Downscaled ESA CCI Soil Moisture: a new dataset at 1 km for the period 2008-2020

Published: 3 March 2023| Version 1 | DOI: 10.17632/xzzt3gzdjy.1
Contributor:
Luca Zappa

Description

The European Space Agency (ESA) Climate Change Initiative (CCI) provides long-term surface soil moisture (SM) records with daily temporal resolution. However, the coarse spatial resolution of approximately 25 km limits their use in many applications, such as agricultural water management, drought monitoring, and rainfall-runoff response. To address this constraint, we downscaled the CCI SM product to 0.01° (~ 1 km) using machine learning and a set of static and dynamic variables affecting the spatial organization of SM. In particular, datasets describing the vegetation status throughout time, as well as land cover class, soil and topographic attributes were fed into a Random Forest model. The dataset currently consists of two study areas of the WATERLINE project, located in Poland and Greece.

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Institutions

Technische Universitat Wien Forschungsbereich Fernerkundung

Categories

Machine Learning, Soil Moisture

Funding

CHIST-ERA

CHIST-ERA-19-CES-006

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