Global automated quality control of in situ soil moisture data from the International Soil Moisture Network
Global automated quality control of in situ soil moisture data from the International Soil Moisture Network (in Remote sensing for vadose zone hydrology, Binayak P. Mohanty (editor), Michael Cosh (editor), Venkat Lakshmi (editor) and Carsten Montzka (editor))
Vadose Zone Journal (August 2013) 12 (3)
- Asia
- atmospheric precipitation
- automated analysis
- case studies
- climate
- data bases
- data integration
- data management
- data processing
- equations
- errors
- Eurasia
- Europe
- geophysical methods
- global
- in situ
- information management
- measurement
- moisture
- networks
- observations
- quality control
- soils
- statistical analysis
- time series analysis
- United States
- unsaturated zone
- data sets
- International Soil Moisture Network
- data portals
The International Soil Moisture Network (ISMN) was initiated in 2009 to support calibration and validation of remote sensing products and land surface models, and to facilitate studying the behavior of our climate over space and time. The ISMN does this by collecting and harmonizing soil moisture data sets from a large variety of individually operating networks and making them available through a centralized data portal. Due to the diversity of climatological conditions covered by the stations and differences in measurement devices and setup, the quality of the measurements is highly variable. Therefore, appropriate quality characterization is desirable for a correct use of the data sets. This study presents a new, automated quality control system for soil moisture measurements contained in the ISMN. Two types of quality control procedures are presented. The first category is based on the geophysical dynamic range and consistency of the measurements. It includes flagging values exceeding a certain threshold and checking the validity of soil moisture variations in relation to changes in soil temperature and precipitation. In particular, the usability of global model- or remote sensing-based temperature and precipitation data sets were tested for this purpose as an alternative to in situ measurements, which are often not recorded at the soil moisture sites themselves. The second category of procedures analyzes the shape of the soil moisture time series to detect outliers (spikes), positive and negative breaks, saturation of the signal, and unresponsive sensors. All methods were first validated and then applied to all the data sets currently contained in the ISMN. A validation example of an AMSR-E satellite and a GLDAS-Noah model product showed a small but positive impact of the flagging. On the basis of the positive results of this study we will add the flags as a standard attribute to all soil moisture measurements contained in the ISMN.
