The impact of vertical measurement depth on the information content of soil moisture times series data

Abstract

Using a decade of ground-based soil moisture observations acquired from the United States Department of Agriculture's Soil Climate Analysis Network (SCAN), we calculate the mutual information (MI) content between multiple soil moisture variables and near-future vegetation condition to examine the existence of emergent drought information in vertically integrated (surface to 60 cm) soil moisture observations (θ 0-60 [cm] ) not present in either superficial soil moisture observations (θ 5 [cm] ) or a simple low-pass transformation of θ 5 . Results suggest that while θ 0-60 is indeed more valuable than θ 5 for predicting near-future vegetation anomalies, the enhanced information content in θ 0-60 soil moisture can be effectively duplicated by the low-pass transformation of θ 5 . This implies that, for drought monitoring applications, the shallow vertical penetration depth of microwave-based θ 5 retrievals does not represent as large a practical limitation as commonly perceived.

Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values

“…Second, crosscorrelation is limited to evaluating linear lagged dependence and, in incorporating nonlinear lagged dependence, can make the test more robust. Equivalent methods exist (e.g., mutual information content; Qiu et al, 2014) but they are much more computationally demanding when the goal is simply to check for the existence of lag-lead relation.…”

“…In such a case, we infer that the translation to depth-averaged values would result in (de)coupled values that are close, but not identical, to the values obtained when only comparing two discrete depths. As an illustration, we calculated the depthaverage values using all the available measurements at each site (i.e., 5, 10, 20, and 40 cm depth) following the formula from Qiu et al (2014). Figure 9 (left) reveals highly similar dynamics for both discrete and depth-average values.…”

Development of an Earth Observation Cloud Platform in Support to Water Resources Monitoring

“…Soil moisture is an important component of the water cycle and the satellite-based products derived from active and passive microwave are increasingly being used for a wide range of applications (Dorigo and de Jeu 2016). For example, satellite-based soil moisture may be used for estimation of near-future vegetation health (Qiu et al 2014), improved calculation of crop water requirement (McNelly et al 2015) and operational drought warnings (Enenkel et al 2016). EODC currently leads the second phase of the ESA Climate Change Initiative Soil Moisture project, providing the operational framework for merging more than a dozen of satellite data sets into consistent long-term soil moisture data records (Liu et al 2012(Liu et al , 2011Wagner et al 2012).…”

Using lagged dependence to identify (de)coupled surface andsubsurface soil moisture values

“…Equivalent methods exist (e.g. mutual information content (Qiu et al, 2014)) but they are much more computationally demanding when the goal is simply to check for the existence of lag-lead 10 relation.…”