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Data Fusion Techniques for Improving Soil Moisture Deficit Using SMOS Satellite and WRF-NOAH Land Surface Model

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Abstract

Microwave remote sensing and mesoscale weather models have high potential to monitor global hydrological processes. The latest satellite soil moisture dedicated mission SMOS and WRF-NOAH Land Surface Model (WRF-NOAH LSM) provide a flow of coarse resolution soil moisture data, which may be useful data sources for hydrological applications. In this study, four data fusion techniques: Linear Weighted Algorithm (LWA), Multiple Linear Regression (MLR), Kalman Filter (KF) and Artificial Neural Network (ANN) are evaluated for Soil Moisture Deficit (SMD) estimation using the SMOS and WRF-NOAH LSM derived soil moisture. The first method (and most simplest) utilizes a series of simple combinations between SMOS and WRF-NOAH LSM soil moisture products, while the second uses a predictor equation generally formed by dependent variables (Probability Distributed Model based SMD) and independent predictors (SMOS and WRF-NOAH LSM). The third and fourth techniques are based on rigorous calibration and validation and need proper optimisation for the final outputs backboned by strong non-linear statistical analysis. The performances of all the techniques are validated against the probability distributed model based soil moisture deficit as benchmark; estimated using the ground based observed datasets. The observed high Nash Sutcliffe Efficiencies between the fused datasets with Probability Distribution Model clearly demonstrate an improved performance from the individual products. However, the overall analysis indicates a higher capability of ANN and KF for data fusion than the LWA or MLR approach. These techniques serve as one of the first demonstrations that there is hydrological relevant information in the coarse resolution SMOS satellite and WRF-NOAH LSM data, which could be used for hydrological applications.

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Acknowledgments

The authors would like to thank the Commonwealth Scholarship Commission, British Council, United Kingdom and Ministry of Human Resource Development, Government of India for providing the necessary support and funding for this research. The authors would like to acknowledge the British Atmospheric Data Centre and Environment Agency, United Kingdom for providing the ground datasets. The author also acknowledges the Advanced Computing Research Centre at University of Bristol for providing the access to the supercomputer facility (The Blue Crystal) and Linux R support.

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Authors and Affiliations

  1. WEMRC, Department of Civil Engineering, University of Bristol, Bristol, UK

    Prashant K. Srivastava, Dawei Han, Miguel A. Rico-Ramirez, Deleen Al-Shrafany & Tanvir Islam

  2. National Oceanic and Atmospheric Administration (NOAA), College Park, MD, USA

    Tanvir Islam

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  1. Prashant K. Srivastava
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  2. Dawei Han
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  3. Miguel A. Rico-Ramirez
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Correspondence to Prashant K. Srivastava.

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Srivastava, P.K., Han, D., Rico-Ramirez, M.A. et al. Data Fusion Techniques for Improving Soil Moisture Deficit Using SMOS Satellite and WRF-NOAH Land Surface Model. Water Resour Manage 27, 5069–5087 (2013). https://doi.org/10.1007/s11269-013-0452-7

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