Journal article

A comparison between remotely-sensed and modelled surface soil moisture (and frozen status) at high latitudes

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 hydrological 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 hydrological 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.

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