Global Soil Moisture Patterns Observed by Space Borne Microwave Radiometers and Scatterometers

R. A. M. de Jeu 0 1 2 3 W. Wagner 0 1 2 3 T. R. H. Holmes 0 1 2 3 A. J. Dolman 0 1 2 3 N. C. van de Giesen 0 1 2 3 J. Friesen 0 1 2 3 0 W. Wagner Institute of Photogrammetry and Remote Sensing, Vienna University of Technology , Vienna, Austria 1 R. A. M. de Jeu (&) Vrije Universiteit Amsterdam , FALW, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands 2 R. A. M. de Jeu T. R. H. Holmes A. J. Dolman Department of Hydrology and Geo-Environmental Sciences, Faculty of Earth- and Life Sciences, Vrije Universiteit Amsterdam , Amsterdam, The Netherlands 3 N. C. van de Giesen J. Friesen Faculty of Civil Engineering and Geosciences, Water Resources Section, Technical University Delft , Delft, The Netherlands Within the scope of the upcoming launch of a new water related satellite mission (SMOS) a global evaluation study was performed on two available global soil moisture products. ERS scatterometer surface wetness data was compared to AMSR-E soil moisture data. This study pointed out a strong similarity between both products in sparse to moderate vegetated regions with an average correlation coefficient of 0.83. Low correlations were found in densely vegetated areas and deserts. The low values in the vegetated regions can be explained by the limited soil moisture retrieval capabilities over dense vegetation covers. Soil emission is attenuated by the canopy and tends to saturate the microwave signal with increasing vegetation density, resulting in a decreased sensor sensitivity to soil moisture variations. It is expected that the new low frequency satellite mission (SMOS) will obtain soil moisture products with a higher quality in these regions. The low correlations in the desert regions are likely due to volume scattering or to the dielectric dynamics within the soil. The volume scattering in dry soils causes a higher backscatter under very dry conditions than under conditions when the sub-surface soil layers are somewhat wet. In addition, at low moisture levels the dielectric constant has a reduced sensitivity in response to changes in the soil moisture content. At a global scale the spatial correspondence of both products is high and both products clearly distinguish similar regions with high seasonal and inter annual variations. Based on the global analyses we concluded that the quality of both products was comparable and in the sparse to 1 Introduction moderate vegetated regions both products may be beneficial for large scale validation of SMOS soil moisture. Some limitations of the studied products are different, pointing to significant potential for combining both products into one superior soil moisture data set. Global Soil moisture Soil moisture is an important state variable in land surface hydrology and has a dominant influence on physical processes. It is a variable that has always been required in many disciplinary and cross-cutting scientific and operational applications (e.g. ecology, biogeochemical cycles, climate monitoring, flood forecasting, etc.) (Jackson et al. 1999). Unfortunately, accurate estimates of surface soil moisture are often difficult to make, especially at larger spatial scale. The main reason is that it is a very difficult variable to measure, not at a point in time, but on a consistent and spatially comprehensive basis (Leese et al. 2001). Satellite remote sensing can be a powerful tool in fulfilling those needs because it can monitor environmental processes in both spatial and temporal terms. Since the 1960s, satellites have provided data for water resources management. Most importantly, visible and infrared imaging sensors have been used for observing land surface parameters such as snow cover, surface water areas, land use and surface temperature (Schmugge 1985). A major drawback of these instruments is their dependence on atmospheric conditions. The occurrence of clouds, water vapor and aerosols can easily disturb the signals, resulting in limited land surface information. Also, with the exception of thermal infrared sensors, these techniques depend on the sun as a source of illumination of the land surface and cannot provide data during night time and low sun elevation. On the other hand, remote sensing instruments working in the microwave range of the electromagnetic spectrum can be operated day and night and are less affected by atmospheric conditions. This has prompted much research and development in the field of microwave remote sensing. In microwave remote sensing one distinguishes active and passive techniques. While passive microwave radiometers record naturally emitted radiation, active microwave sensors transmit electromagnetic waves and record the backscattered radiation. The latter are often referred to as radar which stands for radio detection and ranging. Because scattering and emission phenomena are closely related (Schanda 1986), the development of passive and active microwave remote sensing techniques went hand in hand. The first space-borne earth-observation radiometer was launched in 1968 on the Russian satellite Comos 243, followed ten years later by the first spaceborne radar satellite Seasat that was built and operated by NASA (Ulaby et al. 1981). Since then numerous satellites carrying microwave radiometers and/or radar instruments have been launched. The potential of microwave sensors for measuring soil moisture has been recognized early (Eagleman and Ulaby 1975). The theoretical basis for measuring soil moisture at microwave frequencies lies in the large contrast between the dielectric properties of liquid water and dry soil material. The large dielectric constant of water is the result of the water molecules alignment of its permanent electric dipole in response to an applied electromagnetic field. Therefore, when water is added to the soil matrix, the effective dielectric constant of the soil increases strongly (Hipp 1974). Since the emission and scattering properties of the soil are strongly influenced by the soil dielectric constant, both active and passive microwave measurements are highly sensitive to soil moisture (Ulaby 1974; Schmugge et al. 1986). For retrieving soil moisture it is necessary to develop models that are capable of accounting for vegetation and surface roughness effects on the microwave signal. In 1975 Wilheit developed one of the first radiative transfer models that described the physics of microwave radiation in the soil. This important step in microwave research started a series of papers on the possibilities to retrieve soil moisture in both the active and passive microwave domain. In the passive domain Njoku and Kong (1977) used a simple regression technique on multi frequency microwave observations in combination with a given surface temperature to obtain soil moisture from a controlled bare soil site. In time, these models started to become more complex with the addition of surface roughness models (Choudhury et al. 1979; Wang and Choudhury 1981; Wigneron et al. 2001) cano (...truncated)