Multiscale Data Fusion for Surface Soil Moisture Estimation: A Spatial Hierarchical Approach

Abstract

Surface soil moisture (SSM) has been identified as a key climate variable governing hydrologic and atmospheric processes across multiple spatial scales at local, regional, and global levels. The global burgeoning of SSM datasets in the past decade holds a significant potential in improving our understanding of multiscale SSM dynamics. The primary issues that hinder the fusion of SSM data from disparate instruments are (1) different spatial resolutions of the data instruments, (2) inherent spatial variability in SSM caused due to atmospheric and land surface controls, and (3) measurement errors caused due to imperfect retrievals of instruments. We present a data fusion scheme which takes all the above three factors into account using a Bayesian spatial hierarchical model (SHM), combining a geostatistical approach with a hierarchical model. The applicability of the fusion scheme is demonstrated by fusing point, airborne, and satellite data for a watershed exhibiting high spatial variability in Manitoba, Canada. We demonstrate that the proposed data fusion scheme is adept at assimilating and predicting SSM distribution across all three scales while accounting for potential measurement errors caused due to imperfect retrievals. Further validation of the algorithm is required in different hydroclimates and surface heterogeneity as well as for other data platforms for wider applicability.Plain Language Summary Surface soil moisture (SSM) is an essential climate-variable governing land-atmosphere interactions. SSM is spatially variable in the presence of changing atmospheric factors such as rainfall and land-surface characteristics such as soil, vegetation, and topography. SSM is measured using various instruments from point to satellite resolutions (25-40 km) and each instrument is accompanied by its own set of errors. Due to the importance of SSM, it would be beneficial to combine the SSM measurements from all available instruments in a region while accounting for the spatially varying nature of SSM and the measurement errors caused due to instruments. We present a novel framework to achieve the abovementioned objective and successfully apply it to a watershed in Manitoba, Canada to combine data from point, airborne, and satellite instruments. We demonstrate that the proposed framework can be used to optimally combine and predict SSM across different spatial resolutions in the presence of uncertainty.

Multi-Source Soil Moisture Data Fusion Based on Spherical Cap Harmonic Analysis and Helmert Variance Component Estimation in the Western U.S.

“…Compared with SM products from a single data source, fused SM products are more reliable and provide a more accurate picture of the spatial and temporal variability of surface SM [24]. In past studies, the spatial resolution of 0.1 • has become one of the most common high resolutions in hydrology and agriculture [34]. Various fusion algorithms have been employed to integrate data sources with varying resolutions, ranging from straightforward and analytical methods to more intricate approaches [35][36][37].…”

A Novel Fusion Method for Generating Surface Soil Moisture Data With High Accuracy, High Spatial Resolution, and High Spatio‐Temporal Continuity

“…3H SSM data is of great significance for soil moisture‐based environmental applications. Nevertheless, utilizing downscaling techniques to obtain 3H SSM is still a big challenge (Ford & Quiring, 2019; Kathuria et al., 2019; Peng et al., 2017; Zhang & Chen, 2016).…”