No abstract

Abstract. Soil moisture is one of the fundamental variables in hydrology, meteorology and agriculture. Nevertheless, its spatio-temporal patterns in agriculturally used landscapes that are affected by multiple natural (rainfall, soil, topography etc.) and agronomic (fertilisation, soil management etc.) factors are often not well known. The aim of this study is to determine the dominant factors governing the spatiotemporal patterns of surface soil moisture in a grassland and an arable test site that are located within the Rur catchment in Western Germany. Surface soil moisture (0-6 cm) was measured in an approx. 50×50 m grid during 14 and 17 measurement campaigns

Abstract. Soil moisture is one of the fundamental variables in hydrology, meteorology and agriculture. Nevertheless, its spatio-temporal patterns in agriculturally used landscapes affected by multiple natural (rainfall, soil, topography etc.) and agronomic (fertilisation, soil management etc.) factors are often not well known. The aim of this study is to determine the dominant factors governing the spatio-temporal patterns of surface soil moisture in a grassland and an arable land test site within the Rur catchment in Western Germany. Surface soil moisture (0–6 cm) has been measured in an approx. 50×50 m grid at 14 and 17 dates (May 2007 to November 2008) in both test sites. To analyse spatio-temporal patterns of surface soil moisture, an Empirical Orthogonal Function (EOF) analysis was applied and the results were correlated with parameters derived from topography, soil, vegetation and land management to connect the pattern to related factors and processes. For the grassland test site, the analysis results in one significant spatial structure (first EOF), which explains about 57.5% of the spatial variability connected to soil properties and topography. The weight of the first spatial EOF is stronger on wet days. The highest temporal variability can be found in locations with a high percentage of soil organic carbon (SOC). For the arable land test site, the analysis yields two significant spatial structures, the first EOF, explaining 38.4% of the spatial variability, shows a highly significant correlation to soil properties, namely soil texture. The second EOF, explaining 28.3% of the spatial variability, is connected to differences in land management. The soil moisture in the arable land test site varies more during dry and wet periods on locations with low porosity.

The study aimed to analyze the spa al variability of surface soil moisture at diff erent spa al scales based on fi eld measurements and remote sensing es mates. Mul temporal Envisat satellite Advanced Synthe c Aperture Radar (ASAR) data were used to derive the surface soil moisture u lizing an empirical C-band retrieval algorithm. Eight wide-swath (WS) images with a spa al resolu 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 rela onship of the coeffi cient of varia on of the spa al soil moisture pa erns and the mean soil moisture was analyzed at diff erent spa al scales ranging from the catchment scale to the fi eld scale. Our results show that the coeffi cient of varia on decreases at all scales with increasing soil moisture. The gain of this rela onship decreases with scale, however, indica ng that at a given soil moisture state, the spa al varia on at the large scale of whole catchments is larger than at the fi eld scale. Knowledge of the spa al variability of the surface soil moisture is important to be er understand energy exchange processes and water fl uxes at the land surface as well as their scaling proper es.Abbrevia ons: ASAR, Advanced Synthe c Aperture Radar; SAR, synthe c aperture radar; WS, wide swath.Soil moisture and its distribu 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.Th e sensitivity of the radar backscattering coeffi cient (σ 0 ) to soil moisture at low microwave frequencies is well described in the literature (Boisvert et al., 1997;Loew et al., 20...

No abstract