Characterizing Coarse‐Scale Representativeness of in situ Soil Moisture Measurements from the International Soil Moisture Network

Abstract

In situ soil moisture measurements play a key role for a variety of large‐scale applications. A deep understanding of their quality, especially in terms of spatial representativeness, is crucial for reliably using them as reference data. This study assesses random errors in the coarse‐scale representation of in situ soil moisture measurements from more than 1400 globally distributed stations, drawn from the International Soil Moisture Network (ISMN), using the triple collocation method. The method was applied on the original measurements as well as on soil moisture anomalies. Error estimates were summarized for different networks, depths, and measurement principles and furthermore related to the respective climate class, soil type, average soil moisture condition, and soil moisture variability to find possible relationships between measurement errors and local properties. The average network error varies from about 0.02 to 0.06 m3m−3 with generally increasing error variability with increasing average error. Trends of (i) decreasing errors with increasing measurement depth and of (ii) increasing errors with increasing average soil moisture conditions and soil moisture variability were found for most networks and sensor types. The errors when looking into anomalies are in general lower than for absolute values. No statistically reliable trends for climate‐ and soil texture classes were found. These results highlighted the necessity of developing a comprehensive quality control process for in situ measurements to reliably exploit existing data sets and to select representative sites and sensors most appropriate for the requirements of a particular larger‐scale application.

Validation of Spaceborne and Modelled Surface Soil Moisture Products with Cosmic-Ray Neutron Probes

“…These can be used as additional quality scores of soil moisture time series. Several applications of TC to soil moisture time series have been published [43,[117][118][119][120][121][122][123][124][125][126][127]. Gruber et al [128] list the assumptions made to perform the TC analysis: (i) Linearity between the true soil moisture signal and the observations; (ii) signal and error stationarity; (iii) independency between the errors and the soil moisture signal (error orthogonality); and (iv) independency between the errors of three soil moisture time series (zero error cross-correlation).…”

Evaluation of satellite soil moisture products over Norway using ground-based observations

“…In situ observations have relatively high spatio-temporal resolution (order of centimetres and minutes, respectively) but only have local coverage, which may lead to poor representativeness for a large area Gruber et al, 2013). In this paper we use for the comparison with satellite data from ASCAT and AMSR-E the satellite grid points closest to the ground-based stations.…”

Multi-scale analysis of bias correction of soil moisture

“…In particular, it was used to estimate spatial point-tofootprint sampling errors (Miralles et al, 2011;Gruber et al, 2013), and correct biases in SM (Yilmaz and Crow, 2013). Based on an affine signal model and additive orthogonal error model, it assumes that representativity differences are manifested as additive and multiplicative biases.…”