Integrated sensing of soil moisture at the field-scale: sampling, modelling and sharing for improved agricultural decision-support.

Phillips, A.J.L., Newlands, N.K., Liang, S.H.L., and Ellert, B.H. (2014). "Integrated sensing of soil moisture at the field-scale: sampling, modelling and sharing for improved agricultural decision-support.", Computers and Electronics in Agriculture, 107, pp. 73-88.


Determining the best way to allocate 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. Crop growth curves, based on evapotranspiration with associated uncertainty/confidence ranges, could reliably guide regional crop adaptation decisions. Given that crop growth is strongly coupled to soil moisture, developing reliable crop curves require a detailed understanding of soil moisture at the field-scale. It is especially difficult to sample soil moisture in order to obtain the best field-scale representation of the spatial distribution and the growing season dynamics. 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 input 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 wireless sensor based technology. The experiment utilized a radial-based sensor sampling design 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 and integration with remote sensing and models. 1) In-situ sensing technology advancements that would allow for less restrictive soil sampling designs. 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.

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