Search

In the Congo River Basin (CRB), due to the lack of contemporary in situ observations, there is a limited understanding of the large-scale variability of its present-day hydrologic components and their link with climate. In this context, remote sensing observations provide a unique opportunity to better characterize those dynamics. Analyzing the Global Inundation Extent Multi-Satellite (GIEMS) time series, we first show that surface water extent (SWE) exhibits marked seasonal patterns, well distributed along the major rivers and their tributaries, and with two annual maxima located: i) in the lakes region of the Lwalaba sub-basin and ii) in the “Cuvette Centrale”, including Tumba and Mai-Ndombe Lakes. At an interannual time scale, we show that SWE variability is influenced by ENSO and the Indian Ocean dipole events. We then estimate water level maps and surface water storage (SWS) in floodplains, lakes, rivers and wetlands of the CRB, over the period 2003–2007, using a multi-satellite approach, which combines the GIEMS dataset with the water level measurements derived from the ENVISAT altimeter heights. The mean annual variation in SWS in the CRB is 81 ± 24 km 3 and contributes to 19 ± 5% of the annual variations of GRACE-derived terrestrial water storage (33 ± 7% in the Middle Congo). It represents also ∼6 ± 2% of the annual water volume that flows from the Congo River into the Atlantic Ocean. [ABSTRACT FROM AUTHOR]

The Tool to Estimate Land Surface Emissivity from Microwave to Submillimeter Waves (TELSEM2) is linked to a climatology of monthly emissivity estimates and provides a parameterization of the surface emissivity up to 700 GHz, in the framework of the preparation for the Ice Cloud Imager (ICI) on board the Meteorological Operational Satellite Second Generation (MetOp-SG). It is an updated version of the Tool to Estimate Land Surface Emissivities at Microwave Frequencies (TELSEM; Aires et al. 2011). This study presents the parameterization of continental snow and ice and sea ice emissivities in TELSEM2. It relies upon satellite-derived emissivities up to 200 GHz, and it is anchored to the Special Sensor Microwave Imager (SSM/I) TELSEM monthly climatology dataset (19-85 GHz). Emissivities from Météo-France and the National Oceanic and Atmospheric Administration (NOAA) at frequencies up to 190 GHz were used, calculated from the Special Sensor Microwave Imager/Sounder (SSMIS) and the Advanced Microwave Sounding Unit-B (AMSU-B) observations. TELSEM2 has been evaluated up to 325 GHz with the observations of the International Submillimeter Airborne Radiometer (ISMAR) and the Microwave Airborne Radiometer Scanning System (MARSS), which were operated on board the Facility for Airborne Atmospheric Measurements (FAAM) aircraft during the Cold-Air Outbreak and Submillimeter Ice Cloud Study (COSMICS) campaign over Greenland. Above continental snow and ice, TELSEM2 is very consistent with the aircraft estimates in spatially homogeneous regions, especially at 89 and 157 GHz. Over sea ice, the aircraft estimates are very variable spatially and temporally, and the comparisons with the TELSEM2 were not conclusive. TELSEM2 will be distributed in the new version of the RTTOV radiative transfer community code, to be available in 2017. [ABSTRACT FROM AUTHOR]

With the increasing volume of satellite observations, dimension reduction techniques are more and more important for storage or transmission. Furthermore, they are essential for inversion schemes that, in practice, cannot handle the huge amount of information provided by modern instruments for near-real-time inversion. In this article, we compare the theoretical advantages and limitations of the two general strategies: compression (i.e. feature extraction) and channel selection (i.e. feature selection). The statistical 'input variable selection' framework is adopted to revisit the basis of these remote-sensing techniques. The flexibility of these methods to specific applications is demonstrated. Special emphasis is put on the optimization of observation dimension reduction for the simultaneous retrieval of several variables (e.g. temperature and humidity). In addition to considering the signal-to-noise ratio for the variable to retrieve, we propose to account for contamination by other unknown variables. We also introduce a new approach named 'bottleneck channels' (BC), which combines compression and channel selection techniques and can therefore benefit from the advantages of both strategies. Various configurations of the BC approach can be considered: strict, linearly or nonlinearly projected, each with advantages and drawbacks. In the companion article, experiments will be conducted on microwave data to illustrate the practical advantages of each approach. BC are able to compress and suppress noise in a similar way to principal component analysis (PCA) and they can be interpreted as channels, as in channel selection. [ABSTRACT FROM AUTHOR]

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 Earth's 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. [ABSTRACT FROM PUBLISHER]

The article discusses the development of TOPMODEL for soil moisture redistribution within grid-cell based on its topography. The hydrological simulations using land surface models (LSMs) appears to be beneficial for wetland dynamics in relation to climatic challenges from biochemical emissions, such as methane gas. It presents a new way of evaluating the ORCHIDEE LSM using remote sensing data of areas with spatial distribution and temporal variation.

Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i. e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i. e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal... [ABSTRACT FROM AUTHOR]

A method has been developed to extend the Global Inundation Estimate from Multiple Satellites (GIEMS). The method presented here is based on retrieval principals similar to GIEMS but with an updated estimation of microwave emissivity in order to be less dependent on ancillary data and with some changes to the final surface water estimation to correct a known overestimation over low vegetation areas. The new methodology, GIEMS‐2, provides monthly estimates of surface water extent, including open water, wetlands, or rice paddies, and it has been applied to the Special Sensor Microwave/Imager and the Special Sensor Microwave Imager Sounder intercalibrated observations to produce a global data record of surface water extent from 1992 to 2015, on an equal area grid of 0.25° × 0.25° at the equator (∼25 km). The time series have been thoroughly evaluated: they are seamless and do not show any obvious artifact related to changes in satellite instrumentation over the ∼25 years. Comparisons with precipitation estimates show good agreement, displaying expected patterns related to surface conditions and precipitation regimes. The temporal variability of basin‐averaged estimates has also been compared with altimeter river height, showing a reasonable agreement. Production will be continued up to current time as soon as the observations become available, with efforts to improve the spatial and temporal resolutions of the estimates currently underway. Plain Language Summary: A method has been developed to provide global estimates of the continental surface waters and their dynamics. It comprises all surface waters, including open water, wetlands, or rice paddies. With multiple satellite data, a global data record of surface water extent is produced, on a monthly basis from 1992 to 2015, with a spatial resolution of 25 km. The time series have been thoroughly evaluated: They are seamless and do not show any obvious artifact related to changes in satellite instrumentation over the 25 years. Comparisons with precipitation estimates show good agreement, displaying expected patterns related to surface conditions and precipitation regimes. Temporal variability of basin‐averaged estimates has also been compared with altimeter river height, showing a reasonable agreement. Production will be continued up to current time as soon as carefully intercalibrated satellite observations become available, with efforts to improve the spatial and temporal resolution of the estimates currently underway. Key Points: A method is developed to derive a Global Inundation Estimate from Multiple Satellites (GIEMS‐2 product)It provides monthly estimates of surface water extent since 1992, with a spatial resolution of 0.25° × 0.25°The time series have been thoroughly evaluated and are suitable for climatological studies [ABSTRACT FROM AUTHOR]

A strong linear relationship is found between Special Sensor Microwave/Imager (SSM/I) microwave (19 and 37 GHz) surface emissivities at horizontal and vertical polarizations over snow- and ice-free land surfaces. This allows retrieving the land surface emissivity and temperature from satellite microwave brightness temperatures after atmospheric corrections. Over the Canadian sub-arctic continental area, we show that the main factor modifying the emissivity is the fraction of water surface (FWS) within a pixel. Accordingly, a map of the fraction of water surface across the Canadian landmass is derived, given a correspondence within 6% as compared to the 1 km2 Canadian National Topographic Database of water-covered areas. The microwave-derived surface temperatures are compared to synchronous in situ air and ground surface temperatures and also with independent satellite IR measurements over areas without snow or ice. Root mean square differences range between 2° and 3.5°, with mean bias error of the order of 1–3°. Better results are always obtained with the 37 GHz channel rather than with the 19 GHz channel. Over dense vegetation, the microwave-derived surface temperature is closer to the air temperature (at surface level) than to the ground temperature. The proposed simple retrieval algorithm, not sensitive to cloud cover, appears very useful for monitoring summer interannual or seasonal trends of the fraction of surface water, as well as the daily land surface temperature variation, which are very important parameters in environmental change analysis. [Copyright &y& Elsevier]