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• W2051988857 abstract "Determining the best way to efficiently use limited water resources, for food and energy-dedicated crops, has become crucial due to the rise in extreme events (floods/droughts) and higher variability in rainfall attributed to global climate change. Changing climate conditions will require new crops to be adapted to a changing agricultural environment. Reliable information on seasonal trends in crop growth and evapotranspiration with associated uncertainty/confidence ranges is crucial to guide the development of new crops and management strategies to cope with future climate. Given that crop growth is strongly coupled to soil moisture, developing reliable growth curves requires a detailed understanding of soil moisture at the field-scale. Typically, it is impractical to collect soil samples to adequately assess soil moisture that represents both spatial distribution at the field-scale and temporal dynamics on the scale of a growing season (e.g. 110 days for cereals). A novel way to address soil moisture monitoring challenges is through an integrated, agro-ecosystems-level approach using an integrated sensing system that can link data from multiple platforms (wireless sensors, satellites, airborne imagery, near real-time climate stations). Assimilated data can, then, be fed into predictive models to generate reference crop growth curves and predict regionally-specific yield potentials. However, integrated sensing requires interagency cooperation, common data processing standards and long-term, timely access to data. Large databases need to be reusable by various organizations and accessible, in the future, with comprehensive metadata. During the 2012 growing season a feasibility study was conducted which involved measuring field-scale soil moisture with sensor network technology. The experiment utilized radially-distributed sensors for tracking in-season soil moisture. OpenGIS-compliant services and standards were utilized to provide long-term access to sensor data and construct corresponding metadata. Sensor Model Language, an inter-operable metadata format, was used to create documentation for the sensor system and sensing components. Two different third party implementations of the Sensor Observation Service were tested for providing long-term access to the data. This work discusses a set of key recommendations for monitoring field-scale soil moisture dynamics for integration with remote sensing and models, including: (1) Improved in situ sensing technology that would allow for less restrictive soil moisture measurements. (2) Integration of field-scale in situ networks with regional remote sensing monitoring. (3) The development of software and web services to integrate data from multiple sources with models for decision support." @default.
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• W2051988857 date "2014-09-01" @default.
• W2051988857 modified "2023-10-13" @default.
• W2051988857 title "Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support" @default.
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• W2051988857 doi "https://doi.org/10.1016/j.compag.2014.02.011" @default.
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