SGD-SM: Global Daily Soil Moisture from AMSR2 Satellite (2013-2019)
by Qiang Zhang / Wuhan University
Available on 1 platform
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Description
A seamless global daily soil moisture dataset generated from AMSR2 satellite data covers the period from January 1, 2013, to December 31, 2019. It contains 2553 global NetCDF4 files and was created by Qiang Zhang of Wuhan University. The dataset includes both original and reconstructed soil moisture data, validated through in-situ, time-series, and simulated missing regions methods.
Use Cases
Validating hydrological models based on global daily soil moisture estimates.
Analyzing long-term soil moisture trends from 2013 to 2019.
Filling gaps in satellite soil moisture records using the described reconstruction method.
Studying land-atmosphere interactions using seamless global daily data.
Strengths
Provides 2553 daily global files, offering consistent daily coverage from 2013 to 2019.
Includes both original and reconstructed data layers to address missing observations.
Dataset size is approximately 20GB uncompressed, indicating substantial spatial-temporal detail.
Limitations
Column-level documentation is absent; field semantics must be inferred after download.
Last update date is unknown; freshness unverified.
Provenance
Source
AMSR2 satellite data processed by Wuhan University.
Collection Method
Generated through a proposed model to create seamless daily products.
Time Range
2013-01-01 to 2019-12-31
Geography
Global
Data is in NetCDF4 format; users need to install the netCDF4 and numpy Python toolkits before reading.