) high resolution satellite loaded with four Wide Field of View (WFV) cameras provides observations with high temporal and spatial resolutions. However, the radiometric calibration accuracy of the GF1/WFV should be given when being used to monitoring the earth. In this study, radiometric calibration of the GF1/WFV was carried out first with automatic instrumented Baotou site. Then, the determined radiometric calibration coefficients were cross validated with the MultiSpectral Imager (MSI) onboard the Sentinel-2 satellite. The preliminary results show that the radiometric performances of the four WFV cameras are relatively stable with averaged relative difference less than -1.85%. The standard deviation of the radiometric calibration coefficients during the period from April 2019 to June 2020 is 0.0056, 0.0081, 0.0072, and 0.0076 with respect to the blue, green, red, and near infrared channel. The results of cross validation with Sentinel-2/MSI suggest that the averaged relative difference is -3.16%, -4.28%, -1.15%, and -3.22% with respect to the blue, green, red, and near infrared channel. The results of cross-validation demonstrate that radiometric calibration of the GF-1/WFV cameras using automatic instrumented Baotou site is feasible and operational. And, it is also essential and necessary to update the on-orbit radiometric calibration coefficients of GF-1/WFV cameras during its' entire lifetime for further quantitative application.

Millions of people are severely injured or killed in road accidents every year and most of these accidents are caused by human error. Fatigue and negative emotions such as anger adversely affect driver performance, thereby increasing the risk involved in driving. Research has shown that listening to the right kind of music in these situations can ameliorate driver performance and improve road safety. Context-aware music delivery systems succeed in delivering suitable music according to the situation through the process of music mood-mapping which identifies the mood of a song. Additionally, we can leverage the power of the cloud to enable crowd sensing of the mood-mapping of various songs and enhance the effectiveness of situation-aware music delivery for drivers. The cloud can be used to aggregate the crowd sensed music mood-mapping data and improve the effectiveness of music delivery by providing accurate mood-mappings from the aggregated data. Currently, context-aware music delivery systems consider only features from the song for music mood-mapping. In this paper, we propose a novel approach to music mood-mapping for drivers which also incorporates the social context of a driver including age, gender and cultural background to enhance the effectiveness of music delivery in context-aware music recommendation systems for drivers.

Driving is an integral part of our everyday lives, but it is also a time when people are uniquely vulnerable. Previous research has demonstrated that not only does listening to suitable music while driving not impair driving performance, but it could lead to an improved mood and a more relaxed body state, which could improve driving performance and promote safe driving significantly. In this article, we propose SAfeDJ, a smartphone-based situation-aware music recommendation system, which is designed to turn driving into a safe and enjoyable experience. SAfeDJ aims at helping drivers to diminish fatigue and negative emotion. Its design is based on novel interactive methods, which enable in-car smartphones to orchestrate multiple sources of sensing data and the drivers' social context, in collaboration with cloud computing to form a seamless crowdsensing solution. This solution enables different smartphones to collaboratively recommend preferable music to drivers according to each driver's specific situations in an automated and intelligent manner. Practical experiments of SAfeDJ have proved its effectiveness in music-mood analysis, and mood-fatigue detections of drivers with reasonable computation and communication overheads on smartphones. Also, our user studies have demonstrated that SAfeDJ helps to decrease fatigue degree and negative mood degree of drivers by 49.09% and 36.35%, respectively, compared to traditional smartphone-based music player under similar driving situations.

A reliable solution for the task of unicast video streaming over urban VANETs is of great demand. Single path solutions which address this topic are highly prone to collision at a high data rates which are necessary for high quality videos. Multipath solution solves this problem by distributing the heavy traffic load into a set of paths. Among numbers of multipath works, only 2-path LIAITHON+takes into consideration the high dynamic topology of VANETs, route coupling effect, and path length growth. In this paper, we make several improvements on top of the 2-path LIAITHON+. We evaluate the use of more than two paths in this multipath solution. Moreover, the impact of added redundancy on the both 2-path and 3-path LIAITHON+is investigated as a solution for packet loss.

The performance of the progressive compression and the transmission of images over Visual Sensor Networks (VSN) has been experimentally evaluated. This was performed through a real testbed based on Imote2 sensors and an IMB400 camera. In fact, by using the Progressive-JPEG (P-JPEG), an image was encoded into several scans. When the image is being sent, these scans are transmitted one by one, providing the observer with a gradual coarse-to-fine view of the image. This helps reducing the energy consumption of the whole network as well as the display time of the visualized image at the base station. For that purpose, a VSN testbed based on Imote2 and IMB400 nodes was developed. To provide P-JPEG compression capabilities and to support the IMB400 camera node, TinyOs applications were developed. Extensive sets of experiments were conducted to show the efficiency of P-JPEG over the baseline JPEG.

Transmitting video content over Vehicular Ad Hoc Networks (VANETs) faces a great number of challenges caused by strict QoS (Quality of Service) requirements and highly dynamic network topology. In order to tackle these challenges, multipath forwarding schemes can be regarded as potential solutions. However, route coupling will severely impair the performance of multipath schemes. In this work, we present a LocatIon-Aware multIpaTH videO streamiNg (LIAITHON) scheme to address video streaming over urban VANETs. LIAITHON uses location information to discover two relatively short paths with minimum route coupling effect. The performance results have shown it outperforms the underlying single path solution as well as the node-disjoint multipath solution.