Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals
Yi Y. Liu,Yi Y. Liu,Yi Y. Liu,Robert Parinussa,Wouter Dorigo,R.A.M. de Jeu,Wolfgang Wagner,A. I. J. M. van Dijk,Matthew F. McCabe,Jason P. Evans +9 more
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In this article, the retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates are combined to produce an improved soil moisture product. But the results of the satellite-based passive and active microwave sensors have the potential to offer improved estimates of surface soil moisture at global scale.Abstract:
. Combining information derived from satellite-based 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 (m3 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 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.read more
Citations
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Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products
TL;DR: In this article, a large-scale hydrological model PCRaster GLOBAL Water Balance (PCR-GLOBWB) was calibrated using satellite-based products of evapotranspiration and soil moisture for the Moroccan Oum er Rbia River basin.
Journal ArticleDOI
Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products
Hyunglok Kim,Robert Parinussa,Alexandra G. Konings,Wolfgang Wagner,Michael H. Cosh,Venkat Lakshmi,Muhammad Zohaib,Minha Choi +7 more
TL;DR: In this paper, the authors compared global-scale error patterns and combined the Soil Moisture Active Passive (SMAP), Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) products using a triple collocation (TC) analysis and the maximized Pearson correlation coefficient (R) method from April 2015 to December 2016.
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Impact of the soil hydrology scheme on simulated soil moisture memory
Stefan Hagemann,Tobias Stacke +1 more
TL;DR: In this paper, the authors investigated how different setups of a soil hydrology scheme affect soil moisture memory simulated by the global climate model of the Max Planck Institute for Meteorology, ECHAM6/JSBACH.
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A drought event composite analysis using satellite remote-sensing based soil moisture
Nadine Nicolai-Shaw,Jakob Zscheischler,Martin Hirschi,Lukas Gudmundsson,Sonia I. Seneviratne +4 more
TL;DR: In this article, satellite derived soil moisture observations from the Climate Change Initiative of the European Space Agency were used to quantify the relation between soil moisture drought and temperature, precipitation, evapotranspiration and vegetation during the peak of the growing season.
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Spatial Downscaling of Satellite Soil Moisture Data Using a Vegetation Temperature Condition Index
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