No abstract

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.

In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term 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. To overcome many of these limitations, the International Soil Moisture Network (ISMN; <a href="http://www.ipf.tuwien.ac.at/insitu" target="_blank">http://www.ipf.tuwien.ac.at/insitu</a>) was initiated to serve as a centralized data hosting facility where globally available in situ soil moisture measurements from operational networks and validation campaigns are collected, harmonized, and made available to users. Data collecting networks share their soil moisture datasets with the ISMN on a voluntary and no-cost basis. Incoming soil moisture data are automatically transformed into common volumetric soil moisture units and checked for outliers and implausible values. Apart from soil water measurements from different depths, important metadata and meteorological variables (e.g., precipitation and soil temperature) are stored in the database. These will assist the user in correctly interpreting the soil moisture data. The database is queried through a graphical user interface while output of data selected for download is provided according to common standards for data and metadata. Currently (status January 2011), the ISMN contains data of 16 networks and more than 500 stations located in the North America, Europe, Asia, and Australia. The time period spanned by the entire database runs from 1952 until the present, although most datasets have originated during the last decade. The database is rapidly expanding, which means that both the number of stations and the time period covered by the existing stations are still growing. Hence, it will become an increasingly important resource for validating and improving satellite-derived soil moisture products and studying climate related trends. As the ISMN is animated by the scientific community itself, we invite potential networks to enrich the collection by sharing their in situ soil moisture data

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.

Abstract. Following the launch of ESA's Soil Moisture andOcean Salinity (SMOS) mission, it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In the first demonstration study, sea ice thickness up to 50 cm has been derived using a semi-empirical algorithm with constant tie-points. Here, we introduce a novel iterative retrieval algorithm that is based on a thermodynamic sea ice model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within the SMOS spatial resolution are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS-based sea ice thickness data set from 2010 on. The data set is compared to and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study (Kaleschke et al., 2012).