Data Description

Data Description

For sites that run off of line power our sensor data collection strategy utilizing a ten-minute acquisition loop is as follows:

  1. Soil sensors collect VWC, EC, temperature, and soil CO2 concentration data
  2. Weather sensors collect ambient outside air temperature, precipitation input, and chamber box temperature
  3. One of six soil respiration autochambers is randomly selected and sampled for CO2 flux.
  4. Loop repeats, data is sent via telemetry back to UNH for quality assurance and quality control analysis and then pushed to the web for visualization every hour.

Remote sites are powered from solar and their sampling strategy is one acquisition loop per hour, sometimes less often as solar inputs change with the season.

Quality control (QC) for environmental sensor data begins with preventative measures taken in the field and continues throughout the data processing workflow. The team is incorporating an automated data quality review and flagging procedure to evaluate and flag raw sensor data, generating a data product for provisional release in near real-time (level 1a). Subsequent quality control includes a more detailed data review/flagging process by the data management team (level 1b). Near real-time provisional data displayed in the EPSCoR data viewer will be either level 1a or 1b. Higher level data products (level 2 and above; see below), will be available in the future.

The Implementation of QC varies for each sensor. A set of rules for automated QC have been developed for each parameter, and flagging may be based on one or more of the following tests: voltage limits, sensor limits, missing values, persistent values, slope exceedance, spatial and internal inconsistency etc, as described in Campbell et al. 2013. All QC tests and data processing history are documented in the associated metadata files.

The NH EPSCoR-funded infrastructure of web-servers and availability of custom implemented and publicly available/open-source tools (e.g., GCE Toolbox for Matlab) are used to process the data and visualize Level 1 data in near real-time.

Potential Uses

Data will be used to examine how soil properties and processes change spatially and temporally as the climate in the region changes. Data will also provide input to ecosystem models used to predict how ecosystems in the region will respond to climate and land-use change. It can also be used as an outreach and teaching resource.