No 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.

Abstract. The validation of geophysical data sets (e.g. derived from models, exploration techniques or remote sensing) presents a formidable challenge as all products are inherently different and subject to errors. The collocation technique permits the retrieval of the error variances of different data sources without the need to specify one data set as a reference. In addition calibration constants can be determined to account for biases and different dynamic ranges. The method is frequently applied to the study and comparison of remote sensing, in-situ and modelled data, particularly in hydrology and oceanography. Previous studies have almost exclusively focussed on the validation of three data sources; in this paper it is shown how the technique generalizes to an arbitrary number of data sets. It turns out that only parts of the covariance structure can be resolved by the collocation technique, thus emphasizing the necessity of expert knowledge for the correct validation of geophysical products. Furthermore the bias and error variance of the estimators are derived with particular emphasis on the assumptions necessary for establishing those characteristics. Important properties of the method, such as the structural deficiencies, dependence of the accuracy on the number of measurements and the impact of violated assumptions, are illustrated by application to simulated data.

Abstract:In permafrost areas, seasonal freeze-thaw cycles result in upward and downward movements of the ground. For some permafrost areas, long-term downward movements were reported during the last decade. We measured seasonal and multi-year ground movements in a yedoma region of the Lena River Delta, Siberia, in 2013-2017, using reference rods installed deep in the permafrost. The seasonal subsidence was 1.7 ± 1.5 cm in the cold summer of 2013 and 4.8 ± 2 cm in the warm summer of 2014. Furthermore, we measured a pronounced multi-year net subsidence of 9.3 ± 5.7 cm from spring 2013 to the end of summer 2017. Importantly, we observed a high spatial variability of subsidence of up to 6 cm across a sub-meter horizontal scale. In summer 2013, we accompanied our field measurements with Differential Synthetic Aperture Radar Interferometry (DInSAR) on repeat-pass TerraSAR-X (TSX) data from the summer of 2013 to detect summer thaw subsidence over the same study area. Interferometry was strongly affected by a fast phase coherence loss, atmospheric artifacts, and possibly the choice of reference point. A cumulative ground movement map, built from a continuous interferogram stack, did not reveal a subsidence on the upland but showed a distinct subsidence of up to 2 cm in most of the thermokarst basins. There, the spatial pattern of DInSAR-measured subsidence corresponded well with relative surface wetness identified with the near infra-red band of a high-resolution optical image. Our study suggests that (i) although X-band SAR has serious limitations for ground movement monitoring in permafrost landscapes, it can provide valuable information for specific environments like thermokarst basins, and (ii) due to the high sub-pixel spatial variability of ground movements, a validation scheme needs to be developed and implemented for future DInSAR studies in permafrost environments.