AL-YAARI, Amen; WIGNERON, Jean-Pierre; KERR, Yann H.; RODRIGUEZ‐FERNANDEZ, Nemesio; O'NEILL, Peggy; JACKSON, Thomas; DE LANNOY, Gabrielle; AHMAD, Al Bitar; MIALON, Arnaud; RICHAUME, Philippe; YUEH, Simon

doi:10.1109/IGARSS.2016.7729417

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AL-YAARI, Amen
Interactions Sol Plante Atmosphère [UMR ISPA]

WIGNERON, Jean-Pierre
Interactions Sol Plante Atmosphère [UMR ISPA]

KERR, Yann H.
Centre d'études spatiales de la biosphère [CESBIO]

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IEEE International Geoscience and Remote Sensing Symposium Proceedings, IEEE International Geoscience and Remote Sensing Symposium Proceedings, IGARSS 2016 Advancing the understanding of our living planet, 2016-07-10, Pékin. 2016

IEEE

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In this study, we used a multilinear regression approach to retrieve surface soil moisture from NASA's Soil Moisture Active Passive (SMAP) satellite data to create a global dataset of surface soil moisture which is consistent with ESA's Soil Moisture and Ocean Salinity (SMOS) satellite retrieved surface soil moisture. This was achieved by calibrating coefficients of the regression model using SMOS soil moisture and horizontal and vertical brightness temperatures (TB), over the 2013 — 2014 period. Next, this model was applied to recent SMAP TB data from 31/03/2015–08/09/2015. The retrieved surface soil moisture from SMAP (referred here to as SMAP-reg) was compared to the operational SMAP L3 surface soil moisture retrieved using the single channel algorithm. Both exhibit comparable temporal dynamics with a good agreement of correlation (correlation coefficient R mostly > 0.8) between the SMAP-reg and the operational SMAP L3 surface soil moisture products.< Réduire

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soil moisture

SMOS

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SMAP

statistical regression

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First application of regression analysis to retrieve soil moisture from SMAP brightness temperature observations consistent with SMOS

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