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Soil moisture is spatially variable due to complex interactions between geologic, topographic, vegetation, and atmospheric variables. Correct representation of subgrid soil moisture variability is crucial in improving land surface modeling schemes and remote sensing retrievals. In addition to the mean structure, the variance and correlation of soil moisture are affected by the underlying land surface heterogeneity. This often violates the underlying assumption of stationarity/isotropy made by classical geostatistical models. The present study proposes a geostatistical framework to predict and upscale soil moisture in a nonstationary setting using a flexible spatial model whose variance/correlation structure varies with changing land surface characteristics. The proposed framework is applied to model soil moisture distribution using in situ data in the Red River watershed in Southern Manitoba, Canada. It is seen that both the variance and correlation structure exhibits spatial nonstationarity for the given surface heterogeneity driven primarily by vegetation and soil texture. At the beginning of the crop season, soil texture plays a critical role in the drying cycle by decreasing variance and increasing correlation as the soil becomes drier. Once the crops begin to mature, vegetation becomes the dominant driver, promoting spatial correlation and reducing SM variance. We upscale our point scale soil moisture predictions to the airborne extent (approximate to 1.5 km) and find that the upscaled soil moisture agrees well with the observed airborne data with root-mean-square error values ranging from 0.04 to 0.08 (v/v). The proposed framework can be used to predict and upscale soil moisture in heterogeneous environments.

Plain Language Summary Soil moisture (SM) is a critical variable governing the global water and energy cycles. Understanding how SM varies in space is therefore critical. This spatial variation of SM can be typically defined by three statistical quantities: mean (average value), variance (how far the individual SM values are from the average value), and correlation (how individual SM values are related to each other). Variance/correlation of SM are typically assumed to be constant in traditional geostatistics methods. This is a major shortcoming because it has been well established that land surface characteristics such as soil, vegetation, and topography affects the spatial variability of SM. In this study, we propose a framework that accounts for the effect of these characteristics on the variance/correlation of SM. We apply our framework to a watershed in Manitoba, Canada, and find that our framework performs significantly better than the traditional method. We find that soil texture and vegetation affect SM distribution at different stages of crop growth. We aggregate our point scale SM predictions to 1.5-km (airborne) scale and find that our predictions mimic observed SM data at this scale. We conclude that our framework can be used to predict and aggregate SM using surface data.