Filipe Aires

Global inundation dynamics inferred from multiple satellite observations, 1993–2000

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° x 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 x 10 6 km 2 (average for 1993-2000) to a mean minimum of 2.12 x 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.

Land surface microwave emissivities over the globe for a decade

Abstract Microwave land surface emissivities have been calculated over the globe for ∼10 yr between 19 and 85 GHz at 53° incidence angle for both orthogonal polarizations, using satellite observations from the Special Sensor Microwave Imager (SSM/I). Ancillary data (IR satellite observations and meteorological reanalysis) help remove the contribution from the atmosphere, clouds, and rain from the measured satellite signal and separate surface temperature from emissivity variations. The method to calculate the emissivity is general and can be applied to other sensors. The monthly mean emissivities are available for the community, with a 0.25° × 0.25° spatial resolution. The emissivities are sensitive to variations of the vegetation density, the soil moisture, the presence of standing water at the surface, or the snow behavior, and can help characterize the land surface properties. These emissivities (not illustrated in this paper) also allow for improved atmospheric retrieval over land and can help evaluat...

Sensitivity of satellite microwave and infrared observations to soil moisture at a global scale: Relationship of satellite observations to in situ soil moisture measurements

[1]xa0This study presents a systematic and integrated analysis of the sensitivity of the available satellite observations to in situ soil moisture measurements. Although none of these satellites is optimized for land surface characterization, before the launches of the SMOS- and HYDROS-dedicated missions they are the only potential sources of global soil moisture measurements. The satellite observations include passive microwave emissivities, active microwave scatterometer data, and infrared estimates of the diurnal amplitude of the surface skin temperature. The Global Soil Moisture Data Bank provides in situ soil moisture measurements in five separate regions. This simultaneous analysis of various satellite observations and the large amount of in situ measurements has two major advantages. First, this analysis helps identify and separate the physical mechanisms that affect the satellite observations. For example, we show that the passive microwave polarization differences at 19 GHz and above are essentially sensitive to the vegetation and not to the soil moisture (i.e., the correlation between microwave observations and soil moisture is only indirect and comes from the statistical correlation between vegetation and soil moisture). Second, this analysis enables an objective comparison of the relative potential of the various satellite observations for soil moisture retrieval when other conditions are held constant. The second part of this study benefits from this synthesis to derive a relationship between satellite observations and soil moisture at a global scale.

Joint characterization of vegetation by satellite observations from visible to microwave wavelengths: A sensitivity analysis

This study presents an evaluation and comparison of visible, near-infrared, passive and active microwave observations for vegetation characterization, on a global basis, for a year, with spatial resolution compatible with climatological studies. Visible and near-infrared observations along with the Normalized Difference Vegetation Index come from the Advanced Very High Resolution Radiometer. An atlas of monthly mean microwave land surface emissivities from 19 to 85 GHz has been calculated from the Special Sensor Microwave/Imager for a year, suppressing the atmospheric problems encountered with the use of simple channel combinations. The active microwave measurements are provided by the ERS-1 scatterometer at 5.25 GHz. The capacity to discriminate between vegetation types and to detect the vegetation phenology is assessed in the context of a vegetation classification obtained from in situ observations. A clustering technique derived from the Kohonen topological maps is used to merge the three data sets and interpret their relative variations. NDVI varies with vegetation density but is not very sensitive in semiarid environments and in forested areas. Spurious seasonal cycles and large spatial variability in several areas suggest that atmospheric contamination and/or solar zenith angle drift still affect the NDVI. Passive and active microwave observations are sensitive to overall vegetation structure: they respond to absorption, emission, and scattering by vegetation elements, including woody parts. Backscattering coefficients from ERS-1 are not sensitive to atmospheric variations and exhibit good potential for vegetation discrimination with ∼10 dB dynamic range between rain forest to arid grassland. Passive microwave measurements also show some ability to characterize vegetation but are less sensitive than active measurements. However, passive observations show sensitivity to the underlying surface wetness that enables detection of wetlands even in densely vegetated areas. Merging the data sets using clustering techniques capitalizes on the complementary strengths of the instruments for vegetation discrimination and shows promising potential for land cover characterization on a global basis.

An Evaluation of Microwave Land Surface Emissivities Over the Continental United States to Benefit GPM-Era Precipitation Algorithms

Passive microwave (PMW) satellite-based precipitation over land algorithms rely on physical models to define the most appropriate channel combinations to use in the retrieval, yet typically require considerable empirical adaptation of the model for use with the satellite measurements. Although low-frequency channels are better suited to measure the emission due to liquid associated with rain, most techniques to date rely on high-frequency, scattering-based schemes since the low-frequency methods are limited to the highly variable land surface background, whose radiometric contribution is substantial and can vary more than the contribution of the rain signal. Thus, emission techniques are generally useless over the majority of the Earths surface. As a first step toward advancing to globally useful physical retrieval schemes, an intercomparison project was organized to determine the accuracy and variability of several emissivity retrieval schemes. A three-year period (July 2004-June 2007) over different targets with varying surface characteristics was developed. The PMW radiometer data used includes the Special Sensor Microwave Imagers, SSMI Sounder, Advanced Microwave Scanning Radiometer (AMSR-E), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Advanced Microwave Sounding Units, and Microwave Humidity Sounder, along with land surface model emissivity estimates. Results from three specific targets in North America were examined. While there are notable discrepancies among the estimates, similar seasonal trends and associated variability were noted. Because of differences in the treatment surface temperature in the various techniques, it was found that comparing the product of temperature and emissivity yielded more insight than when comparing the emissivity alone. This product is the major contribution to the overall signal measured by PMW sensors and, if it can be properly retrieved, will improve the utility of emission techniques for over land precipitation retrievals. As a more rigorous means of comparison, these emissivity time series were analyzed jointly with precipitation data sets, to examine the emissivity response immediately following rain events. The results demonstrate that while the emissivity structure can be fairly well characterized for certain surface types, there are other more complex surfaces where the underlying variability is more than can be captured with the PMW channels. The implications for Global Precipitation Measurement-era algorithms suggest that physical retrievals are feasible over vegetated land during the warm seasons.

Temporal interpolation of global surface skin temperature diurnal cycle over land under clear and cloudy conditions

[1]xa0The surface skin temperature is a key parameter at the land-atmosphere interface. An accurate description of its diurnal cycle would not only help estimate the energy exchanges at the interface, it would also enable an analysis of the global surface skin diurnal cycle and its variability within the last 20 years. This study is based on the 3-hourly surface skin temperature estimated by the International Satellite Cloud Climatology Project (ISCCP) from the infrared measurements collected by the polar and geostationary meteorological satellites. The diurnal cycle of surface skin temperature is analyzed almost globally (60N–60S snow-free areas), using a Principal Component Analysis. The first three components are identifyed as the amplitude, the phase, and the width (i.e., daytime duration) of the diurnal cycle and represent 97% of the variability. PCA is used to regularize estimates of the diurnal cycle at a higher time resolution. A new temporal interpolation algorithm, designed to work when only a few measurements of surface temperature are available, is developed based on the PCA representation and an iterative optimization algorithm. This method is very flexible: only temperature measurements are used (no ancillary data), no surface model constraints are used, and the time and number of measurements are not fixed. The performance of this interpolation algorithm is tested for various diurnal sampling configurations. In particular, the potential to use the satellite microwave observations to provide a full diurnal surface temperature cycle in cloudy conditions is investigated.

Remote sensing from the infrared atmospheric sounding interferometer instrument 2. Simultaneous retrieval of temperature, water vapor, and ozone atmospheric profiles

[1]xa0A fast algorithm is developed to retrieve temperature, water vapor, and ozone atmospheric profile from the high spectral resolution Infrared Atmospheric Sounding Interferometer spaceborne instrument. Compression, denoising, and pattern recognition algorithms have been developed in a companion paper [Aires et al., 2002b]. A principal component analysis neural network using this a guess information is developed here to retrieve simultaneously temperature, water vapor and ozone atmospheric profiles. The performance of the resulting fast and accurate inverse model is evaluated with a climatological data set including rare events: temperature is retrieved with an error ≤1 K, and total amount of water vapor has a mean percentage error between 5 and 7%. Atmospheric water vapor layers are retrieved with an error between 10 and 15% most of the time. The statistics of the ozone retrieval are too optimistic due to a lack of representation of ozone variability in our test data set.

Independent component analysis of multivariate time series: Application to the tropical SST variability

With the aim of identifying the physical causes of variability of a given dynamical system, the geophysical community has made an extensive use of classical component extraction techniques such as principal component analysis (PCA) or rotational techniques (RT). We introduce a recently developed algorithm based on information theory: independent component analysis (ICA). This new technique presents two major advantages over classical methods. First, it aims at extracting statistically independent components where classical techniques search for decorrelated components (i.e., a weaker constraint). Second, the linear hypothesis for the mixture of components is not required. In this paper, after having briefly summarized the essentials of classical techniques, we present the new method in the context of geophysical time series analysis. We then illustrate the ICA algorithm by applying it to the study of the variability of the tropical sea surface temperature (SST), with a particular emphasis on the analysis of the links between El Nino Southern Oscillation (ENSO) and Atlantic SST variability. The new algorithm appears to be particularly efficient in describing the complexity of the phenomena and their various sources of variability in space and time.

Soil Moisture Retrieval Using Neural Networks: Application to SMOS

A methodology to retrieve soil moisture (SM) from Soil Moisture and Ocean Salinity (SMOS) data is presented. The method uses a neural network (NN) to find the statistical relationship linking the input data to a reference SM data set. The input data are composed of passive microwaves (L-band SMOS brightness temperatures,

Remote sensing from the infrared atmospheric sounding interferometer instrument 1. Compression, denoising, and first‐guess retrieval algorithms

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