Improving the fusion of global soil moisture datasets from SMAP, SMOS, ASCAT, and MERRA2 by considering the non-zero error covariance

Improving the fusion of global soil moisture datasets from SMAP, SMOS, ASCAT, and MERRA2 by considering the non-zero error covariance

Surface soil moisture (SSM) estimates from different sources have distinct error characteristics. Multi-source data combination is an efficient way to obtain improved SSM data with reduced uncertainties. Previous data merging studies based on the linear weight averaging scheme rarely considered the...

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Bibliographic Details
Main Authors: Xiaoxiao Min, Yulin Shangguan, Danlu Li, Zhou Shi
Format: Article
Language:English
Published: Elsevier 2022-09-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Surface soil moisture
Multi-source data merging
Quadruple collocation
Error covariance
Online Access:http://www.sciencedirect.com/science/article/pii/S1569843222002047

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http://www.sciencedirect.com/science/article/pii/S1569843222002047

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