A spatially consistent downscaling approach for SMOS using an adaptive window
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hdl:2117/128591
Document typeConference report
Defense date2017
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
All rights reserved. This work is protected by the corresponding intellectual and industrial
property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public
communication or transformation of this work are prohibited without permission of the copyright holder
ProjectPRODUCTOS Y SERVICIOS INNOVADORES CON SENSORES DE MICROONDAS, SMOS Y SENTINELS PARA TIERRA (MINECO-ESP2015-67549-C3-1-R)
SEDAL - Statistical Learning for Earth Observation Data Analysis. (EC-H2020-647423)
SEDAL - Statistical Learning for Earth Observation Data Analysis. (EC-H2020-647423)
Abstract
The ESA's Soil Moisture and Ocean Salinity (SMOS, 2009-2017) is the first mission using L-band radiometry to monitor the Earth's global surface soil moisture (SM). After more than 7 years in orbit, many studies have contributed to improving the quality and applicability of SMOS-derived SM maps. In this research, a novel downscaling algorithm is proposed for retrieving high resolution (1 km) SM. This model is an extension of the “universal triangle” technique, and also introduces the concept of adaptive moving window. Its inputs are the low resolution SMOS BEC L3 SM and the brightness temperatures at vertical and horizontal polarizations (SMOS L1C), and the high resolution NDVI and LST from optically-based sensors. The proposed method allows obtaining high resolution SM maps worldwide, with no limitation in extension.
CitationPortal, G. [et al.]. A spatially consistent downscaling approach for SMOS using an adaptive window. A: IEEE International Geoscience and Remote Sensing Symposium. "IGARSS 2017: International Geoscience and Remote Sensing Symposium: Texas, Estats Units: July 23-28, 2017: proceedings book". Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 5022-5025.
ISBN978-1-5090-4951-6
Publisher versionhttps://ieeexplore.ieee.org/document/8127915
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