Variability of Surface Soil Moisture Observed from Multitemporal C-Band Synthetic Aperture Radar and Field Data

The study aimed to analyze the spaƟ al variability of surface soil moisture at diff erent spaƟ al scales based on fi eld measurements and remote sensing esƟ mates. MulƟ temporal Envisat satellite Advanced SyntheƟ c Aperture Radar (ASAR) data were used to derive the surface soil moisture uƟ lizing an empirical C-band retrieval algorithm. Eight wide-swath (WS) images with a spaƟ al resoluƟ on of 150 m acquired between February and October 2008 were used to determine the surface soil moisture contents. The accuracy of the surface soil moisture retrievals was evaluated by comparison with in situ measurements. This comparison yielded a root mean square error of 5% (v/v). Based on our in situ measurements as well as remote sensing results, the relaƟ onship of the coeffi cient of variaƟ on of the spaƟ al soil moisture paƩ erns and the mean soil moisture was analyzed at diff erent spaƟ al scales ranging from the catchment scale to the fi eld scale. Our results show that the coeffi cient of variaƟ on decreases at all scales with increasing soil moisture. The gain of this relaƟ onship decreases with scale, however, indicaƟ ng that at a given soil moisture state, the spaƟ al variaƟ on at the large scale of whole catchments is larger than at the fi eld scale. Knowledge of the spaƟ al variability of the surface soil moisture is important to beƩ er understand energy exchange processes and water fl uxes at the land surface as well as their scaling properƟ es. AbbreviaƟ ons: ASAR, Advanced SyntheƟ c Aperture Radar; SAR, syntheƟ c aperture radar; WS, wide swath. Soil moisture and its distribu�u on in space and time plays a critical role in the surface energy balance at the soil–atmosphere interface; it is a key variable infl uencing the partitioning of solar energy into latent and sensible heat fl ux as well as the partitioning of precipitation into runoff and percolation. In situ measurements of soil moisture are time and cost intensive. Due to their large spatial variability, estimation of spatial patterns of soil moisture from fi eld measurements is rather diffi cult and not feasible for large-scale analyses. Although hydrologic models have shown their capability to derive spatial soil moisture patterns, their application is a challenging task, requiring a multitude of input data (such as soil properties, i.e., hydraulic characteristics and permeability, along with meteorologic and climatologic data). Neither the full spatial variability of these environmental parameters nor the full details of the processes are typically known, thus modeled spatial patterns tend to reduce spatial variability. Th erefore, as well as due to the need for independent validation, direct and repeatable soil moisture measurements covering large spatial scales obtained from remote sensing instruments is becoming increasingly necessary and now, with the advent of new sensor generations, feasible.