Impact of Rescaling Approaches in Simple Fusion of Soil Moisture Products

In this study, the impact of various rescaling approaches in the framework of data fusion is explored. Four different soil moisture products (Advanced Scatterometer; Advanced Microwave Scanning Radiometer for EOS, AMSR-E; Antecedent Precipitation Index; and Global Land Data Assimilation System-NOAH) are fused. The systematic differences between products are removed before the fusion utilizing various rescaling approaches focusing on different methods (regression, variance/cumulative distribution function (CDF) matching, multivariate adaptive regression splines, and support vector machines based), stationarity assumptions (constant or time-varying rescaling coefficients), and time-frequency techniques (periodic or nonperiodic high- and low-frequency components). Given that statistical descriptions (e.g., standard deviation and correlation coefficient) of reference data sets are utilized in rescaling approaches, the precision of the selected reference data set also impacts the final fused product precision. Experiments are validated over 542 soil moisture monitoring sites selected from the International Soil Moisture Network data sets between 2007 and 2011. Overall, results highlight the importance of reference data set selection-particularly that a more precise reference product yields a higher precision fused soil moisture product. This conclusion is sensitive neither to the number of fused products nor the rescaling procedure. Among rescaling approaches, the precision of fused products is most affected by the choice of rescaling stationary assumption and time-frequency decomposition technique. Variations in rescaling methods have only a small impact on the precision of pair fused products. In contrast, utilizing a time-varying stationary assumption and nonperiodic decomposition technique produces correlation improvements of 0.07 [-] and 0.02 [-], respectively, versus the other widely implemented rescaling approaches

The Effect of Three Different Data Fusion Approaches on the Quality of Soil Moisture Retrievals from Multiple Passive Microwave Sensors

Long-term climate records of soil moisture are of increased importance to climate researchers. In this study, we aim to evaluate the quality of three different fusion approaches that combine soil moisture retrieval from multiple satellite sensors. The arrival of L-band missions has led to an increased focus on the integration of L-band-based soil moisture retrievals in climate records, emphasizing the need to improve our understanding based on its added value within a multi-sensor framework. The three evaluated approaches were developed on 10-year passive microwave data (2003–2013) from two different satellite sensors, i.e., SMOS (2010–2013) and AMSR-E (2003–2011), and are based on a neural network (NN), regressions (REG), and the Land Parameter Retrieval Model (LPRM). The ability of the different approaches to best match AMSR-E and SMOS in their overlapping period was tested using an inter-comparison exercise between the SMOS and AMSR-E datasets, while the skill of the individual soil moisture products, based on anomalies, was evaluated using two verification techniques; first, a data assimilation technique that links precipitation information to the quality of soil moisture (expressed as the Rvalue), and secondly the triple collocation analysis (TCA). ASCAT soil moisture was included in the skill evaluation, representing the active microwave-based counterpart of soil moisture retrievals. Besides a semi-global analysis, explicit focus was placed on two regions that have strong land–atmosphere coupling, the Sahel (SA) and the central Great Plains (CGP) of North America. The NN approach gives the highest correlation coefficient between SMOS and AMSR-E, closely followed by LPRM and REG, while the absolute error is approximately the same for all three approaches. The Rvalue and TCA show the strength of using different satellite sources and the impact of different merging approaches on the skill to correctly capture soil moisture anomalies. The highest performance is found for AMSR-E over sparse vegetation, for SMOS over moderate vegetation, and for ASCAT over dense vegetation cover. While the two SMOS datasets (L3 and LPRM) show a similar performance, the three AMSR-E datasets do not. The good performance for AMSR-E over spare vegetation is mainly perceived for AMSR-E LPRM, benefiting from the physically based model, while AMSR-E NN shows improved skill in densely vegetated areas, making optimal use of the SMOS L3 training dataset. AMSR-E REG has a reasonable performance over sparsely vegetated areas; however, it quickly loses skill with increasing vegetation density. The findings over the SA and CGP mainly reflect results that are found in earlier sections. This confirms that historical soil moisture datasets based on a combination of these sources are a valuable source of information for climate research