Understanding the spatiotemporal dynamics of global urbanization over a long time series is increasingly important for sustainable development goals. The harmonized nighttime light (NTL) time-series composites created by fusing multi-source NTL observations provide a long and consistent record of the nightscape for characterizing and understanding global urban dynamics. In this study, we generated a global dataset of annual urban extents (1992–2020) using consistent NTL observations and analyzed the spatiotemporal patterns of global urban dynamics over nearly 30 years. The urbanized areas associated with locally high intensity human activities were mapped from the global NTL time-series imagery using a new stepwise-partitioning framework. This framework includes three components: (1) clustering of NTL signals to generate potential urban clusters, (2) identification of optimal thresholds to delineate annual urban extents, and (3) check of temporal consistency to correct pixel-level urban dynamics. We found that the global urban land area percentage of the Earth's land surface rose from 0.22 % to 0.69 % between 1992 and 2020. Urban dynamics over the past 3 decades at the continent, country, and city levels exhibit various spatiotemporal patterns. Our resulting global urban extents (1992–2020) were evaluated using other urban remote sensing products and socioeconomic data. The evaluations indicate that this dataset is reliable for characterizing spatial extents associated with intensive human settlement and high-intensity socioeconomic activities. The dataset of global urban extents from this study can provide unique information to capture the historical and future trajectories of urbanization and to understand and tackle urbanization impacts on food security, biodiversity, climate change, and public well-being and health. This dataset can be downloaded from https://doi.org/10.6084/m9.figshare.16602224.v1 (Zhao et al., 2021).

This study visualized and analyzed the developing trends and hot topics in natural disaster research. 19694 natural disaster-related articles (January 1900 to June 2015) are indexed in the Web of Science database. The first step in this study is using complex networks to visualize and analyze these articles. CiteSpace and Gephi were employed to generate a countries collaboration network and a disciplines collaboration network, and then attached hot topics to countries and disciplines, respectively. The results show that USA, China, and Italy are the three major contributors to natural disaster research. “Prediction model”, “social vulnerability”, and “landslide inventory map” are three hot topics in recent years. They have attracted attention not only from large countries like China but also from small countries like Panama and Turkey. Comparing two hybrid networks provides details of natural disaster research. Scientists from USA and China use image data to research earthquakes. Indonesia and Germany collaboratively study tsunamis in the Indian Ocean. However, Indonesian studies focus on modeling and simulations, while German research focuses on early warning technology. This study also introduces an activity index (AI) and an attractive index (AAI) to generate time evolution trajectories of some major countries from 2000 to 2013 and evaluate their trends and performance. Four patterns of evolution are visible during this 14-year period. China and India show steadily rising contributions and impacts, USA and England show relatively decreasing research efforts and impacts, Japan and Australia show fluctuating activities and stable attraction, and Spain and Germany show fluctuating activities and increasing impacts.

Spatiotemporal fusion (STF) is considered a feasible and cost-effective way to deal with the trade-off between the spatial and temporal resolution of satellite sensors, and to generate satellite images with high spatial and high temporal resolutions. This is achieved by fusing two types of satellite images, i.e., images with fine temporal but rough spatial resolution, and images with fine spatial but rough temporal resolution. Numerous STF methods have been proposed, however, it is still a challenge to predict both abrupt landcover change, and phenological change, accurately. Meanwhile, robustness to radiation differences between multi-source satellite images is crucial for the effective application of STF methods. Aiming to solve the abovementioned problems, in this paper we propose a hybrid deep learning-based STF method (HDLSFM). The method formulates a hybrid framework for robust fusion with phenological and landcover change information with minimal input requirements, and in which a nonlinear deep learning-based relative radiometric normalization, a deep learning-based superresolution, and a linear-based fusion are combined to address radiation differences between different types of satellite images, landcover, and phenological change prediction. Four comparative experiments using three popular STF methods, i.e., spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC, as benchmarks demonstrated the effectiveness of the HDLSFM in predicting phenological and landcover change. Meanwhile, HDLSFM is robust for radiation differences between different types of satellite images and the time interval between the prediction and base dates, which ensures its effectiveness in the generation of fused time-series data.

In recent years, remote sensing (RS) research on crop growth status monitoring has gradually turned from static spectrum information retrieval in large-scale to meso-scale or micro-scale, timely multi-source data cooperative analysis; this change has presented higher requirements for RS data acquisition and analysis efficiency. How to implement rapid and stable massive RS data extraction and analysis becomes a serious problem. This paper reports on a Raster Dataset Clean & Reconstitution Multi-Grid (RDCRMG) architecture for remote sensing monitoring of vegetation dryness in which different types of raster datasets have been partitioned, organized and systematically applied. First, raster images have been subdivided into several independent blocks and distributed for storage in different data nodes by using the multi-grid as a consistent partition unit. Second, the “no metadata model” ideology has been referenced so that targets raster data can be speedily extracted by directly calculating the data storage path without retrieving metadata records; third, grids that cover the query range can be easily assessed. This assessment allows the query task to be easily split into several sub-tasks and executed in parallel by grouping these grids. Our RDCRMG-based change detection of the spectral reflectance information test and the data extraction efficiency comparative test shows that the RDCRMG is reliable for vegetation dryness monitoring with a slight reflectance information distortion and consistent percentage histograms. Furthermore, the RDCGMG-based data extraction in parallel circumstances has the advantages of high efficiency and excellent stability compared to that of the RDCGMG-based data extraction in serial circumstances and traditional data extraction. At last, an RDCRMG-based vegetation dryness monitoring platform (VDMP) has been constructed to apply RS data inversion in vegetation dryness monitoring. Through actual applications, the RDCRMG architecture is proven to be appropriate for timely vegetation dryness RS automatic monitoring with better performance, more reliability and higher extensibility. Our future works will focus on integrating more kinds of continuously updated RS data into the RDCRMG-based VDMP and integrating more multi-source datasets based collaborative analysis models for agricultural monitoring.

Spatiotemporal fusion is considered a feasible and cost-effective way to solve the trade-off between the spatial and temporal resolution of satellite sensors. Recently proposed learning-based spatiotemporal fusion methods can address the prediction of both phenological and land-cover change. In this paper, we propose a novel deep learning-based spatiotemporal data fusion method that uses a two-stream convolutional neural network. The method combines both forward and backward prediction to generate a target fine image, where temporal change-based and a spatial information-based mapping are simultaneously formed, addressing the prediction of both phenological and land-cover changes with better generalization ability and robustness. Comparative experimental results for the test datasets with phenological and land-cover changes verified the effectiveness of our method. Compared to existing learning-based spatiotemporal fusion methods, our method is more effective in predicting phenological change and directly reconstructing the prediction with complete spatial details without the need for auxiliary modulation.

The vulnerability of vegetation ecosystems and hydrological systems in high-altitude areas makes their phenology more sensitive and their response to climate change more intense. The Qilian Mountains, an important geographic unit located in the northeastern Tibetan Plateau (TP), has experienced the more significant increases in temperature and precipitation in the past few decades than most areas of the TP. However, under such intense climate change, the temporal and spatial differences in phenology in the Qilian Mountains are not clear. This study explored the spatial and temporal heterogeneity of phenology in the Qilian Mountains from 1982 to 2014 and its response to three temperature indicators, including the mean daily temperature (T_mean), mean daily daytime temperature (T_max), and mean daily nighttime temperature (T_min). The results showed that (1) as the altitude rose from southeast to northwest, the multiyear mean of the start of the growing season (SOS) was gradually delayed mainly from 120 to 190 days, the multiyear mean of the end of the growing season (EOS) as a whole was advanced (from 290 to 260 days), and the multiyear mean of the length of the growing season (LGS) was gradually shortened (from 150 to 80 days). (2) In general, there was an advanced trend in the annual average SOS (0.2 days per decade), a delayed trend in the annual average EOS (0.15 days per decade), and an extended trend in the annual average LGS (0.36 days per decade) over the study period. However, there has been no significant phenological trend in recent years, especially for the SOS after 2000 and the EOS and LGS after 2003. (3) Higher preseason temperatures led to an advanced SOS and a delayed EOS at the regional scale. Moreover, the SOS and EOS were more triggered by T_max than T_min and T_mean. The LGS was significantly positively correlated with annual mean temperature (r = −0.82, p < 0.01).