Abstract. In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, longterm time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring soil moisture, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol.

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.

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. In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

Agricultural and hydrological applications could greatly benefit from soil moisture (SM) information at sub-field resolution and (sub-) daily revisit time. However, current operational satellite missions provide soil moisture information at either lower spatial or temporal resolution. Here, we downscale coarse resolution (25–36 km) satellite SM products with quasi-daily resolution to the field scale (30 m) using the random forest (RF) machine learning algorithm. RF models are trained with remotely sensed SM and ancillary variables on soil texture, topography, and vegetation cover against SM measured in the field. The approach is developed and tested in an agricultural catchment equipped with a high-density network of low-cost SM sensors. Our results show a strong consistency between the downscaled and observed SM spatio-temporal patterns. We found that topography has higher predictive power for downscaling than soil texture, due to the hilly landscape of the study area. Furthermore, including a proxy of vegetation cover results in considerable improvements of the performance. Increasing the training set size leads to significant gain in the model skill and expanding the training set is likely to further enhance the accuracy. When only limited in-situ measurements are available as training data, increasing the number of sensor locations should be favored over expanding the duration of the measurements for improved downscaling performance. In this regard, we show the potential of low-cost sensors as a practical and cost-effective solution for gathering the necessary observations. Overall, our findings highlight the suitability of using ground measurements in conjunction with machine learning to derive high spatially resolved SM maps from coarse-scale satellite products.