Abstract
The prediction of spatial and temporal variation of soil water content brings numerous benefits in the studies of soil. However, it requires a considerable number of covariates to be included in the study, complicating the analysis. Integrated nested Laplace approximations (INLA) with stochastic partial differential equation (SPDE) methodology is a possible approach that allows the inclusion of covariates in an easy way. The current study has been conducted using INLA-SPDE to study soil moisture in the area of the Valencia Anchor Station (VAS), soil moisture validation site for the European Space Agency SMOS (Soil Moisture and Ocean Salinity). The data used were collected in a typical ecosystem of the semiarid Mediterranean conditions, subdivided into physio-hydrological units (SMOS units) which presents a certain degree of internal uniformity with respect to hydrological parameters and capture the spatial and temporal variation of soil moisture at the local fine scale. The paper advances the knowledge of the influence of hydrodynamic properties on VAS soil moisture (texture, porosity/bulk density and soil organic matter and land use). With the goal of understanding the factors that affect the variability of soil moisture in the SMOS pixel (50 km × 50 km), five states of soil moisture are proposed. We observed that the model with all covariates and spatial effect has the lowest DIC value. In addition, the correlation coefficient was close to 1 for the relationship between observed and predicted values. The methodology applied presents the possibility to analyze the significance of different covariates having spatial and temporal effects. This process is substantially faster and more effective than traditional kriging. The findings of this study demonstrate an advancement in that framework, demonstrating that it is faster than previous methodologies, provides significance of individual covariates, is reproducible, and is easy to compare with models.
Highlights
Soil moisture is a key state variable with multiple inter relationships with the functioning of terrestrial ecosystems [1]
Study,special specialattention attentionhas hasbeen beengiven given to tothe thephysical physical properties properties of of the the soil, soil, evaluated evaluatedin inthe thedifferent differentsampling samplingcampaigns, campaigns,associated associatedto tothe thespatial spatialvariability variabilityof ofsoil soil moisture content over the
We have presented here the results of the spatial and temporal analysis of soil moisture data used to calibrate their values through the SMOS campaigns
Summary
Soil moisture is a key state variable with multiple inter relationships with the functioning of terrestrial ecosystems [1]. It is one of the Global Climate Observing System (GCOS), Essential Climate Variables (ECV), as well as one of the European Space Agency. Soil moisture content is associated to a large collection of factors, so predicting its temporal and spatial variations may provide useful information in fields such as ecology, biogeochemical cycles, climate monitoring and water management among others [4,5,6,7].
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