Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Abstract

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

Development and validation of an Approximate Redistributive Balance model to estimate the distribution of water resources using the WEAP: The lower Hron river basin, Slovakia

“…Thanks to the calculation method used, the variables Rtot and EA maintain the value of Si in the range (0, Smax) without using the min/max functions in the equation of Si. By observing the behavior of the variables during modeling, they show an asymptotic bound to the range (0, Smax) while the Si values balance within this range, which provides a more realistic approach to the data range (de Figueiredo et al, 2021).…”

Uniplanar multirod TDR probe for soil water content measurement over a larger spatial scale

“…Although automated systems with multiple TDR probes can monitor the changes in soil water over time at different locations, they offer information at only a series of point locations rather than continuously at the field scale. The estimates with hydrologic models (Feddes et al, 1993; de Figueiredo et al, 2021; Santi et al, 2013) are generally poor because of the large spatial and temporal variations in soil water due to the heterogeneity of soil properties, vegetation and precipitation (Vereecken et al, 2008).…”

Spatial and Temporal Evolution Characteristics of Water Conservation in the Three-Rivers Headwater Region and the Driving Factors over the Past 30 Years

“…By contrast, the assessment of large-scale WC is typically accomplished through remote sensing inversion and ecological model simulation based on water balance [19]. Remote sensing products provide information on WC at larger time and spatial scales but may not accurately capture local parameters such as soil water storage capacity [20]. The Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, based on the water balance, is widely used for long-term WC simulation due to its relatively straightforward data preparation and its ability to capture the physical mechanisms of soil-vegetation-atmosphere interactions in terrestrial ecosystems [21].…”