Upscaling of sparse in situ soil moisture observations by integrating auxiliary information from remote sensing

ABSTRACT

Upscaling of sparse in situ soil moisture (SM) observations is essential for the validation of current and upcoming space-borne SM retrievals, and the successful application of SM observations in hydrological models or data assimilation. In this study, we construct a novel method based on Bayesian data fusion to upscale in situ SM observations to the coarse scale of microwave remote sensing. In the framework of Bayesian theory, the valuable auxiliary information obtained in Moderate Resolution Imaging Spectroradiometer (MODIS) apparent thermal inertia (ATI) is integrated into the upscaling process. The method is validated using SM wireless sensor network data in the Tibetan plateau, which covers an area of approximately 30 × 30 km² with 20 in situ stations. Results confirm that the upscaled SM using the method with randomly selected three stations from the 20 stations is extremely close to the mean of the 20 SMs. The mean root mean square error (RMSE) between the upscaled SM and the mean of the 20 in situ SMs was 0.02 m³ m⁻³, and the max RMSE was less than 0.05 m³ m⁻³. Furthermore, the sensitivity of the upscaling accuracy to the number of in situ observations is discussed. When the number of in situ observations is greater than nine, the increasing accuracy of the Bayesian method is limited by the uncertainty in the ATI of the remote sensing.

Acknowledgements

We thank the anonymous reviewers of this article for providing the constructive comments that have significantly contributed to the improvement of this final version. Furthermore, we thank all of the scientists, engineers and students who participated in the Tibetan Plateau Soil Moisture/Temperature Monitoring Network (SMTMN). This work was funded by the National Natural Science Foundation of China (41671336 and 41531174) and the National High Technology Research and Development Program of China (863 Program) (2012AA12A305). We also greatly thank the BMElab of University of North Carolina at Chapel Hill for sharing the program package

Disclosure statement

No potential conflict of interest was reported by the authors.

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Funding

This work was supported by the National Natural Science Foundation of China [41531174, 41671336]; National High Technology Res arch and Development Program of china (863 Program) [2012AA12A305].