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

Qiuxia Xie; Li Jia; Massimo Menenti; Guangcheng Hu

doi:10.1038/s41597-022-01772-x

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

Sci Data. 2022 Nov 11;9(1):687. doi: 10.1038/s41597-022-01772-x.

Authors

Qiuxia Xie^{1

2}, Li Jia³, Massimo Menenti^{2

4}, Guangcheng Hu²

Affiliations

¹ School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China.
² State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China.
³ State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China. jiali@aircas.ac.cn.
⁴ Delft University of Technology, Delft, The Netherlands.

Abstract

Surface Soil Moisture (SSM) information is needed for agricultural water resource management, hydrology and climate analysis applications. Temporal and spatial sampling by the space-borne instruments designed to retrieve SSM is, however, limited by the orbit and sensors of the satellites. We produced a Global Daily-scale Soil Moisture Fusion Dataset (GDSMFD) with 25 km spatial resolution (2011~2018) by applying the Triple Collocation Analysis (TCA) and Linear Weight Fusion (LWF) methods. Using five metrics, the GDSMFD was evaluated against in-situ soil moisture measurements from ten ground observation networks and compared with the prefusion SSM products. Results indicated that the GDSMFD was consistent with in-situ soil moisture measurements, the minimum of root mean square error values of GDSMFD was only 0.036 cm³/cm³. Moreover, the GDSMFD had a good global coverage with mean Global Coverage Fraction (GCF) of 0.672 and the maximum GCF of 0.837. GDSMFD performed well in accuracy and global coverage fraction, making it valuable in applications to the global climate change monitoring, drought monitoring and hydrological monitoring.