This data package is formatted as a hymetDP (Hydrological-Meteorological Data Pattern). For more information on hymetDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-jrn/210437113/19. The abstract below was extracted from the Level 0 data package and is included for context:
30-minute Soil Moisture data is measured at 10cm, 20 cm, and 30 cm soil depths at the NPP M-NORT substation located in center of 70m x 70m Jornada LTER NPP M-NORT site. Collection of soil volumetric water content data at Jornada LTER NPP sites, New Mexico, supports the environmental monitoring objectives of the Jornada LTER monitoring program that look at plant-soil water dynamics. Volumetric water content, bulk electrical conductivity, soil temperature, and bulk dielectric permittivity are measured every 30 minutes. This is an ONGOING dataset.
Collection of soil volumetric water content data at Jornada LTER NPP sites, New Mexico, supports the environmental monitoring objectives of the Jornada LTER monitoring program that look at plant-soil water dynamics. Measurements are made every 30 minutes and downloaded hourly from each remote site via a 900 MHz spread spectrum wireless radio network. Variables measured include Volumetric Water Content, Bulk Electrical Conductivity, Soil Temperature, Bulk Dielectric Permittivity, Period Average, and Voltage Ratio.
The GCE Data Toolbox is a comprehensive library of functions for metadata-based analysis quality control, transformation and management of ecological data sets. The toolbox is based on the GCE Data Structure, a MATLAB specification for storing tabular data along with all information required to interpret the data and generate formatted metadata (documentation). Metadata fields in the structure are queried by toolbox functions for all operations, allowing functions to process and format values appropriately based on the type of information they represent. This semantic processing approach supports highly automated and intelligent data analysis and ensures data set validity throughout all processing steps.