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Sensor planners find it difficult to choose the right sensor combination for observation tasks because current sensor discovery methods can only generate lists of individual sensors without considering the association between sensors that is conveyed by understanding their observation capability information. This limitation results in the failure to properly plan sensor observations and to obtain accurate observation information. The observation capability information association model (OCIAM) is proposed. An inherent state model, dynamic observation model, and correlation model are formulated in the framework of the OCIAM. The core of OCIAM is the correlation model in which four correlation modes (competitive, complementary, enhanced, and cooperative) are illustrated. A prototype system called MultisensorAssociation is developed for constructing an experiment for flood observation in the lower reaches of the Jinsha River Basin. The results of this case study verify that the OCIAM can be used for the selection of sensors or sensor combinations and has important theoretical and practical significance for multisensor observation integration management.

Seasonal precipitation forecasts at regional or local areas can help guide agricultural practice and urban water resource management. The North American multi-model ensemble (NMME) is a seasonal forecasting system providing precipitation forecasts globally. Bias correction and downscaling of the NMME is a critical step before applied at local scales. Here, the machine learning methods coupling with wavelet are used to correct the precipitation forecasts in NMME for 518 meteorological stations in China for eight models at 0.5–8.5 months leads. Compared with the traditional quantile mapping (QM) approach, the wavelet support vector machine (WSVM) and wavelet random forest (WRF) methods exhibit obvious advantage in downscaling, with an overall average improvement of Pearson’s correlation coefficient increasing by 0.05–0.3 and root mean square error (RMSE) reducing by 18–40 mm (21–33%) for individual models. Both the spatial and seasonal patterns of downscaled results demonstrate the superiority of wavelet machine learning methods over QM. A spatial analysis indicates that the corrected NMME precipitation forecasts show the best skill in South China, with an average RMSE of about 30 mm, while the worst skill in Central and Southwest China with a RMSE of 80 mm. In spite of the correction, the uncertainties of seasonal precipitation forecasts in summer and extreme wet cases are still large. However, the WSVM and WRF methods may serve as an effective tool in the bias correction of NMME precipitation forecasts.

Observation schedules depend upon the accurate understanding of a single sensor’s observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no study has described the dynamic associations among the observation capabilities of multiple sensors used for integrated observational planning. This limitation results in a failure to realize effective sensor selection. This paper develops a sensor observation capability association (SOCA) ontology model that is resolved around the task-sensor-observation capability (TSOC) ontology pattern. The pattern is developed considering the stimulus-sensor-observation (SSO) ontology design pattern, which focuses on facilitating sensor selection for one observation task. The core aim of the SOCA ontology model is to achieve an observation capability semantic association. A prototype system called SemOCAssociation was developed, and an experiment was conducted for flood observations in the Jinsha River basin in China. The results of this experiment verified that the SOCA ontology based association method can help sensor planners intuitively and accurately make evidence-based sensor selection decisions for a given flood observation task, which facilitates efficient and effective observational planning for flood satellite sensors.

An accurate and comprehensive representation of an observation task is a prerequisite in disaster monitoring to achieve reliable sensor observation planning. However, the extant disaster event or task information models do not fully satisfy the observation requirements for the accurate and efficient planning of remote-sensing satellite sensors. By considering the modeling requirements for a disaster observation task, we propose an observation task chain (OTChain) representation model that includes four basic OTChain segments and eight-tuple observation task metadata description structures. A prototype system, namely OTChainManager, is implemented to provide functions for modeling, managing, querying, and visualizing observation tasks. In the case of flood water monitoring, we use a flood remote-sensing satellite sensor observation task for the experiment. The results show that the proposed OTChain representation model can be used in modeling process-owned flood disaster observation tasks. By querying and visualizing the flood observation task instances in the Jinsha River Basin, the proposed model can effectively express observation task processes, represent personalized observation constraints, and plan global remote-sensing satellite sensor observations. Compared with typical observation task information models or engines, the proposed OTChain representation model satisfies the information demands of the OTChain and its processes as well as impels the development of a long time-series sensor observation scheme.

Sensor capability information can be used as a basis for the integrated management of vast and heterogeneous remote sensing sensors in Global Earth Observation System of Systems environment. However, the existing representation of this information shows an inconsistent pattern, incomplete capability aspects, and casual expression forms, resulting in information silos among different systems. A sensor capability representation model (SCRM) is proposed in this study. Based on the Meta Object Facility architecture, a five-tuple hierarchical SCRM framework is formulated. Five specific representation element collections for typical remote sensing sensor types are developed to satisfy the requirements of detailed capability expression. The Open Geospatial Consortium Sensor Model Language is used as the expression form of the proposed SCRM. A prototype system is developed and a case study is conducted for a soil moisture monitoring application in Baoxie Town. The SCRM can also be extensively utilised for other environmental monitoring and modelling situations. We propose a heterogeneous remote sensing sensor capability meta-model.We define a five-tuple sensor capability representation structure.We design an extension mode to support five typical types of RS sensors.A prototype system is designed and implemented.The model is applied in the multi-sensor monitoring of soil moisture.

Precipitation stations and streamflow stations are important components of hydrological monitoring networks. To effectively obtain observation data, an optimal deployment of different hydrological stations should be established to maximize the spatial coverage and sensing capability given that such resources are often limited. By transforming this problem into a maximal covering location problem (MCLP), a MCLP-based approach can be used to solve the problem of siting hydrological stations. This paper designs and implements a hydrological sensor web optimal deployment system (HSWODS) for determining the optimal locations to deploy hydrological stations based on the maximal covering location approach. The main functions of the HSWODS's optimization procedure include demand representation, weighting scheme design, maximal coverage range definition, finite dominant set identification, model calculation, and deployment visualization. The optimal deployment of precipitation stations and streamflow stations in the lower reach of the Jinsha River Basin is selected for this study to demonstrate that HSWODS provides a flexible and extended platform to optimize the configuration of different hydrological stations for achieving the spatial coverage maximization.

Accurate and fine-grained discovery by diverse Earth observation (EO) sensors ensures a comprehensive response to collaborative observation-required emergency tasks. This discovery remains a challenge in an EO sensor web environment. In this study, we propose an EO sensor observation capability metadata model that reuses and extends the existing sensor observation-related metadata standards to enable the accurate and fine-grained discovery of EO sensors. The proposed model is composed of five sub-modules, namely, ObservationBreadth, ObservationDepth, ObservationFrequency, ObservationQuality and ObservationData. The model is applied to different types of EO sensors and is formalized by the Open Geospatial Consortium Sensor Model Language 1.0. The GeosensorQuery prototype retrieves the qualified EO sensors based on the provided geo-event. An actual application to flood emergency observation in the Yangtze River Basin in China is conducted, and the results indicate that sensor inquiry can accurately achieve fine-grained discovery of qualified EO sensors and obtain enriched observation capability information. In summary, the proposed model enables an efficient encoding system that ensures minimum unification to represent the observation capabilities of EO sensors. The model functions as a foundation for the efficient discovery of EO sensors. In addition, the definition and development of this proposed EO sensor observation capability metadata model is a helpful step in extending the Sensor Model Language (SensorML) 2.0 Profile for the description of the observation capabilities of EO sensors.

In the Earth Observation sensor web environment, the rapid, accurate, and unified discovery of diverse remote sensing satellite sensors, and their association to yield an integrated solution for a comprehensive response to specific emergency tasks pose considerable challenges. In this study, we propose a remote sensing satellite sensor object model, based on the object-oriented paradigm and the Open Geospatial Consortium Sensor Model Language. The proposed model comprises a set of sensor resource objects. Each object consists of identification, state of resource attribute, and resource method. We implement the proposed attribute state description by applying it to different remote sensors. A real application, involving the observation of floods at the Yangtze River in China, is undertaken. Results indicate that the sensor inquirer can accurately discover qualified satellite sensors in an accurate and unified manner. By implementing the proposed union operation among the retrieved sensors, the inquirer can further determine how the selected sensors can collaboratively complete a specific observation requirement. Therefore, the proposed model provides a reliable foundation for sharing and integrating multiple remote sensing satellite sensors and their observations.

Flood observation-supporting satellite sensors (FO-SSs) are the important geospatial resources to flood disaster management. In the geospatial sensor Web environment, effective flood disaster management requires on-demand integration of instant observation information from the diverse web-ready FO-SSs. We propose a sharable and interoperable Earth Observation Satellite Sensor Information (SSI) model for satellite observations integration. The SSI model reuses and extends the existing metadata standards to be a standard metadata-filled description framework. The SensorModel prototype serving here is to apply the SSI model into FO-SSs, retrieve and visualize the qualified Web-ready FO-SSs. An experiment on the flood emergency in the middle reaches of Yangtze River basin in China is conducted. The results show that, three kinds of users including FO-SSs providers, common emergency responders, and emergency managers can benefit from the proposed FO-SSI model, and the managers can reliably and comprehensively integrate the satisfied Web-ready FO-SSs to guide the scheduling of the required observation activities.

Sensor inquirers cannot understand comprehensive or accurate observation capability information because current observation capability modeling does not consider the union of multiple sensors nor the effect of geospatial environmental features on the observation capability of sensors. These limitations result in a failure to discover credible sensors or plan for their collaboration for environmental monitoring. The Geospatial Environmental Observation Capability (GEOC) is proposed in this study and can be used as an information basis for the reliable discovery and collaborative planning of multiple environmental sensors. A field-based GEOC (GEOCF) information representation model is built. Quintuple GEOCF feature components and two GEOCF operations are formulated based on the geospatial field conceptual framework. The proposed GEOCF markup language is used to formalize the proposed GEOCF. A prototype system called GEOCapabilityManager is developed, and a case study is conducted for flood observation in the lower reaches of the Jinsha River Basin. The applicability of the GEOCF is verified through the reliable discovery of flood monitoring sensors and planning for the collaboration of these sensors.

Many achievements in web-based geoprocessing focus on logically chaining Open Geospatial Consortium (OGC) web services, for example using Business Process Execution Language to orchestrate web services that are interfaced through the OGC Web Processing Service. For e-Science application in a sensor web environment, how to internally integrate the sensor system, observation, and processes (physical and non-physical) as a geoprocessing e-Science workflow model is a critical issue. The OGC Sensor Model Language offers the possibility to construct a geoprocessing e-Science workflow model in the form of observation processes. We propose a construction method for a geoprocessing e-Science workflow model that integrates logical and physical processes into a composite process chain for sensor observations. The three phases of geoprocessing e-Science workflow creation are abstract process chain modeling, process chain instantiation, and process chain workflow execution. An experiment on chaining-related sub-processes for deriving the Normalized Difference Vegetation Index of Hubei Province (China) was conducted to verify the feasibility of the proposed workflow model.

How the heterogeneous and distributed atmospheric satellite sensors can achieve precise discovery and collaborative observation is a big challenge. In this study, we propose an atmospheric satellite sensor observation system meta-model that reuses and extends the existing geospatial or sensor-related metadata standards to enable the sharing and interoperability of atmospheric satellite sensors. The Open Geospatial Consortium Sensor Model Language (SensorML) has a clear hierarchy in describing the metadata framework, and it is adopted as the carrier to formalize our proposed meta-model into the Atmospheric Satellite Sensor Observation Information Model (A-SSOIM). Three different types of atmospheric satellite sensors are used to test the versatility of the proposed meta-model and the applicability of this formal expression of A-SSOIM. Results show that the proposed meta-model can be reused in all kinds of atmospheric satellite sensors to enable the sharing of atmospheric satellite sensor information and potentially promoting the interoperability of these satellite sensors.

Finding the right spatially aware web service in a heterogeneous distributed environment using criteria such as service type, version, time, space, and scale has become a challenge in the integration of geospatial information services. A new method for retrieving Open Geospatial Consortium (OGC) Web Service (OWS) that deals with this challenge using page crawling, link detection, service capability matching, and ontology reasoning, is described in this paper. Its major components are distributed OWS, the OWS search engine, the OWS ontology generator, the ontology-based OWS catalog service, and the ontology-based multi-protocol OWS client. Experimental results show that the execution time of this proposed method equals only 0.26 of that of Nutch's method. In addition, the precision is much higher. Moreover, this proposed method can carry out complex OWS reasoning-based queries. It is being used successfully for the Antarctica multi-protocol OWS portal of the Geo-Information Web Service Portal of the Polar.

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.

For the vast and heterogeneous sensor resources of the Geospatial Sensor Web, discovery and utilization of these sensors for cooperative observation are often considered inaccurate, incomplete and time-lagged. This is in a large part due to a lack of a standard, comprehensive, observation capability contained sensor information model. A sensor information model covering the capabilities around the sensor and sensor system plays an important role in making these sensors sharable and interoperable for precise discovery and effective cooperative observation. In this paper, a Sensor Capability Information Model (SCIM) is proposed. The fundamental SCIM framework and its basic elements are formulated through analyzing the requirements and criteria for sensor capabilities information, generalizing and extracting from the existing geospatial or sensor-related metadata standards. SCIM mainly consists of the core observation capability of a sensor, as well as the computational capability, transmission capability, energy endurance capability and environmental adaptability capability. OGC Sensor Model Language (SensorML) is utilized as the expression form of the proposed SCIM. The versatility of the proposed information model is verified by three different types of earth observation sensors.

With the development of earth observation technology, various countries have established data systems, such as NASA, has established ECHO MODIS data system, NOAA has established CLASS data system, USGS has established the LANDSAT data system, INPE (Brazilian Space Information Bureau) has established the CBERS2 data system. The access protocols of these data system differ, while different access protocols of heterogeneous data system bring users with great difficulty of data finding. Sensor observation service (SOS) provides users with data query and find interface, so this paper constructs SOS middleware to couple with different data system access protocol, and encodes the founded data with Earth observation encoding format (EOP) format, which provides users with an unified and standard data finding method and conveys rich and useful metadata for external users.

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 V1.0” 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.

Disaster emergency is important for relieving the damage and mitigating the loss to human. Conventional disaster emergency processing presents a phase of separated, passive and time-lag response mode. Open Geospatial Consortium (OGC) sensor web works in associated environments that integrate heterogeneous sensor-intensive resources into an interoperable and controllable system through the web. This paper adopts Geospatial Sensor Web framework into the disaster emergency processing, which aims at realizing a Smart Disaster Emergency Processing (SDEP). It consists of vitalization of geospatial sensor web resources, SDEP service and SDEP application. The landslide of Three Gorges reservoir area has used as a SDEP scenario, which verifies the reasonability of SDEP architecture based on geospatial sensor web.

Various sensors play a very important role in our everyday life. Sensor model language is a vital part of the OGC for the description of the common sensor. SensorML not only describe properties of the sensor itself, but also the process about the sensor. Based on the SensorML which is coding with XML schema, we need an efficient way for modeling a sensor or a system. In this paper, firstly, the SensorML model is introduced, in which the SensorML schema is analyzed, and a common procedure for sensor modeling is defined. Secondly, the establishment of a common sensor model platform comes true using the dynamic generation, reflex, XML DOM in the .NET Framework 3.5. At last, the BJ-1 satellite was used as an example for the SensorML modeling. For the non-physical process chain, the red band and near infrared band of the BJ-1 satellite were as inputs for the NDVI calculation. After all, we validate the BJ-1 satellite SensorML instance.