[1] Wetlands and surface waters are recognized to play important roles in climate, hydrologic and biogeochemical cycles, and availability of water resources. Until now, quantitative, global time series of spatial and temporal dynamics of inundation have been unavailable. This study presents the first global estimate of monthly inundated areas for 1993-2000. The data set is derived from a multisatellite method employing passive microwave land surface emissivities calculated from SSM/I and ISCCP observations, ERS scatterometer responses, and AVHRR visible and near-infrared reflectances. The satellite data are used to calculate inundated fractions of equal area grid cells (0.25°Â 0.25°at the equator), taking into account the contribution of vegetation to the passive microwave signal. Global inundated area varies from a maximum of 5.86 Â 10 6 km 2 (average for 1993-2000) to a mean minimum of 2.12 Â 10 6 km 2 . These values are considered consistent with existing independent, static inventories. The new multisatellite estimates also show good agreement with regional high-resolution SAR observations over the Amazon basin. The seasonal and interannual variations in inundation have been evaluated against rain rate estimates from the Global Precipitation Climatology Project (GPCP) and water levels in wetlands, lakes, and rivers measured with satellite altimeters. The inundation data base is now being used for hydrology modeling and methane studies in GCMs.

[1] Land surface waters play a primary role in the global water cycle and climate. As a consequence, there is a widespread demand for accurate and long-term quantitative observations of their distribution over the whole globe. This study presents the first global data set that quantifies the monthly distribution of surface water extent at ∼25 km sampling intervals over 12 years (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004). These estimates, generated from complementary multiple-satellite observations, including passive (Special Sensor Microwave Imager) and active (ERS scatterometer) microwaves along with visible and near-infrared imagery (advanced very high-resolution radiometer; AVHRR), were first developed over 1993-2000. The ERS encountered technical problems in 2001 and the processing scheme had to be adapted to extend the time series. Here we investigate and discuss the adjustments of the methodology, compare the various options, and show that the data set can be extended with good confidence beyond 2000, using ERS and AVHRR mean monthly climatologies. In addition to a large seasonal and interannual variability, the new results show a slight overall decrease in global inundated area between 1993 and 2004, representing an ∼5.7% reduction of the mean annual maximum in 12 years. The decrease is mainly observed in the tropics during the 1990s. Over inland water bodies and large river basins, we assess the variability of the surface water extent against related variables such as in situ river discharges, altimeter-derived and in situ river/floodplain water level heights, and precipitation estimates. This new 12 year data set of global surface water extent represents an unprecedented source of information for future hydrological or methane modeling.

Abstract. The analysis of microwave observations over land to determine atmospheric and surface parameters is still limited due to the complexity of the inverse problem. Neural network techniques have already proved successful as the basis of e•cient retrieval methods for nonlinear cases; however, first guess estimates, which are used in variational assimilation methods to avoid problems of solution nonuniqueness or other forms of solution irregularity, have up to now not been used with neural network methods. In this study, a neural network approach is developed that uses a first guess.