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A systematic and timely monitoring of land surface parameters that affect the hydrological cycle at local and global scales is of primary importance in obtaining a better understanding of geophysical processes and in managing environmental resources as well as natural disasters. Soil moisture and snow water equivalent are two quantities that play a major role in these applications. In this paper an algorithm for hydrological purposes (called hereinafter HydroAlgo), which is able to generate maps of snow depth (SD) and soil moisture content (SMC) from AMSR-E data, has been developed and implemented within the framework of the JAXA ADEOS-II/AMSR-E and GCOM/AMSR-2 programs, as well as of a project of the Italian Space Agency that is devoted to civil protection from floods and landslides. As auxiliary output, the algorithm also generates maps of vegetation biomass (VB). An initial phase of pre-processing includes the improvement of spatial resolution, as well as masking for urban areas, water bodies, and dense vegetation. The algorithm was then split into two branches, the first of which focused on the retrieval of SMC and the second, on SD. Both parameters were retrieved using Artificial Neural Network (ANN) methods. The algorithm was calibrated using a wide set of experimental data collected on three sites: Mongolia and Australia (for SMC), and Siberia (for SD), integrated with model simulations. These results were then validated by comparing the algorithm outputs with experimental data collected on two additional sites: a part of a watershed in Northern Italy, and a large portion of Scandinavia. An additional test of the algorithm was also performed on a large scale, and included sites characterized by differing climatic and meteorological conditions. [ABSTRACT FROM AUTHOR]

A systematic and timely monitoring of land surface parameters that affect the hydrological cycle at local and global scales is of primary importance in obtaining a better understanding of geophysical processes and in managing environmental resources as well as natural disasters. Soil moisture and snow water equivalent are two quantities that play a major role in these applications. In this paper an algorithm (HydroAlgo) which is able to generate maps of snow depth (SD) and soil moisture content (SMC) from AMSR-E data has been developed and implemented within the framework of the JAXA ADEOS-II/AMSR-E and GCOM/AMSR-2 programs, as well as of a project of the Italian Space Agency that is devoted to civil protection from floods and landslides. As auxiliary output, the algorithm also generates maps of vegetation biomass (VB). An initial phase of pre-processing includes the improvement of spatial resolution, as well as masking for urban areas, water bodies, and dense vegetation. The algorithm was then split into two branches, the first of which focused on the retrieval of SMC and the second, on SD. Both parameters were retrieved using Artificial Neural Network (ANN) methods. The algorithm was calibrated using a wide set of experimental data collected on three sites: Mongolia and Australia (for SMC), and Siberia (for SD), integrated with model simulations. These results were then validated by comparing the algorithm outputs with experimental data collected on two additional sites: a part of a watershed in Northern Italy, and a large portion of Scandinavia. An additional test of the algorithm was also performed on a large scale, and included sites characterized by differing climatic and meteorological conditions. [ABSTRACT FROM AUTHOR]

The sensitivity of radar backscattering to the principal hydrological parameters, such as vegetation biomass, soil moisture, and surface roughness, is discussed. Results obtained by using multifrequency synthetic aperture radar (SAR) data measured by the Jet Propulsion Laboratory Airborne Synthetic Aperture Radar, Spaceborne Imaging Radar-C, and European Remote Sensing 1/2 sensors are summarized. The sensitivity of L- and C-bands to spatial variations of plant and soil parameters is masked by the presence of surface roughness, which in turn affects the radar signal. However, from the observation of data collected at different dates and averaged over a relatively wide area that includes several fields, the correlation to soil moisture and vegetation biomass is found to be significant, since the effects of spatial variations are smoothed. On the other hand, the sensitivity to surface roughness becomes appreciable when multitemporal data are averaged in time, thus reducing the effects of temporal moisture variations. [ABSTRACT FROM PUBLISHER]

The potential of Special Sensor Microwave Imager (SSM/I) data in monitoring land surface features and global changes in synergy with the European Space Agency/European Remote Sensing satellite (ESA/ERS) wind scatterometer was investigated. The backscattering coefficient, the brightness temperature, and some related quantities, measured during a yearly cycle on a number of selected test sites in different climatic regions of the world, were related to surface features obtained from ground information. The major characteristics of land surfaces were obtained from their emissivity spectra and polarization state, while a further classification of surfaces was carried out by means of multifrequency, dual-polarized data. Vegetation biomass and snow cover were estimated from appropriate microwave indices and from spectra of brightness temperature. [ABSTRACT FROM AUTHOR]

Multi-frequency and multi-temporal polarimetric SAR measurements, carried out during SIR-C/X-SAR missions over the Montespertoli area have been analysed and compared with data collected at the same frequency and polarization, but at different dates, with the NASA/JPL AIRSAR. This paper presents an analysis of the achieved results aiming at evaluating the contribution of SAR data for estimating some geophysical parameters which play a significant role in hydrological processes and in particular soil moisture and roughness. The study has pointed out that in the scale of surface roughness typical of agricultural areas, a co-polar L-bandsensor gives the highest information content for estimating soil moisture and surface roughness. The sensitivity to soil moisture and surface roughness for individual fields is rather low since both parameters affect the radar signal. However, considering data collected at different dates and averaged over a relatively wide area that includes several fields, the correlation to soil moisture is significant, since the effects of spatial roughness variations are smoothed. On the other hand the sensitivity to surface roughness is better manifested at a spatial scale, integrating on time to reduce the effects of moisture variation. The retrieval of both soil moisture and surface roughness has been performed with good results by means of a semi-empirical model. [ABSTRACT FROM AUTHOR]

The radio structure of the active region McMath 11976 has been investigated with an east-west resolution of 16″ at a wavelength of 2.8 cm. Six bright components were found with angular sizes of the order of 20″. From the position measure of these components in two following days the heights above the solar photosphere are determined. Their positions are compared with the Hα and magnetic structure of the active region. The derived brightness temperatures reach unusual values, up to 6 × 10K for the brightest component. [ABSTRACT FROM AUTHOR]

The development of three intense active centers during their appearance on the solar disk is examined using high resolution observations at 2.8 cm. Each region shows a very bright component with brightness temperature > 10 K and size smaller than 20″. The development of the bright components have been investigated on different time scales. Intensity fluctuations on a time scale of minutes are within the instrumental accuracy while the evolution over periods of days shows a variation of the flux density up to 30-40% per day. The problem of the bright cores height is discussed. Heights within 10 × 10 and 40 × 10 km are found using their apparent displacement on the disk. [ABSTRACT FROM AUTHOR]

Discusses the geological aspects concerning the microwave emission of crops. Information on the experimental results; Estimation of the dependency of optical depth and single-scattering albedo on plant water content; Representation of the emission characteristics of two wheat crops.