Spatiotemporal processes, such as floods, rainfall-runoff, and land-use changes, continuously evolve over space and time with high dynamism and complex nonlinearity. Accurate and efficient spatiotemporal process prediction is crucial for understanding their underlying patterns. Recently, deep learning has effectively addressed spatiotemporal prediction issues in Earth science. However, most existing studies address either short-term or long-term dependencies, but ignore the multiscale characteristics and spatial heterogeneity inherent to spatiotemporal processes and critical for practical applicability. This study develops a Spatiotemporal Adaptive Multiscale Transformer (SAMT) model for spatiotemporal process prediction. First, we design an enhanced multiscale spatial heterogeneity module to extract multiscale spatial heterogeneity. Then, we introduce the adaptive scale selection that assigns weights to features at different scales based on their contributions. In addition, we incorporate a spatiotemporal transformer block to simultaneously capture short-term and long-term dependencies. We conduct extensive experiments on three representative spatiotemporal datasets of rainfall, temperature, and flood. Compared to state-of-the-art models, the SAMT model achieves significant improvements across all evaluation metrics. The developed SAMT model critically improves the performance of spatiotemporal process prediction for more accurate and effective modelling of spatiotemporal evolution patterns in the field of Earth sciences.

Geographically weighted regression (GWR) is a classic local linear method for modeling spatial non-stationarity that is applied in various geographical scenarios. The modeling of spatial proximity in traditional GWR and its variants is usually based on various forms of spatial distances to construct spatial weights, overlooking the potential effect of multidimensional attribute similarity of physical entities. Therefore, we proposed the geographically spatial-attribute weighted regression (GSAWR) method with convolutional neural networks to account for spatial non-stationarity based on spatial proximity and attribute similarity. An attribute fusion convolutional neural network (AFCNN) considers the differential effects of attribute variables by assessing similarities among multiple variables. A spatial-attribute joint proximity neural network (SAJPNN) combines attribute similarity and spatial proximity to generate a proximity measure adaptive to both spatial proximity and attribute similarity. A spatial-attribute weighted convolutional neural network (SAWCNN) and ordinary linear regression (OLR) use the spatial-attribute joint proximity to make final predictions. We validated the GSAWR approach on simulated dataset and two real-world datasets: the PM2.5 and HIV datasets. The results revealed that the GSAWR model outperformed the other baseline models in terms of fitting and prediction performance. Ablation experiments and coefficient visualization further determined the effectiveness and interpretability of GSAWR model.

In vehicle-road cooperation, the advancement of vehicle-side autonomous driving is hindered by bottlenecks, such as limited sensing range, computational power, and environmental adaptability. Collaboration with roadside units is essential for achieving more accurate and complex spatial perception. This study presents a new prototype to enhancing spatial perception in road environments within Augmented Intelligence of Things (AIoT) systems using light detection and ranging (LiDAR) technology. Unlike traditional AIoT systems, which rely on cameras and struggle in complex conditions, the proposed prototype uses an edge-cloud–terminal collaborative sensing model to enhance 3-D spatial perception. A notable feature of this prototype is the integration of the distance and density adaptive filtering (DDAF) method, which ensures efficient point cloud filtering at the edge, with an average F1-score of 96.03% and an average latency of 11.78 ms, demonstrating strong accuracy and low latency across various scenarios. The incorporation of DDAF further improves the mean average precision (mAP) of deep learning-based 3-D object detection on the cloud by 2.34%, reduces processing time by 83.54%, and decreases peak memory usage by 90.18%, facilitating precise 3-D spatial analysis. The final results are displayed in real-time on the terminal for visualization and interaction. The efficacy of this prototype is demonstrated through a real-world case study. This research highlights the role of LiDAR and AIoT in overcoming spatial perception challenges in vehicle-road cooperation, leading to safer, more efficient transportation solutions.

In cross-view image geolocation tasks, traditional CNN models are limited in performance due to their inability to effectively capture global correlations. Although Transformer-based methods can address this shortcoming, their computational complexity and GPU memory consumption are relatively high. To address these limitations, we propose Swin Transformer for Cross-view Geo-localization (STG), a novel model based on the Swin Transformer (ST) that leverages its advantage of linear computational complexity scaling with image resolution. We design an adaptive window shift mechanism and introduce a pre-training strategy for STG, which can enhance STGs global modeling capability and its ability to learn general features. To further address the redundancy issues in our STG model, we propose the Distillation Swin Transformer for Cross-view Geo-localization model (DSTG), which is obtained through knowledge distillation using the STG model as the teacher model. To tackle the issue that the student model strugglings to learn from the teacher model’s output during distillation directly, we propose a multi-scale logit standardization distillation method. The proposed STG and DSTG models can obtain superior global embedding descriptions without relying on polar coordinate transformations. Experimental results on the CVUSA, CVACT_val, and CVACT_test datasets indicate that the DSTG model has significantly lower computational cost and GPU memory usage compared to the STG model. At the same time, both models achieve state-of-the-art performance. The source code is available at https://github.com/liangjxiong/DSTG.

Accurate and spatiotemporal seamless soil moisture (SM) products are important for hydrological drought monitoring and agricultural water management. Currently, physically-based process models with data assimilation are widely used for global seamless SM generation, such as Soil Moisture Active Passive level 4 (SMAP L4), the land component of the fifth generation of European Reanalysis (ERA5-land) and Global Land Data Assimilation System Noah (GLDAS-Noah). These datasets are usually produced using high-performance computation platforms and may subject to potential uncertainties from model structure and parameters, limiting their practical application capacity in a flexible way in local or global areas. Here, we proposed a data-driven artificial intelligence (AI)-based method to generate spatiotemporal seamless daily soil moisture data using triple collocation, machine learning and data assimilation. Specifically, the triple collocation correlation coefficients (TCR) method is employed to combine different SM datasets in order to obtain high-accuracy label data for model training first. A LightGBM machine learning (ML) model is constructed to simulate global daily soil moisture at 0.25◦ in an autoregressive way, using ERA5 meteorological forcings and MSWEP precipitation data as inputs. In addition, the satellite-based soil moisture SMAP level 3 (SMAP L3) is assimilated into the developed machine learning model using the simple Newtonian nudging technique to update the soil moisture simulation states. The incorporation of data assimilation into machine learning mimics the idea of physical models and brings much room for adaptable soil moisture simulations. The developed data-driven model is examined over global land areas from March 31, 2015 to May 31, 2023 with a ten-fold cross validation scheme, evaluated using 1094 in-situ soil moisture stations from International Soil Moisture Network (ISMN). The results indicate that the ML-based assimilated soil moisture dataset (MLDA) demonstrates a median correlation (R) of 0.741 and an unbiased root mean square error (ubRMSE) of 0.0437 m3/m3, better than SMAP L4 (R=0.717, ubRMSE=0.0452 m3/m3, ERA5-land (R=0.706, ubRMSE=0.0452 m3/m3) and GLDAS (R=0.633, ubRMSE=0.0501 m3/m3). Compared to the three model-based soil moisture products, the ML-DA dataset exhibits superior performance in time and space and also in dry-wet zones. Therefore, the developed ML-DA framework offers significant potential for accurate, spatiotemporal soil moisture simulations globally.

Physical layer security is a promising approach to enhancing the security of multi-user networks. However, user interference causes constellation points from different users to overlap, limiting both network reliability and security. To address this, we propose a parallel pulse amplitude modulation (PAM) scheme that ensures constellations are regularly superimposed at the legitimate receiver while appearing chaotic to the eavesdropper. Consequently, the eavesdropper experiences a consistently high bit/symbol error rate, whereas the legitimate receiver maintains a very low error rate. Furthermore, we extend the parallel PAM scheme to both the in-phase and quadrature components of the signal, forming a heterogeneous quadrature amplitude modulation (QAM) scheme. This enhances transmission efficiency while preserving security. We analyze the bit/symbol error rates at both the legitimate receiver and the eavesdropper, deriving a lower bound for the eavesdropper’s error rate. Finally, simulation results validate our theoretical analysis.

Location prediction based on social media information is highly valuable in human mobility research and has multiple real-life applications. However, existing research methods often ignore social influences, largely ignoring implicit information regarding interactions between users and geographical locations. Additionally, they generally employ single modeling structures, which restricts the effective integration of complex spatiotemporal characteristics and factors influencing user mobility. In this context, we propose a novel network with physical and social awareness that expresses both physical and social influences of user mobility from a global perspective based on a heterogeneous graph constructed using users and spatial locations as nodes and relationships between them as edges. This graph enables the model to leverage information from connected nodes and edges to infer missing or unobserved data. The model predicts future locations of users by effectively integrating the temporal and spatial features of user trajectory series. The proposed model is validated using three social media datasets. The experimental results demonstrate that the proposed method outperforms the state-of-the-art baseline models. This indicates the importance of considering complex interactions between users and locations, as well as the various influences of physical and social spaces.

Vegetation is a key component of biodiversity and ecosystem stability. The normalized difference vegetation index (NDVI) is widely used to monitor the vegetation growth status. Timely spatiotemporal prediction of NDVI is of great significance to agriculture and the ecological environment. Traditional deep learning models neglect the local spatial relationships and the effect of spatial autocorrelation in vegetation, failing to capture the neighboring contributions to the forecast target. In this study, a geographically weighted convolutional long short-term memory (GWConvLSTM) model is coupled with the spatial weight and the convolutional long short-term memory (ConvLSTM) model by the Hadamard product is proposed to predict NDVI. In the GWConvLSTM model, a spatiotemporal memory flow with the ConvLSTM unit is designed to calculate the spatial weight of each step, which is iteratively updated with the training of the model. We use the proposed GWConvLSTM model to forecast the monthly NDVI of Huangpi District and the experiment results indicate that the developed GWConvLSTM model outperforms five other baseline models. The predictive coefficient of determination ($R^{2}$) reached up to 0.8427, 0.8209, and 0.8149 for one-, three-, and five-month lead times, respectively. The mean absolute error (MAE) decreased by 14.26%, 10.04%, and 16.75% for GWConvLSTM versus the baseline ConvLSTM model in one-, three-, and five-month NDVI predictions, respectively. Therefore, the developed GWConvLSTM model could be an effective geospatial deep learning model for spatiotemporal NDVI prediction at region scales.

We propose a distributed bundle adjustment (DBA) method using the exact Levenberg-Marquardt (LM) algorithm for super large-scale datasets. Most of the existing methods partition the global map to small ones and conduct bundle adjustment in the submaps. In order to fit the parallel framework, they use approximate solutions instead of the LM algorithm. However, those methods often give suboptimal results. Different from them, we utilize the exact LM algorithm to conduct global bundle adjustment where the formation of the reduced camera system (RCS) is actually parallelized and executed in a distributed way. To store the large RCS, we compress it with a block-based sparse matrix compression format (BSMC), which fully exploits its block feature. The BSMC format also enables the distributed storage and updating of the global RCS. The proposed method is extensively evaluated and compared with the state-of-the-art pipelines using both synthetic and real datasets. Preliminary results demonstrate the efficient memory usage and vast scalability of the proposed method compared with the baselines. For the first time, we conducted parallel bundle adjustment using LM algorithm on a real datasets with 1.18 million images and a synthetic dataset with 10 million images (about 500 times that of the state-of-the-art LM-based BA) on a distributed computing system.

Climatology (mean seasonal cycle) often dominates the errors in satellite soil moisture (SM) products, which is highly essential for the water-carbon cycle in tropical rainforests. Although microwave observations at L-band are expected to provide more accurate SM information benefiting from their stronger penetration capacity, the SM mapping in rainforests by L-band measurements is still challenging and is less investigated by the community. To bridge the research gap, five L-band satellite SM products from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites, including SMOS-IC, SMAP SCA-V, DCA, MTDCA and IB were assessed by using the FLUXNET SM data in tropical rainforests. To cope with the time inconsistence between satellite and ground data, the SM climatology variations in rainforests for diverse time periods were compared using long-term ERA5 SM. The results indicate the SM climatology is relatively stable over different time periods during 2001-2020 for rainforest sites. Based on the stability of SM climatology, L-band SM products were demonstrated to satisfactorily capture the SM climatology variations in rainforests, especially for SMOS-IC and SMAP-IB. The results are expected to provide guidelines for the hydro-ecological applications using satellite SM products in tropical rainforests.

Blending both active and passive microwave measurements are expected to provide more robust surface soil moisture (SSM) estimations over various environmental conditions compared to that from the single sensor. The integration of the newest L-band passive (i.e., Soil Moisture Active Passive, SMAP) with the similar observation scale active (i.e., the Advanced Scatterometer, ASCAT) sensors provides a considerable opportunity to improve the accuracy of SSM mapping, which however is rarely investigated to date. In this study, we implemented the integration of SMAP brightness temperature (TB) and ASCAT backscattering coefficient (σ) for estimating SSM using the machine learning approach, by fully considering the error sources in physically-based retrieval approaches (e.g., τ–ω model). The independent validation results using ground data from 14 dense networks show the integration of SMAP and ASCAT measurements can satisfactorily achieve better SSM retrievals compared to ASCAT SSM, SMAP-SCA-V SSM and ESA CCI SSM products, with the lowest ubRMSE of 0.042 m3/m3and the highest R of 0.76. This study is expected to enrich the understanding of SSM retrieval from active and passive microwave satellites, and provide SSM product with higher accuracy for eco-hydrological applications.

From a process perspective, a flood includes several phases with distinguishable features. Fine-grained multisource data for different flood phases can be used to inform decision-making as flooding progresses. Therefore, the aim of this study was to develop an integrated framework based on human perceptions to progressively profile floods, including flood process segmentation rules (FPSR), flood severity index (FSI) and flood process perception ontology (FPPO). FPSR identifies flood phases based on specific signals in multisource data and provides spatiotemporal process information to FPPO consistent with flood perception. FSI follows FPSR to evaluate flooding throughout its evolution process. The comparison between FPSR and the flood monitoring index (IF) demonstrates that FPSR can detect flood events and segment the flooding process into latency, onset, development and recovery phases. The correlations between the standardized antecedent precipitation index (SAPI) and FSI show that FSI can assess flood severity with both natural and social effects in every flooding phase (R2 = 0.726 and 0.673 for the 2016 and 2020 floods, respectively). An experiment finds that flood events in Wuhan, China, usually begin in mid-to-late June and are the most severe in July, when more caution is needed for flood prevention and mitigation.

Dynamic changes in traffic conditions cause spatiotemporal variation in traffic monitoring demand. It is, therefore, necessary to conduct efficient road monitoring to identify dynamic abnormal situations, especially in peak traffic periods. Recently, unmanned aerial vehicles (UAVs) have become an attractive solution to this problem. However, UAV monitoring routes suffer from time limitations during peak traffic hours. To optimize UAV monitoring routes during rush hours, we develop a route planning method incorporating spatiotemporal variations in monitoring demand, in which we introduce a team orienteering arc routing problem with time-varying profits (TOARP-TP) and construct a corresponding mathematical model. The TOARP-TP is an extension of an already existing routing problem, team orienteering arc routing problem (TOARP). An iterated local search (ILS)-based algorithm is designed to solve the large instances of this problem. To verify the proposed method, we conduct sets of numerical experiments with the Sioux Falls road network in South Dakota, US, and a case study is applied using Wuhan, Hubei, PRC. The results demonstrate the efficiency and practicality of our method in optimizing UAV traffic monitoring routes during rush hours. Furthermore, we discuss a strategy for scenario determination and method selection in UAV route planning.

Assessment of the model-based surface soil temperature (ST) products is very important for hydrometeorological and ecological applications, as well as model refinements. Distinguished from previous regional validations using only in situ observations from sparse networks, this study focused on the evaluation of model-based ST products by considering ground observations from 15 dense networks worldwide from April 2015 to December 2017 covering a wide range of ground conditions. Four model-based ST products were selected for the assessment, including the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), the Goddard Earth Observing System Model version 5 Forward Processing (GEOS-5 FP), the ERA-Interim and its successor, the newly developed ERA5. The results indicate the GEOS-5 ST product slightly outperforms other ST products by showing an averaged ubRMSD of 1.84 K. All model-based ST products underestimate in situ ST with a negative bias. All four model-based ST products are demonstrated to well capture the temporal trends of ground observations with very promising$R$values larger than 0.97. The ERA5 shows visible improvements compared to its predecessor ERA-Interim by exhibiting smaller ubRMSD, absolute bias and larger$R$values. These findings are expected to provide useful suggestions for the enhancement and specific usage of the model-based ST products.

Soil moisture is an essential variable both in environmental monitoring research and application. With the requirement of high-precision soil moisture data, it is highly necessary to construct in-situ soil moisture sensors Web in high density, in which the economics, complexity, and low-power consumption of sensor Webs should be essentially considered. However, most current existing soil moisture monitoring networks have limitations due to high power consumption, complex architecture, and expensive equipment. These issues are not conducive to high-density soil moisture observation. In view of the existing problems in the current soil moisture in-situ sites, an effective resolution for high-density soil moisture observation is needed. For the first time, we are bringing Narrow-Band Internet of Things (NB-IoT), a low-power Internet-of-Things technology, into geospatial sensor Web for soil moisture observation. Moreover, we built a high-density in-situ soil moisture sensor Web via NB-IoT and compared it with ZigBee at the Baoxie experimental zone in Wuhan, China, there about 1 km2, and we acquired data for up to 12 months. We analyze the acquisition record of battery status, signals, and soil moisture. We analyzed the battery life, the impact of the signal on the battery performance, the signal quality, and data acquisition clearly and intuitively. This unprecedented study and application, we discovered and concluded that the low-power sensor Web of the NB-IoT communication protocol could be suitable for high-density soil moisture observation.

Early warning of cyanobacteria bloom is very important for water ecological management of lakes. Thus, Based on Landsat8 OLI data, combining the trophic state index (TSI), cyanobacteria and macrophytes index (CMI) and floating algae index (FAI), we proposed a risk zoning framework of cyanobacteria bloom, and applied it in Lake Longgan (China). Eutrophication frequency of Lake Longgan has worsened since 2017. But the maximum eutrophication proportion occurred on 16 February 2016. With a deeper analysis on 16 February 2016, we find that the risk area of cyanobacteria bloom in Lake Longgan is distributed horizontally from the east bank to the center of the lake, and mostly is mid risk. High risk areas are mainly distributed in the east coast, showing a southwest direction. Therefore, the proposed risk zoning framework provides a useful approach to obtain the pre-warning information of cyanobacteria bloom in some Eutrophic and macrophytic lakes.

When facing a specific emergent geographical environment observation task (GeoTask), people need to be able to handle reliable and comprehensive disaster information in the shortest possible time. The lack of effective cognition of multi-sensor collaborated observation capability is a hindrance to performance. By adopting the GIS object field concept as the bottom framework, we propose a sensor observation capability object field (SOCO-Field) with sensor observation capability particle (SOC-Particle) as its core. SOCO-Field integrates SOC-Objects and GeoField for the discovery and association of sensors. SOC-Particle objectively exists on every location point in the geospatial environment, and SOC-Particles in space-continuous areas can further aggregate into SOC-Particle cluster to represent single- or multi-sensor-associated observation capability information. SOCO-Field includes three basic association behaviours and four further association behaviours to solve associated observation capability, in which the dynamic GeoField is the influential factor. An experiment on flood monitoring in the lower reaches of Jinsha River Basin is conducted. The sensor planner can view any sensor combination’s associated observation capability under a specific association mode and can effectively dispatch a multi-sensor for collaborated observation due to the effective modelling of associated sensor observation capability information (SOCInfo).

The hydrologic monitoring is of great importance to the water resources management, ecological environment protection. The equipment and methods used in hydrological monitoring are various, the data types are complex and diverse. There are various causes of the hydrological disasters, and they are interrelated and interactional. This paper summarizes the main hydrological monitoring sensors, observations and events, and builds a preliminary hydrological ontology model for hydrological monitoring, which solves the problem of hydrological monitoring complexity to a certain extent.

Precipitation stations are important components of a hydrological monitoring network. Given their critical role in rainfall forecasting and flood warnings, along with limited observation resources, determining the optimal locations to deploy precipitation stations presents an important problem. In this paper, we use a maximal covering location problem to identify the best precipitation station sites. Considering the terrain conditions and the characteristics of a rainfall network, the original maximal covering location model is modified with the introduction of a set of additional constraints. The minimum density requirement is used to determine a precipitation station’s coverage range, and three weighting schemes are used to evaluate each demand object’s covering priority. As a typical mountainous watershed with high annual precipitation, the Jinsha River Basin is selected as the study area to test the applicability of the proposed method. Results show that the proposed method is effective for precipitation station configuration optimization, and the model solution achieves higher coverage than the real-world deployment. Compared with the commercial solver CPLEX, a genetic algorithm-based heuristic can significantly reduce the computation time when the problem size is large. Several deployment strategies are also discussed for establishing the optimal configuration of precipitation stations.

Clouds often limit the ability of optical satellite sensors (such as the newly launched Gaofen-1 (GF-1) satellite in China) to observe regional soil moisture at high spatial resolutions, especially under full-cloud-contamination condition. Thus, accurate reconstruction of regional soil moisture in this case became a great methodological challenge because of the complexity and ill-posed nature of the problem. In this paper, we present a Satellite and In situ sensor Collaborated Reconstruction (SICR) method. In this method, four reconstruction rules were proposed to rebuild four kinds of corresponding missing pixels, defined as follows: C1 pixel (including one in situ sensor in its area), C2 pixel (physically similar to C1), C3 pixel (with a regular soil moisture observation sequence), and C4 pixel (remaining). By analyzing soil moisture observation relationships between these four types of pixels with in situ measurements and within these pixels, four numerical reconstruction rules were established. Linear regression, similar pixel determination, least square method, and geostatistical interpolation algorithms were used in these four rules. At last, all blank soil moisture pixels in the target soil moisture image can be filled by the SICR method. The experiment conducted in the central south of U.S. integrated 11 in situ soil moisture sensors from the United States Department of Agriculture with 11 GF-1 satellite soil moisture images. It was demonstrated that GF-1 soil moisture observations on October 17, 2014, were successfully reconstructed by the SICR method, based on the evaluations of visual appearance comparison, error distribution analysis, subimage comparison, average relative error, and universal image quality index. SICR also performed better than the reconstruction results only based on in situ or satellite sensor data. Moreover, the comparison with the soil moisture observation from the microwave sensor demonstrated the value of SICR in regional high-resolution soil moisture reconstruction. It was suggested that the SICR method provided an effective reconstruction method under full cloud contamination and showed great potential for collaborating satellite and in situ sensors.

Different Earth-observing (EO) sensors have various capabilities for diverse observing tasks. Sensor planning services make the choice of web-ready sensors for specific observing tasks with regard to observing requests and sensor capabilities. Sensor capabilities rely on various parameters; thus, choosing EO sensors for specific observing tasks relying directly on these parameters is a multicriteria decision process. A sensor's capability can be drawn from these parameters with the help of an algorithm. Furthermore, if divided into different clusters based on capabilities, applicable sensors can be more easily chosen for a category of observing tasks. In this paper, a spaceborne EO optical sensor static capability index (SSCI) mechanism is drawn from an evaluation-and-clustering algorithm, which is composed of a self-organizing neural map in combination with weighted principal component analysis. The scheme of SSCI relies on no expert analysis system and thus is more flexible and efficient. EO scenarios of disaster reactions are among the application of this algorithm. In particular, scenarios of flooding disaster forecasting, relief aiding, and postdisaster loss assessment within the framework of International Charter on Space and Major Disasters have been utilized for experiments. They have shown that the SSCI assessing algorithm is feasible and stable, and the EO optical sensor clustering algorithm based on SSCI can offer reasonable clustering accuracies of EO optical sensors. In our experiments, the EO optical sensor SSCI computation and clustering algorithm had a time consumption within 2 s and 2 min, respectively, and memory consumption within 200 MB on a normal personal computer.

It is a needful and challenging work to integratedly manage the massive, distributed and heterogeneous sensor resources for a cooperative observation task in smart city context, especially for the cooperative task of dynamic environment monitoring, timely emergency response and citizen safety guarantee. Regarding the heterogeneous sensor resources that immersed in the cities, there lacks an open and comprehensive platform for the management of this sensing infrastructure of the smart cities. In this paper, a Geospatial Sensor Web Resource Management System (Geo-SWRMS) is designed and implemented to tackle this issue. With the requirement investigation of the resources management of the Geospatial Sensor Web for the smart city, four main functions including resource modeling and registry, resource query and discovery, resource visualization and resource access and control are considered and implemented leveraging the standard Sensor Web Enablement information model and service interface of Open Geospatial Consortium. The proposed system is flexible, accommodating heterogeneous sensor resources and can be utilized in multiple application fields in the smart cities such as sensing resource management, cooperative observation task for emergency and so on. The Geo-SWRMS promotes the sharing and interoperability of the sensor resources, and facilitates to provide a solid information base for observing task scheduling and decision making in the smart city application.

The sensor web is a new method in the earth observation field, comprising sensors that can sense, compute, and correspond with the World Wide Web. The Chinese Integrated Earth Observation Sensor Web (CIEOSW) project is a five-year national basic research program conducted by Wuhan University since 2011 and is aimed to address the following major challenges: (1) the lack of collaboration within the satellite observation system, (2) the absence of a coupling mechanism in heterogeneous spaceborne -- airborne -- ground sensors, and (3) the limited connection between monitoring and decision-support services. For two years, the CIEOSW project has strived to achieve the following: (1) a theory on the coupling and modeling of the Earth observation sensor web (EOSW), (2) an event-driven multi-sensor collaborative observation method, (3) an EOSW fusion and assimilation method, (4) an EOSW-based information extraction and rapid change detection method, (5) a task-oriented EOSW focusing service model, and (6) an epicontinental environment observation and analysis of typical areas in China.

Cooperative spectrum sensing (CSS) is a promising technique in cognitive radio networks (CRNs) that utilises multi‐user diversity to mitigate channel instability and noise uncertainty. In this study, the relationship between ‘cooperation mechanisms’ and ‘spatial‐spectral diversity’ over multiple channels jointly sensing is investigated in the presence of an imperfect reporting channel. The multiple channels are sensed at the receiver built on the filter bank‐based multi‐carrier system. The multi‐channel CSS strategies are modelled by the introduced ‘cooperative ratio’ to balance the requirements on ‘sensing accuracy’, ‘efficiency’ and ‘overhead’, which is quantitatively characterised by the energy consumption. The target of CSS is to maximise the aggregate opportunistic throughput of secondary users (SUs) by jointly considering constraints on sensing overhead and the aggregate interference to primary users (PUs). The optimisation is divided into two sequential sub‐optimisation processes, ‘multi‐user diversity optimisation’ and ‘multi‐channel diversity optimisation’. An approach is developed from generic algorithms to solve the two sub‐problems. Numerical results show that the optimal CSS scheme is effective in improving channel utilisation for SUs with low interference to PUs. This study establishes a valuable cooperative model for the design of multi‐channel spectrum sensing algorithms in CRNs.

Different versions of the Web Coverage Service (WCS) schemas of the Open Geospatial Consortium (OGC) reflect semantic conflict. When applying the extended FRAG-BASE schema-matching approach (a schema-matching method based on COMA++, including an improved schema decomposition algorithm and schema fragments identification algorithm, which enable COMA++-based support to OGC Web Service schema matching), the average recall of WCS schema matching is only 72%, average precision is only 82% and average overall is only 57%. To improve the quality of multi-version WCS retrieval, we propose a schema-matching method that measures node semantic similarity (NSS). The proposed method is based on WordNet, conjunctive normal form and a vector space model. A hybrid algorithm based on label meanings and annotations is designed to calculate the similarity between label concepts. We translate the semantic relationships between nodes into a propositional formula and verify the validity of this formula to confirm the semantic relationships. The algorithm first computes the label and node concepts and then calculates the conceptual relationship between the labels. Finally, the conceptual relationship between nodes is computed. We then use the NSS method in experiments on different versions of WCS. Results show that the average recall of WCS schema matching is greater than 83%; average precision reaches 92%; and average overall is 67%.

In the era of high-frequency occurrence of natural disasters, users are more urgently concerned with the sharing of the satellite sensor resources information and coordinating the complement of sensor observation. However, the capacity of discovering, retrieving and visualizing the sensor resource information accurately based on heterogeneous sensors over sensor network is very limited. This paper proposes the system architecture for effectively managing those heterogeneous and multiple sensors and their information, which is inspired by the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) Initiative and based on one of its information model- Sensor Model Language (SensorML) of which Process Model is the core. The prototype "SensorModel VI. 0" is designed and implemented used to construct the standard model for unified management of multiple remote sensing satellite sensor resources information and demonstrate the model-based retrieval and visualization of related remote sensors and their information, which promotes the comprehensive accessing and collaborative planning/controlling the available remote sensor's information in time-critical disaster emergency.

The Sensor Web is a coordinated observation infrastructure composed of distributed resources that can behave as a single, autonomous, task-able, reconfigurable observing system that provides observed and derived data along with the associated metadata by using a set of standards-based service oriented interfaces. But these resources, including sensor, data, platform etc., with different characteristics are hard to be fused well. This paper analyzes concepts of various types of sensor web resources for atmospheric observing, focusing on how to access different types of resources expediently, abstract essential features of these sensor web resources, and construct a Sensor Web Resources Ontology for Atmospheric Observation (SWRO-AO) represented by Web Ontology Language. The SWRO-AO could serve as a knowledge repository of sensor web resources for the research and application community in atmospheric science.

Bi-static remote sensing with GPS signal (direct and reflect signals) has supported some researches in soil moisture measuring, sea state estimating and sea altimetry monitoring. In these researches the special designed receivers are generally required, such as the "delay-mapping" GPS receiver or open loop differential real-time receiver, because they can find the characteristics of the reflected electromagnetic wave affected by the object. Now the commercial single frequency GPS receiver becomes more lighter, smaller and lower power, it is potentially capable of sea remote sensing. However it is ignored. In this paper, a ground based GPS-R technology with single frequency receiver is introduced. The technology is based on both the scatter theory from the rough surface and the GPS double difference theory of carrier phase. This paper describes the GPS-R instrument, introduces the basic method and shows the result of GPS-R experiments on bridge. These results provide good initial conditions for widespread sea state monitoring if using GPS-R receiver network . this paper also discusses the results under the influence of the satellite elevation and single difference .

Detecting motion-based video change, such as different types of motion video or applications using different change detection algorithms in a Web system, is difficult. This paper designs and implements architecture for a real-time or near real-time on-demand motion video change detection system using the Sensor Observation Service (SOS) and the Web Processing Service (WPS) in the Sensor Web environment. Real-time or near real-time includes sensors that obtain motion video data, SOS provides motion video data to WPS and WPS processes this data. Three solution methods are introduced: the GetObservation operation of SOS by transaction, dynamical interaction between SOS and WPS, and WPS real-time or near real-time processing. On-demand means that a developer can choose different motion video change detection algorithms under different applications or different conditions. For this purpose, a flexible, standards-based and service-oriented WPS architecture is designed, which consists of three layers: the WPS interface layer, the field interface layer and the implementation layer. To test the proposed approach, a video change detection case of monitoring a road situation is shown, which was a demonstration for Open Geospatial Consortium Web Service phase 7. The results demonstrate that the proposed approach is feasible.

The OGC Web Service (OWS) schemas have the characteristics of a complex element structure, are distributed and large scale, have differences in element naming, and are available in different versions. Applying conventional matching approaches may lead to not only poor quality, but also bad performance. In this article, the OWS schema file decomposition, fragment presentation, fragment identification, fragment element match, and combination of match results are developed based on the extended FRAG-BASE (fragment-based) schema-matching method. Different versions of Web Feature Service (WFS) and Web Coverage Service (WCS) schema-matching experiments show that the average recall of the extended FRAG-BASE matching for the schemas is above 80%, the average precision reaches 90%, the average overall achieves 85%, and the matching efficiency increases by 50% as compared with that of the COMA and CONTEXT matcher. The multi-version WFS retrieval under the Antarctic Spatial Data Infrastructure (AntSDI) data service environment demonstrates the feasibility and superiority of the extended FRAG-BASE method.

Because the advances in research technologies and the geospatial data with which they deal are diverse and complex. The OGC Web Processing Service (WPS) has several areas of complexity. Basic request-response mechanisms need to contend with delays/failures, especially for mid-term or long-term actions. The asynchronicity in communication between a user and the corresponding Web processing service, or between two services, is significant. The Web Notification Service (WNS), an OGC notification and communication service by which a client may conduct asynchronous dialogues (message interchanges) with one or more other services, can be useful when satisfying a client request requires many collaborating services and/or when there are significant delays in satisfying the request. WNS-based asynchronous notification middleware has been implemented by augmenting the OGC Web Processing Service with message-based asynchronous notification to resolve WPS asynchronous communication and notification problems.