Google Fusion Tables for Managing Soil Moisture Sensor Observations

The World-Wide Sensor Web is generating tremendous amount of real-time sensor data streams and will enable scientists to observe phenomena that are previously unobservable. As the concept of sensor web is to connect all the sensors and their data to achieve shared goals, improving the openness and accessibility of sensor data is important. The Open Geospatial Consortium Sensor Observation Service (SOS) defines standard web service protocols for sharing sensor data online in an interoperable manner. However, SOS has a relatively weak ecosystem, which makes it difficult to build and consume, and it only supports predefined queries. On the other hand, the OASIS1 Open Data Protocol (OData) has a strong ecosystem and flexible query functions. But the soft-typing approach of OData requires it to have a commonly agreed data model to be interoperable. As we argue that the two standards can benefit from each other, we propose a sensor data mediator solution and define an SOS entity data model for OData (SOS-OData) to bridge these two standards. Our prototype demonstrates that the proposed system can convert between existing SOS and SOS-OData services. As a result, we not only can consume SOS data with the flexible OData query functions, but can also easily build an SOS-compliant service with the strong OData ecosystem. We argue that the bridging between these two standards would consequently lead us to the vision of open data for sensor web.

Semantic mediation of observation datasets through Sensor Observation Services

The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) is a significant research project that performs airborne, satellite-borne and ground based remote sensing experiments at various scales by coordinating interdisciplinary researchers from different institutions and projects. Therefore, in order to discover, access and process sensors within the heterogeneous systems, we use the Sensor Model Language (Sensor ML) to model these sensors and sensor systems in the stations, and use Sensor Observation Service (SOS) to display the sensor information and observation data information. In this paper, we firstly introduce the Sensor ML and SOS, then mainly present a model of sensor station and the application of this model in SOS.

Assessing the status and trends of the environment requires the collection, management and publication of spatially referred observations. Since many years, Long-Term Ecological Research sites in Europe collect ecological data, resulting in long-term data series. Nowadays, advanced software tools can enable discovering, accessing, and distributing collected data in a user friendly way. Based on Open Geospatial Consortium (OGC) standards, Web based Geographic Information System allows access to interoperable distributed repositories of observations. The present paper gives an overview of the methods and solutions proposed and tested in the LIFE+ project EnvEurope for the community of researchers of the Long-Term Ecosystem Research (LTER) Network in Europe. These solutions consist in a straightforward online environment for metadata management and discovery, shaped on the ecological community and its practices; components of a Spatial Data Infrastructure including both a network of repositories deploying observations via OGC SOS (Sensor Observation Service) services and Web user interfaces to access and visualize them. The success of the presented approach is linked to the development and availability of easy to define, ready to use tools, enabling site managers to create their own repositories and services.

This work demonstrates a low-cost, miniature data repository proof-of-concept. Such a system needs to be resilient to power and network failures, and expose adequate processing power for persistent, long-term storage. Additional services are required for interoperable data sharing and visualization. We designed and implemented a software tool called Airchive to run on a Raspberry Pi, in order to assemble a data repository for archiving and openly sharing timeseries data. Airchive employs a relational database for storing data and implements two standards for sharing data (namely the Sensor Observation Service by the Open Geospatial Consortium and the Protocol for Metadata Harvesting by the Open Archives Initiative). The system is demonstrated in a realistic indoor air pollution data acquisition scenario in a four-month experiment evaluating its autonomy and robustness under power and network disruptions. A stress test was also conducted to evaluate its performance against concurrent client requests.

We developed a mechanism for seamlessly providing weather data and long-term historical climate data from a gridded data source through an international standard web API, which was the Sensor Observation Service (SOS) defined by the Open Geospatial Consortium (OGC). The National Agriculture and Food Research Organization (NARO) Japan has been providing gridded climate data consisting of nine daily meteorological variables, which are average, minimum, maximum of air temperature, relative humidity, sunshine duration, solar radiant exposure, downward longwave radiation, precipitation and wind speed for 35 years covering Japan. The gridded data structure is quite useful for spatial analysis, such as developing crop suitability maps and monitoring regional crop development. Individual farmers, however, make decisions using historical climate information and forecasts for an incoming cropping season of their farms. In this regard, climate data at a point-based structure are convenient for application development to support farmers’ decisions. Through the proposed mechanism in this paper, the agricultural applications and analysis can request point-based climate data from a gridded data source through the standard API with no need to deal with the complicated hierarchical data structure of the gridded climate data source. Clients can easily obtain data and metadata by only accessing the service endpoint. The mechanism also provides several web bindings and data encodings for the clients’ convenience. Caching, including the pre-caching mechanism, was developed and evaluated to secure an effective response time. The mechanism enhances the accessibility and usability of the gridded weather data source, as well as SOS API for agricultural applications.

A conceptual data modeling framework with four levels of abstraction for environmental information

Monitoring the environment is critical for scientists to understand the environmental dynamics. However, the traditional monitoring systems such as sensor networks are usually labor-intensive and complicated to deploy. As the concept of citizen sensing has been proposed to include volunteers into the environmental monitoring systems, a key to realize the citizen sensing vision is to empower citizens with the low-cost and easy-to-use sensing devices. In this paper, we focus on two technologies that have the potential to realize the citizen sensing vision: the Internet of Things (IoT) and the world-wide sensor web. In order to address the issues from the decentralized and heterogeneous nature of IoT devices and sensors, we propose the TinySOS service, a tiny web server hosting a light-weight profile of Open Geospatial Consortium (OGC) Sensor Observation Service (SOS). By hosting open standard sensor web services on the IoT devices, not only the devices become self-describable, self-contained, and interoperable, but also the collected observations are accessible via the Internet as soon as they are measured. In this case, the sensor web can provide real-time sensor data streams in a much higher spatial and temporal resolution, which consequently allows scientists to observe phenomena that were previously unobservable.

Soil hydraulic properties (SHPs) and soil moisture (SM) are fundamental to describing and predicting water and energy cycles at the land surface, and for regulating evapotranspiration, infiltration and runoff. However, information about these soil properties from existing datasets is often scarce and inaccurate for high and cold mountainous areas such as the Qinghai-Tibet Plateau (QTP), which hampers our understanding of hydrological and energy cycle processes over large mountainous areas like the QTP. Based on soil profile data at depths of 5 cm and 25 cm from 238 sampling sites, and on soil data from 32 SM monitoring stations at depths of 5 cm, 15 cm, 25 cm, 40 cm, and 60 cm, we have compiled a SHP and SM dataset for a high and cold mountainous area, Northeastern QTP. We used this dataset to explore the large-scale spatial and temporal variability of SHPs and of SM across the study area. Our evaluation of several existing SHP datasets, SM datasets derived from remote-sensing, reanalysis and data assimilation, showed that SHPs (soil texture, bulk density, and soil saturated hydraulic conductivity) in these datasets are biased, and do not capture the spatial variability recorded in the in-situ observations. When comparing with the in-situ SM observations, the SM product derived from remote-sensing was more reliable than the SM product derived from reanalysis data (which had a higher bias), and than the data assimilation product (which did not capture SM temporal variability). The in situ observation dataset presented here provides unique and important information about the SHP variability and long-term SM trends at a large-scale, high and cold mountainous area, and thus offers opportunity for further understanding of water cycle and energy exchange processes over the QTP.

<strong class="journal-contentHeaderColor">Abstract.</strong> Soil hydraulic properties (SHPs) and soil moisture (SM) are fundamental to describing and predicting water and energy cycles at the land surface, and for regulating evapotranspiration, infiltration and runoff. However, information about these soil properties from existing datasets is often scarce and inaccurate for high and cold mountainous areas such as the Qinghai-Tibet Plateau (QTP), which hampers our understanding of hydrological and energy cycle processes over large mountainous areas like the QTP. Based on soil profile data at depths of 5 cm and 25 cm from 238 sampling sites, and on soil data from 32 SM monitoring stations at depths of 5 cm, 15 cm, 25 cm, 40 cm, and 60 cm, we have compiled a SHP and SM dataset for a high and cold mountainous area, Northeastern QTP. We used this dataset to explore the large-scale spatial and temporal variability of SHPs and of SM across the study area. Our evaluation of several existing SHP datasets, SM datasets derived from remote-sensing, reanalysis and data assimilation, showed that SHPs (soil texture, bulk density, and soil saturated hydraulic conductivity) in these datasets are biased, and do not capture the spatial variability recorded in the in-situ observations. When comparing with the in-situ SM observations, the SM product derived from remote-sensing was more reliable than the SM product derived from reanalysis data (which had a higher bias), and than the data assimilation product (which did not capture SM temporal variability). The in situ observation dataset presented here provides unique and important information about the SHP variability and long-term SM trends at a large-scale, high and cold mountainous area, and thus offers opportunity for further understanding of water cycle and energy exchange processes over the QTP.

Soil hydraulic properties (SHPs) and soil moisture (SM) are fundamental to describing and predicting water and energy cycles at the land surface, and for regulating evapotranspiration, infiltration and runoff. However, information about these soil properties from existing datasets is often scarce and inaccurate for high and cold mountainous areas such as the Qinghai-Tibet Plateau (QTP), which hampers our understanding of hydrological and energy cycle processes over large mountainous areas like the QTP. Based on soil profile data at depths of 5 cm and 25 cm from 238 sampling sites, and on soil data from 32 SM monitoring stations at depths of 5 cm, 15 cm, 25 cm, 40 cm, and 60 cm, we have compiled a SHP and SM dataset for a high and cold mountainous area, Northeastern QTP. We used this dataset to explore the large-scale spatial and temporal variability of SHPs and of SM across the study area. Our evaluation of several existing SHP datasets, SM datasets derived from remote-sensing, reanalysis and data assimilation, showed that SHPs (soil texture, bulk density, and soil saturated hydraulic conductivity) in these datasets are biased, and do not capture the spatial variability recorded in the in-situ observations. When comparing with the in-situ SM observations, the SM product derived from remote-sensing was more reliable than the SM product derived from reanalysis data (which had a higher bias), and than the data assimilation product (which did not capture SM temporal variability). The in situ observation dataset presented here provides unique and important information about the SHP variability and long-term SM trends at a large-scale, high and cold mountainous area, and thus offers opportunity for further understanding of water cycle and energy exchange processes over the QTP.

Abstract. The Unmanned Aerial Vehicle (UAV) is an emerging technology being adapted for a wide range of applications. Real-time monitoring is essential to enhance the effectiveness of UAV applications. Sensor networks are networks constructed from various sensor nodes. International standard such as OGC's SOS (Sensor Observation Service) makes it possible to share sensor data with other systems as well as to provide accessibility to globally distributed users. In this paper, we propose a system combining UAV technology and sensor network technology to use an UAV as a mobile node of sensor network so that the sensor data from UAV is published and shared real-time. A UAV can extend the observation range of a sensor network to remote areas where it is usually difficult to access such as disaster area. We constructed a UAV system using remote-controlled helicopter and various sensors such as GPS, gyrocompass, laser range finder, Digital camera and Thermometer. Furthermore, we extended the Sensor Observation Service (SOS) and Sensor Service Grid (SSG) to support mobile sensor nodes. Then, we conducted experiments of flying the helicopter over an area of the interest. During the flight, the system measured environmental data using its sensors and captured images of the ground. The data was sent to a SOS node as the ground station via Wi-Fi which was published using SSG to give real- time access to globally distributed users.

• A data service system for the selected SMAP data and its derivative data products over CONUS is developed. • All map and geoprocessing services are implemented via the OGC standard interface. • The data service system is interoperable with web and desktop clients. • Monitoring and analyzing U.S. cropland soil moisture conditions are significantly simplified. Soil moisture is an essential parameter to understand crop conditions throughout the growing season. Collecting soil moisture data by field observation is labor-intensive, especially when attempting to obtain Conterminous United States (CONUS) geographic coverage. In addition, using soil moisture for assessing current and future crop conditions is best realized by combining soil moisture estimates with concurrent observations of crop conditions. However, until recently, this capability has not been available to the public. In this paper, we present an interoperable data service application system, the Crop Condition and Soil Moisture Analytics (Crop-CASMA) system, that facilitates the retrieval, analysis, visualization, and sharing of soil moisture data for the CONUS. This system delivers a variety of satellite remote sensing based data products that are derived from Soil Moisture Active Passive (SMAP) Level-4 data and SMAP Thermal Hydraulic disaggregation of Soil Moisture (THySM) data, as well as vegetation index data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations. To make services interoperable and reusable, all data products are disseminated via Open Geospatial Consortium (OGC) Web Map Service (WMS) and Web Coverage Service (WCS) interface standards. Additionally, a suite of geoprocessing operations, such as geospatial statistics, time-series profile generation, PDF map production, and image composition, has been implemented in the OGC Web Processing Service (WPS) interface standard. The implementation shows the proposed web service system can significantly simplify the mapping and quantitative analysis of soil moisture and crop condition over U.S. cropland. In addition, it is interoperable with GIS software and has been successfully integrated with web-based applications.

Abstract. Increased use of prescribed fire by land managers and the increasing likelihood of wildfires due to climate change require an improved modeling capability of extreme heating of soils during fires. This issue is addressed here by developing and testing the soil (heat–moisture–vapor) HMV-model, a 1-D (one-dimensional) non-equilibrium (liquid–vapor phase change) model of soil evaporation that simulates the coupled simultaneous transport of heat, soil moisture, and water vapor. This model is intended for use with surface forcing ranging from daily solar cycles to extreme conditions encountered during fires. It employs a linearized Crank–Nicolson scheme for the conservation equations of energy and mass and its performance is evaluated against dynamic soil temperature and moisture observations, which were obtained during laboratory experiments on soil samples exposed to surface heat fluxes ranging between 10 000 and 50 000 W m−2. The Hertz–Knudsen equation is the basis for constructing the model's non-equilibrium evaporative source term. Some unusual aspects of the model that were found to be extremely important to the model's performance include (1) a dynamic (temperature and moisture potential dependent) condensation coefficient associated with the evaporative source term, (2) an infrared radiation component to the soil's thermal conductivity, and (3) a dynamic residual soil moisture. This last term, which is parameterized as a function of temperature and soil water potential, is incorporated into the water retention curve and hydraulic conductivity functions in order to improve the model's ability to capture the evaporative dynamics of the strongly bound soil moisture, which requires temperatures well beyond 150 °C to fully evaporate. The model also includes film flow, although this phenomenon did not contribute much to the model's overall performance. In general, the model simulates the laboratory-observed temperature dynamics quite well, but is less precise (but still good) at capturing the moisture dynamics. The model emulates the observed increase in soil moisture ahead of the drying front and the hiatus in the soil temperature rise during the strongly evaporative stage of drying. It also captures the observed rapid evaporation of soil moisture that occurs at relatively low temperatures (50–90 °C), and can provide quite accurate predictions of the total amount of soil moisture evaporated during the laboratory experiments. The model's solution for water vapor density (and vapor pressure), which can exceed 1 standard atmosphere, cannot be experimentally verified, but they are supported by results from (earlier and very different) models developed for somewhat different purposes and for different porous media. Overall, this non-equilibrium model provides a much more physically realistic simulation over a previous equilibrium model developed for the same purpose. Current model performance strongly suggests that it is now ready for testing under field conditions.

[EN] SAFE-ECH is an innovative intelligent AAL open source system for monitoring nursing homes, that creates an Ambient Intelligent environment in a residence by collecting and storing sensor monitoring data, performing intelligent data analysis and specific actions to enhance the safety, comfort and efficient care of aged people. Our system implements open standards of the Open Geospatial Consortium complying with Observations &amp; Measurements Schema (O&amp;M), SensorML and Sensor Web Enablement (SWE) specifications. Our system adapts to the specific needs of each nursing home, integrating the required sensors, actuators, rules and services. It is scalable and allows the management of several residences simultaneously.