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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Downscaling satellite soil moisture using geomorphometry and machine learning.
Mario Guevara,Rodrigo Vargas +1 more
TL;DR: This approach relies on geomorphometry derived terrain parameters and machine learning models to improve the statistical accuracy and the spatial resolution of satellite soil moisture information across the conterminous United States on an annual basis (1991–2016).
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The carbon cycle in Mexico: past, present and future of C stocks and fluxes
Guillermo N. Murray-Tortarolo,Pierre Friedlingstein,Stephen Sitch,Víctor J. Jaramillo,Fabiola Murguia-Flores,Alessandro Anav,Yi Y. Liu,Almut Arneth,A. Arvanitis,Anna B. Harper,Atul K. Jain,Etsushi Kato,Charles D. Koven,Benjamin Poulter,Benjamin D. Stocker,Andy Wiltshire,Sönke Zaehle,Ning Zeng +17 more
TL;DR: In this paper, the authors modeled the carbon cycle in Mexico with a process-based approach and found that the country was a C sink over the period 1990-2009 (+31.5 TgC) and that C accumulation over the last century amounted to 1210TgC.
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How Oceanic Oscillation Drives Soil Moisture Variations over Mainland Australia: An Analysis of 32 Years of Satellite Observations*
TL;DR: In this paper, a global 32-yr dataset of remotely sensed surface soil moisture (SSM) was used to examine hydrological variations in mainland Australia for the period 1978-2010.
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An observational study of the variability of East African rainfall with respect to sea surface temperature and soil moisture.
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A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002-2019).
Panpan Yao,Panpan Yao,Hui Lu,Jiancheng Shi,Tianjie Zhao,Kun Yang,Michael H. Cosh,Daniel J. Short Gianotti,Dara Entekhabi +8 more
TL;DR: In this article, the authors transferred the merits of SMAP to AMSR-E/2, and developed a global daily SSM dataset (named as NNsm) with stable and consistent quality at a 36'km resolution (2002-2019).
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