To reconstruct Moderate Resolution Imaging Spectroradiometer (MODIS) band reflectance with optimal spatiotemporal continuity, three bidirectional reflectance distribution function (BRDF) models—the Ross-Thick-Li-Sparse Reciprocal (RTLSR) model, Gao model, and adjusted BF model—were used to retrieve MODIS-band reflectance for cloudy MODIS pixels according to different inversion conditions with a proposed filling algorithm. Then, a spatiotemporally continuous MODIS-band reflectance dataset for most of Asia with more than 98% spatiotemporal coverage was reconstructed from 2012 to 2015. The validation highlighted an evident improvement in filling cloudy MODIS observations; a reasonable spatial distribution, such as in South Asia and Southeast Asia; and acceptable precision for the filled MODIS pixels, with the root mean square error percentage (RMSE%) at 9.7–9.8% and 12–16% for the Gao and adjusted BF models, respectively. In the course of reconstructing the spatiotemporal continuous MODIS-band reflectance, the differences among the three models were discussed further. For a 16-day period with a stable and unchanged land surface, the RTLSR model, as a basic model, accurately derived land surface reflectance (no more than 10% RMSE% for MCD43C1 V006 band 1) and outperformed the other two models. When the inversion period is sufficiently long (e.g., 108 days, 188 days, 268 days, or a full year), the Gao/adjusted BF model provides better precision than the RTLSR model by considering the normalized difference vegetation index (NDVI) and soil moisture/NDVI as intermediate variables used to adjust the BRDF parameters in real time. The Gao model is optimal when the inversion period is sufficiently long. Based on combining the RTLSR model and Gao/adjusted BF model, we proposed a filling algorithm to derive a dataset of MODIS-band reflectance with optimal spatiotemporal continuity.

Surface water bodies exhibit high dynamic variability on seasonal and interannual scales, and high spatiotemporal resolution water bodies extent data are crucial for studying surface water bodies’ evolution. Existing surface water bodies datasets are mainly based on optical data acquisition, which has the advantages of long temporal coverage and convenience but is susceptible to cloud contamination, leading to low spatiotemporal continuity. Although microwave remote sensing data are not affected by clouds, early SAR acquisition and short temporal coverage limit its use. Therefore, existing surface water bodies datasets face the problem of insufficient spatiotemporal resolution or low continuity. This research integrates Sentinel-2 optical data and Sentinel-1 Synthetic Aperture Radar (SAR) observations to reconstruct the surface water bodies dataset with a 6-day and 10-meter spatiotemporal resolution. Then, the proposed method introduces a spatiotemporal correlation model and predicts the land cover (water or land) of Sentinel-2 cloudy pixels, which improves the spatiotemporal continuity of the reconstructed surface water bodies dataset further. Based on the proposed method, we construct the Haihe River Water Dataset (HRWD) from 2016 to 2020 with a 6-day and 10-meter spatiotemporal resolution. Compared with the European Commission’s Joint Research Centre’s (JRC’s) Global Surface Water Explorer and Global Surface Water Extent Dataset (GSWED), the HRWD shows a rational accuracy (e.g., the overall accuracy of the HRWD is more than 93%) and a better spatiotemporal continuity, which provide an improved performance in identifying and monitoring surface water bodies in the Haihe River Basin. This indicates that the proposed method can improve the spatiotemporal continuity of surface water body mapping and meet the needs of accurate and long-term quantitative observation of the distribution of large-scale and high spatiotemporal continuity surface water bodies.

Recently, cloud computing based on virtualization technology has developed rapidly. A cloud computing provider offers services mainly by renting virtual machine (VM) to users, through which users can share various computing, storage and network resources. However, VM consolidation in cloud computing environments leads to significant degradation of the network performance of VMs, such as a huge drop in throughput and simultaneously a severe increase in latency and jitter. In this paper, we provide a deep insight into the relationship between VCPU scheduling and I/O congestion in Xen, particularly in data receiving direction. Through experiments, we observe that the VCPU scheduling plays a key role in the network performance of VMs due to the congestion in I/O ring. Then we propose a novel mechanism called Diff-Scheduler. It can identify the so-called NSVM (Network Queuing Sensitive Virtual Machine) that is more vulnerable to the congestion in I/O ring. Furthermore, it can schedule the VCPUs of the NSVMs more frequently than other VMs in different pools independently, without violating the CPU share fairness in VM consolidation. Evaluations based on a prototype of Xen platform featured Diff-Scheduler show that our mechanism significantly improves the network performance of VMs. Specifically, Diff-Scheduler proposed in this paper jointly enhances throughput, latency, and fairness of resource allocation.

Introduction: To address the spatiotemporal discontinuities in Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance time series caused by cloud contamination, snow cover, and sensor limitations, this study proposes an an optimized RTLSR inversion strategy with dynamiclly adjusted of multi-surface parameters.Methods: The method specifically aims to improve surface reflectance reconstruction accuracy in seasonally snow-covered regions and regions with significant vegetation phenological changes. To enhance the quality control of input data, the conventional NDVI threshold-based snow masking approach was replaced with the more rigorous “Internal Snow Mask” from the MOD09GA product. Additionally, vegetation indices exhibiting higher saturation resistance—namely the Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI)—were adopted in place of NDVI to better characterize surface reflectance variations during significant phenological transitions.Results: Experiments conducted in East and South Asia show that in seasonally snow-covered regions (e.g., eastern Tibetan Plateau and parts of northern Asia), RMSE reductions of 5.8%–7.1% are achieved in visible bands (Band1, Band3, Band4). Across the entire study area, the average RMSE across all MODIS bands (Band1–7) is reduced by 4.5%, with notable improvements in vegetation-sensitive near-infrared bands: Band2 and Band5 exhibit RMSE decreases of 14.3% and 6.3%, respectively. Compared with the MCD43A1 product, the proposed method demonstrates superior spatiotemporal continuity in mid- to low-latitude monsoon regions during summer and autumn, achieving a 9.77% increase in annual data availability.Discussion: These results indicate that the improved approach effectively fills gaps in surface reflectance time series in persistently cloudy regions and offers a reliable complementary solution to existing MODIS products.