Many physical, chemical and biological processes taking place at the land surface are strongly influenced by the amount of water stored within the upper soil layers. Therefore, many scientific disciplines require soil moisture observations for developing, evaluating and improving their models. One of these disciplines is meteorology where soil moisture is important due to its control on the exchange of heat and water between the soil and the lower atmosphere. Soil moisture observations may thus help to improve the forecasts of air temperature, air humidity and precipitation. However, until recently, soil moisture observations had only been available over a limited number of regional soil moisture networks. This has hampered scientific progress as regards the characterisation of land surface processes not just in meteorology but many other scientific disciplines as well. Fortunately, in recent years, satellite soil moisture data have increasingly become available. One of the freely available global soil moisture data sets is derived from the backscatter measurements acquired by the Advanced Scatterometer (ASCAT) that is a C-band active microwave remote sensing instrument flown on board of the Meteorological Operational (METOP) satellite series. ASCAT was designed to observe wind speed and direction over the oceans and was initially not foreseen for monitoring soil moisture over land. Yet, as argued in this review paper, the characteristics of the ASCAT instrument, most importantly its wavelength (5.7 cm), its high radiometric accuracy, and its multiple-viewing capabilities make it an attractive sensor for measuring soil moisture. Moreover, given the operational status of ASCAT, and its promising long-term prospects, many geoscientific applications might benefit from using ASCAT soil moisture data. Nonetheless, the ASCAT soil moisture product is relatively complex, requiring a good understanding of its properties before it can be successfully used in applications. To provide a comprehensive overview of the major characteristics and caveats of the ASCAT soil moisture product, this paper describes the ASCAT instrument and the soil moisture processor and near-real-time distribution service implemented by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). A review of the most recent validation studies shows that the quality of ASCAT soil moisture product is-with the exception of arid environments-comparable to, and over some regions (e.g. Europe) even better than currently available soil moisture data derived from passive microwave sensors. Further, a review of applications studies shows that the use of the ASCAT soil moisture product is particularly advanced in the fields of numerical weather prediction and hydrologic modelling. But also in other application areas such as yield monitoring, epidemiologic modelling, or societal risks assessment some first progress can be noted. Considering the generally positive evaluation results, it is expected that the ASCAT soil moisture product ...

The scatterometers onboard the European Remote Sensing satellites (ERS-1 & ERS-2) and the METeorological OPerational satellite (METOP) have been shown to be useful for surface soil moisture retrieval using the so-called TU-Wien change detection method. This paper presents an improved soil moisture retrieval algorithm based on the existing TU-Wien method but with new parameterization as well as a series of modifications. The new algorithm, WAter Retrieval Package 5 (WARP5), copes with some limitations identified in the earlier method WARP4 and provides the possibility of migrating soil moisture retrieval from ERS-SCAT to METOP-ASCAT data. The WARP5 algorithm results in a more robust and spatially uniform soil moisture product, thanks to its new processing elements, including a method for the correction of azimuthal anisotropy of backscatter, a comprehensive noise model, and new techniques for calculation of the model parameters. Cross-comparisons of WARP4 and WARP5 data sets with the Oklahoma Mesonet in situ observations and also with European Centre of Medium Range Weather Forcast (ECMWF) ReAnalysis (ERA-Interim) global modeled data show that the new algorithm has a better performance and effectively corrects retrieval errors in certain areas.

[1] This article presents first results of deriving relative surface soil moisture from the METOP-A Advanced Scatterometer. Retrieval is based on a change detection approach which has originally been developed for the Active Microwave Instrument flown onboard the European satellites ERS-1 and ERS-2. Using model parameters derived from eight years of ERS scatterometer data, first global soil moisture maps have been produced from ASCAT data. The ASCAT data were distributed by EUMETSAT for validation purposes during the ASCAT product commissioning activities. Several recent cases of drought and excessive rainfall are clearly visible in the soil moisture data. The results confirm that seamless soil moisture time series can be expected from the series of two ERS and three METOP scatterometers, providing global coverage on decadal time scales (from 1991 to about 2021). Thereby, operational, nearreal-time ASCAT soil moisture products will become available for weather prediction and hydrometeorological applications.

Abstract. The role and the importance of soil moisture for meteorological, agricultural and hydrological applications is widely known. Remote sensing offers the unique capability to monitor soil moisture over large areas (catchment scale) with, nowadays, a temporal resolution suitable for hydrological purposes. However, the accuracy of the remotely sensed soil moisture estimates has to be carefully checked. The validation of these estimates with in-situ measurements is not straightforward due the well-known problems related to the spatial mismatch and the measurement accuracy. The analysis of the effects deriving from assimilating remotely sensed soil moisture data into hydrological or meteorological models could represent a more valuable method to test their reliability. In particular, the assimilation of satellite-derived soil moisture estimates into rainfall-runoff models at different scales and over different regions represents an important scientific and operational issue.In this study, the soil wetness index (SWI) product derived from the Advanced SCATterometer (ASCAT) sensor onboard of the Metop satellite was tested. The SWI was firstly compared with the soil moisture temporal pattern derived from a continuous rainfall-runoff model (MISDc) to assess its relationship with modeled data. Then, by using a simple data assimilation technique, the linearly rescaled SWI that matches the range of variability of modelled data (denoted as SWI * ) was assimilated into MISDc and the model performance on flood estimation was analyzed. Moreover, three synthetic experiments considering errors on rainfall, model parameters and initial soil wetness conditions were carried out. These experiments allowed to further investiCorrespondence to: L. Brocca (l.brocca@irpi.cnr.it) gate the SWI potential when uncertain conditions take place. The most significant flood events, which occurred in the period 2000-2009 on five subcatchments of the Upper Tiber River in central Italy, ranging in extension between 100 and 650 km 2 , were used as case studies. Results reveal that the SWI derived from the ASCAT sensor can be conveniently adopted to improve runoff prediction in the study area, mainly if the initial soil wetness conditions are unknown.

Abstract.A long term data acquisition effort of profile soil moisture is currently underway at 13 automatic weather stations located in Southwestern France. In this study, the soil moisture measured in-situ at 5 cm is used to evaluate the normalised surface soil moisture (SSM) estimates derived from coarse-resolution (25 km) active microwave data of the AS-CAT scatterometer instrument (onboard METOP), issued by EUMETSAT for a period of 6 months (April-September) in 2007. The seasonal trend is removed from the satellite and in-situ time series by considering scaled anomalies. One station (Mouthoumet) of the ground network, located in a mountainous area, is removed from the analysis as very few ASCAT SSM estimates are available. No correlation is found for the station of Narbonne, which is close to the Mediterranean sea. On the other hand, nine stations present significant correlation levels. For two stations, a significant correlation is obtained when considering only part of the ASCAT data. The soil moisture measured in-situ at those stations, at 30 cm, is used to estimate the characteristic time length (T ) of an exponential filter applied to the ASCAT product. The best correlation between a soil water index derived from AS-CAT and the in-situ soil moisture observations at 30 cm is obtained with a T -value of 14 days.