Earth observation data with high spatiotemporal resolution are critical for dynamic monitoring and prediction in geoscience applications, however, due to some technique and budget limitations, it is not easy to acquire satellite images with both high spatial and high temporal resolutions. Spatiotemporal image fusion techniques provide a feasible and economical solution for generating dense-time data with high spatial resolution, pushing the limits of current satellite observation systems. Among existing various fusion algorithms, deeplearningbased models reveal a promising prospect with higher accuracy and robustness. This paper refined and improved the existing deep convolutional spatiotemporal fusion network (DCSTFN) to further boost model prediction accuracy and enhance image quality. The contributions of this paper are twofold. First, the fusion result is improved considerably with brand-new network architecture and a novel compound loss function. Experiments conducted in two different areas demonstrate these improvements by comparing them with existing algorithms. The enhanced DCSTFN model shows superior performance with higher accuracy, vision quality, and robustness. Second, the advantages and disadvantages of existing deeplearningbased spatiotemporal fusion models are comparatively discussed and a network design guide for spatiotemporal fusion is provided as a reference for future research. Those comparisons and guidelines are summarized based on numbers of actual experiments and have promising potentials to be applied for other image sources with customized spatiotemporal fusion networks.

Global warming-induced climate change evolved to be one of the most important research topics in Earth System Sciences, where remote sensing-based methods have shown great potential for detecting spatial temperature changes. This study utilized a time series of Landsat images to investigate the Land Surface Temperature (LST) of dry seasons between 1989 and 2019 in the Bac Binh district, Binh Thuan province, Vietnam. Our study aims to monitor LST change, and its relationship to land-cover change during the last 30 years. The results for the study area show that the share of Green Vegetation coverage has decreased rapidly for the dry season in recent years. The area covered by vegetation shrank between 1989 and 2019 by 29.44%. Our findings show that the LST increase and decrease trend is clearly related to the change of the main land-cover classes, namely Bare Land and Green Vegetation. For the same period, we find an average increase of absolute mean LST of 0.03 °C per year for over thirty years across all land-cover classes. For the dry season in 2005, the LST was extraordinarily high and the area with a LST exceeding 40 °C covered 64.10% of the total area. We expect that methodological approach and the findings can be applied to study change in LST, land-cover, and can contribute to climate change monitoring and forecasting of impacts in comparable regions.

Dynamic visualization of multidimensional hydrometeorological data is vital for decision-makers to catch situational awareness and command an emergency response in natural disasters. Nevertheless, few software tools can comprehensively visualize hydrometeorological data in different scales, dimensions, and time. In this paper, an interactive 4D spatio-temporal visualization system based on a virtual globe is proposed. Voxel-based data model and multi-level index are adopted to organize the field data in a unified data structure. Meanwhile, it is resampled in both spatial and temporal dimensions in memory to prepare smooth data stream for rendering. Ten field models, including large-scale volume rendering and adaptive streamline rendering, are accelerated and integrated to display the field data collaboratively. The profile analysis and eddy tracking improve user experience in interactively exploring specific scenes. The system is tested against both large-scale meteorological data in the atmosphere and small-scale hydrological data at the surface, using typhoon landfall and riverine flood, respectively. The results demonstrate the applicability and efficiency of the system to dynamically visualize hydrometeorological data.

Rapid advancements in Earth-observing sensor systems have led to the generation of large amounts of remote sensing data that can be used for the dynamic monitoring and analysis of hydrological disasters. The management and analysis of these data could take advantage of distributed information infrastructure technologies such as Web service and Sensor Web technologies, which have shown great potential in facilitating the use of observed big data in an interoperable, flexible and on-demand way. However, it remains a challenge to achieve timely response to hydrological disaster events and to automate the geoprocessing of hydrological disaster observations. This article proposes a Sensor Web and Web service-based approach to support active hydrological disaster monitoring. This approach integrates an event-driven mechanism, Web services, and a Sensor Web and coordinates them using workflow technologies to facilitate the Web-based sharing and processing of hydrological hazard information. The design and implementation of hydrological Web services for conducting various hydrological analysis tasks on the Web using dynamically updating sensor observation data are presented. An application example is provided to demonstrate the benefits of the proposed approach over the traditional approach. The results confirm the effectiveness and practicality of the proposed approach in cases of hydrological disaster.

Climate change including global warming is an important issue in Earth science research. Remote sensing technology has shown great promise for finding temperature changes in Earth. This study explored periods of temperature increase in Bacbinh district, Binhthuan province in Vietnam within 20 years from 1999 to 2019 using the remote sensing approach. The Land Surface Temperature (LST) is determined through the Normalized Difference Vegetation Index (NDVI), Land Surface Emissivity (LSE) and Brightness Temperature (TB) values of Landsat satellite image data. With an increase of LST in the mean of every year is relatively high, it can reach the uncomfortable hot threshold affecting the environment. The empirical research helps to determine the influence of climate change on the process of increasing surface temperature at Bacbinh area.