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In this study, the combined surface status and surface soil moisture products retrieved by the ASCAT sensor within the ESA-DUE Permafrost project are compared to the hydrological outputs of the land surface model ORCHIDEE over Northern Eurasia. The objective is to derive broad conclusions as to the strengths and weaknesses of hydro-logical modelling and, to a minor extent, remote sensing of soil moisture over an area where data is rare and hydrological modelling is though crucial for climate and ecological applications. The spatial and temporal resolutions of the ASCAT products make them suitable for comparison with model outputs. Modelled and remotely-sensed surface frozen and unfrozen statuses agree reasonably well, which allows for a seasonal comparison between modelled and observed (liquid) surface soil moisture. The atmospheric forcing and the snow scheme of the land surface model are identified as causes of moderate model-to-data divergence in terms of surface status. For unfrozen soils, the modelled and remotely-sensed surface soil moisture signals are positively correlated over most of the study area. The correlation deteriorates in the North-Eastern Siberian regions, which is consistent with the lack of accurate model parameters and the scarcity of meteorological data. The model shows a reduced ability to capture the main seasonal dynamics and spatial patterns of observed surface soil moisture in Northern Eurasia, namely a characteristic spring surface moistening resulting from snow melt and flooding. We hypothesize that these weak performances mainly originate from the non-representation of flooding and surface ponding in the model. Further identified limitations proceed from the coarse treatment of the hydro-logical specificities of mountainous areas and spatial inaccuracies in the meteorological forcing in remote, North-Eastern Siberian areas. Investigations are currently underway to determine to which extent plausible inaccuracies in the satellite data could also contribute to the diagnosed model-to-data discrepancies. [ABSTRACT FROM AUTHOR]

Information on soil surface state is valuable for many applications such as climate studies and monitoring of permafrost regions. C-band scatterometer data indicate good potential to deliver information on surface freeze/thaw. Variation in state or amount of water contained in the soil causes significant alteration of dielectric properties of the soil which is markedly observable in scatterometer backscattered signal. A threshold-analysis method is developed to derive a set of parameters to be used in evaluating the normalized backscatter measurements through decision trees and anomaly detection modules for determination of freeze/thaw conditions. The model parameters are extracted from two years (2007-2008) backscatter data from ASCAT scatterometer onboard Metop satellite collocated with ECMWF ReAnalysis (ERA-Interim) soil temperature. Backscatter measurements are flagged as indicator of frozen/unfrozen surface, and snowmelt or existing water on the surface. The output product, so-called surface state flag (SSF), compares well with two modeled soil temperature data sets as well as the air temperature measurements from synoptic meteorological stations across the northern hemisphere. The SSF time series are also validated with soil temperature data available at four in situ observation sites in Siberian and Alaska regions showing the overall accuracy of about 80% to 90%. [ABSTRACT FROM AUTHOR]

For integrative management of soil and water in theWuding River basin, Loess plateau, China, where severe soil erosion damages are incurred, the ecohydrological behavior of the region is needed to be explored. In this study we focus on the evolution of soil moisture (SM) in the basin. Since there are only twelve years in-situ SM measurements available at two stations from 1992 to 2004, an eco-hydrological processes-based model (VIP, Vegetation Interface Processes model) is employed to simulate the long-term SM, evapotranspiration (ET ), vegetation cover and production variation from 1956 to 2004, for the mechanical analysis of SM change. In-situ SM observations and a remotely sensed SM dataset retrieved by the Vienna University of Technology are used to validate the model. The results show that the model is able to capture seasonal SM variations. The seasonal pattern, multi-year variation, standard deviation and CV (coefficient of the variation) of SM at the daily, monthly and annual scale are well explained by the climatic and ecological factors such as precipitation, temperature, net radiation, evapotranspiration, and Leaf Area Index (LAI, denoted as LAI ). The annual and inter-annual variability of SM is the lowest comparing with that for other 11-ecohydrological variables. The trend analysis shows that SM is in decreasing tendency at α=0.01 level of significance. Its significance is lower than that of runoff and that of temperature (α =0.001), whereas higher than that of precipitation ( =0.1). The products of these long-term SM data aim to help integrative management of soil and water resources. [ABSTRACT FROM AUTHOR]

This study compares ERS scatterometer top soil moisture observations with simulations of a dual layer conceptual hydrologic model. The comparison is performed for 148 Austrian catchments in the period 1991-2000. On average, about 5 to 7 scatterometer images per month with a mean spatial coverage of about 37% are available. The results indicate that the agreement between the two top soil moisture estimates changes with the season and the weight given to the scatterometer in hydrologic model calibration. The hydrologic model generally simulates larger top soil moisture values than are observed by the scatterometer. The differences tend to be smaller for lower altitudes and the winter season. The average correlation between the two estimates is more than 0.5 in the period from July to October, and about 0.2 in the winter months, depending on the period and calibration setting. Using both ERS scatterometer based soil moisture and runoff for model calibration provides more robust model parameters than using either of these two sources of information. [ABSTRACT FROM AUTHOR]

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 ASCAT 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, the other 11 stations present significant correlation levels. 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 ASCAT and the in-situ soil moisture observations at 30 cm is obtained with a T-value of 14 days. [ABSTRACT FROM AUTHOR]