How to integrate ground-truth and satellite image to estimate surface soil moisture at the field and watershed scales?

Abstract

Soil moisture is a key environmental variable for developing a coupled hydrological and biogeochemical modeling approach. It is recognized that a relationship does exist between water stress and emission of volatile organic compounds (VOCs) in forested areas, which may have negative effect on human health and ecosystems. Therefore it is necessary to achieve a better understanding of the land phase of the hydrological cycle, namely soil moisture estimation, which modulates surface energy balance and consequently vegetation cover patterns. This work focuses on a new methodological approach to evaluate the spatial variability of surface soil moisture at the field scale using the Bayesian kriging model jointly with TDR measurements and Landsat 8-TM remotely sensed image. The analysis looked for quantifying different deterministic sources of variability, measurement errors and also components not well understood of variability. In particular, the spatial distribution of in situ measurements and digital image data in a scene provided by remote sensing technology is addressed through a geostatistical framework. This technique is explored as alternative to the regression techniques currently used for modeling soil moisture mapping. Tests conducted on an extensively sampled pasture field showed significant improvement, which suggests that the methodological approach could be applied at the watershed scale for validating remotely sensed datasets.