Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals

Abstract

Abstract. Combining information derived from satellitebased passive and active microwave sensors has the potential to offer improved estimates of surface soil moisture at global scale. We develop and evaluate a methodology that takes advantage of the retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates to produce an improved soil moisture product. First, volumetric soil water content (m 3 m −3 ) from AMSR-E and degree of saturation (%) from ASCAT are rescaled against a reference land surface model data set using a cumulative distribution function matching approach. While this imposes any bias of the reference on the rescaled satellite products, it adjusts them to the same range and preserves the dynamics of original satellite-based products. Comparison with in situ measurements demonstrates that where the correlation coefficient between rescaled AMSR-E and ASCAT is greater than 0.65 ("transitional regions"), merging the different satellite products increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT, respectively, are used for the merged product. Therefore the merged product carries the advantages of better spatial coverage overall and increased number of observations, particularly for the transitional regions. The combination method developed has the potential to be applied Correspondence to: Y. Y. Liu (yi.y.liu@csiro.au) to existing microwave satellites as well as to new missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

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Anomalous carbon uptake in Australia as seen by GOSAT

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A global water cycle reanalysis (2003–2012) merging satellite gravimetry and altimetry observations with a hydrological multi-model ensemble

“…Data assimilation is used routinely to overcome data and model limitations in atmospheric reconstructions or "reanalysis". In hydrological applications, there has been an (over-)emphasis on parameter calibration ( Van Dijk, 2011), with data assimilation approaches largely limited to flood forecasting. New applications are being developed, however (Liu et al, 2012a), including promising developments towards large-scale water balance reanalyses, alternatively referred to as monitoring, assessment or estimation (van Dijk and Renzullo, 2011).…”

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