Global soil moisture data fusion by Triple Collocation Analysis from 2011 to 2018

Area of Science:

• Earth Science
• Environmental Science
• Remote Sensing

Background:

• Surface Soil Moisture (SSM) is crucial for water resource management, hydrology, and climate analysis.
• Satellite-based SSM retrieval faces limitations in temporal and spatial sampling due to orbital and sensor constraints.

Purpose of the Study:

• To develop a high-resolution, globally comprehensive daily soil moisture dataset.
• To overcome limitations of existing satellite-based SSM products.

Main Methods:

• Utilized Triple Collocation Analysis (TCA) and Linear Weight Fusion (LWF) methods.
• Generated a Global Daily-scale Soil Moisture Fusion Dataset (GDSMFD) at 25 km resolution (2011-2018).
• Evaluated GDSMFD against in-situ measurements from ten ground networks and compared with pre-fusion products.

Main Results:

• GDSMFD demonstrated high consistency with in-situ soil moisture measurements, achieving a minimum root mean square error of 0.036 cm³/cm³.
• The dataset exhibits good global coverage, with a mean Global Coverage Fraction (GCF) of 0.672 and a maximum GCF of 0.837.
• The fusion dataset outperformed pre-fusion SSM products in accuracy and coverage.

Conclusions:

• The GDSMFD provides a valuable resource for applications requiring accurate and extensive daily surface soil moisture data.
• The dataset's performance in accuracy and global coverage makes it suitable for global climate change, drought, and hydrological monitoring.
• This fusion dataset addresses the need for improved SSM information in critical environmental applications.