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.
Highlights
Climate models provide support to weather and climate forecasting and projections of likely climate change scenarios
We provide a comparison between the results obtained using the ground-truth, the IUS index derived by Landsat satellite image and the proposed geostatistical methodology
We initially develop the semivariograms for depicting the correlation structure of the ground-truth volumetric soil moisture and the IUS index
Summary
Climate models provide support to weather and climate forecasting and projections of likely climate change scenarios These models consider numerically the behavior of components of the terrestrial climate system, such as the atmosphere, oceans, cryosphere (ice and snow areas), vegetation, biogeochemical cycles and their interactions. Given the importance of soil moisture applications both in surface and groundwater hydrology and in modeling of weather and climate, water and geochemical cycling, there is an evident need for studies to enable a better understanding of this variable on a field scale and regional scale. It is acknowledged that an important issue still remains to be addressed: how should hydrological and hydraulic variability be related to the spatial distribution of brightness values in the images provided by remote sensing technology. The spatial distribution of digital image data in a scene provided by remote sensing technology is addressed through a geostatistical analysis procedure
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