Abstract
Several studies currently strive to improve the spatial resolution of coarse scale high temporal resolution global soil moisture products of SMOS, SMAP, and ASCAT. Soil texture heterogeneity is known to be one of the main sources of soil moisture spatial variability. With the recent development of high resolution maps of basic soil properties such as soil texture and bulk density, relevant information to estimate soil moisture variability within a satellite product grid cell is available. We use this information for the prediction of the sub-grid soil moisture variability for each SMOS, SMAP, and ASCAT grid cell. The approach is based on a method that predicts the soil moisture standard deviation as a function of the mean soil moisture based on soil texture information. It is a closed-form expression using stochastic analysis of 1D unsaturated gravitational flow in an infinitely long vertical profile based on the Mualem-van Genuchten model and first-order Taylor expansions. We provide a look-up table that indicates the soil moisture standard deviation for any given soil moisture mean, available at https://doi.org/10.1594/PANGAEA.878889. The resulting data set helps identify adequate regions to validate coarse scale soil moisture products by providing a measure of representativeness of small-scale measurements for the coarse grid cell. Moreover, it contains important information for downscaling coarse soil moisture observations of the SMOS, SMAP, and ASCAT missions. In this study, we present a simple application of the estimated sub-grid soil moisture heterogeneity scaling down SMAP soil moisture to 1 km resolution. Validation results in the TERENO and REMEDHUS soil moisture monitoring networks in Germany and Spain, respectively, indicate a similar or slightly improved accuracy for downscaled and original SMAP soil moisture in the time domain for the year 2016, but with a much higher spatial resolution.
Highlights
Soil moisture is an important driver for the control of weather and climate feedbacks [1]
In addition to a fractal scaling rule, they found that soil moisture standard deviation versus mean moisture in this humid climate exhibited a convex-upward relationship, i.e., that the standard deviation increases until mean soil moisture reaches around 0.2 m3 m−3 and decreases beyond that
This field capacity (FC) map is used as a proxy for soil moisture downscaling
Summary
Soil moisture is an important driver for the control of weather and climate feedbacks [1]. In the last 40 years, a large number of studies have attempted to understand the spatial and temporal variability of soil moisture from the local to the global scale [12,19,20]. Since the description of the soil moisture variance as a function of the observation scale by a power law decay by Rodriguez-Iturbe et al [21], different methods have been applied [22,23], including statistical [24,25,26] and geostatistical approaches [27,28,29,30], temporal stability analysis [31,32,33], and wavelet techniques [34,35,36,37,38]. Vereecken et al [45] explained the general shape of the soil moisture standard deviation versus mean moisture as a function of hydraulic parameter variation
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