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Jimenez C.,Estellus | Jimenez C.,Paris Observatory | Michel D.,ETH Zurich | Hirschi M.,ETH Zurich | And 3 more authors.
Remote Sensing Applications: Society and Environment | Year: 2017

Land heat fluxes are essential components of the water and energy cycle and important variables in the management of agronomy and forestry resources. The estimation of the heat fluxes can be done with a number of methodologies, with some of them having the land surface temperature (Ts) as one of their key inputs to derive the fluxes. Here the production of Ts-driven surface heat fluxes over a grassland site in Switzerland is demonstrated by running a specific heat flux methodology (SEBS) fed by a number of satellite Ts estimates (from the instruments AATSR, MODIS, SEVIRI, AMSR-E, and SSMIS). The Ts estimates are compared with an in situ estimate derived from radiometric observations at the station, and the satellite latent heat flux (LE) estimates with the station Eddy Covariance (EC) measurements. The satellite Ts products include estimates at different spatial resolutions (from ∼1 to ∼25 km) and time samplings (from 2 overpasses per day to 1/2 hourly observations). Root Mean Square Differences (RMSDs) between the daytime satellite and station Ts are 2.72 (AATSR), 4.41 (MODIS), 3.59 (SEVIRI), 3.81 (AMSR-E), and 2.79 (SSMIS), but given the different time samplings and spatial resolutions its is difficult to be conclusive about the accuracy of the Ts estimates. Concerning the flux estimates, for those sensors with midday overpasses, a RMSD of ∼25% are found when comparing the instantaneous latent flux (LE, or evaporation expressed as an amount of water) at satellite overpass with the EC observations, which compares well with the accuracy reported elsewhere for similar landscapes. Given that both Ts and LE are evaluated at the station, a link between Ts and LE accuracy is investigated, but its is not apparent for this specific comparison. This could be related to SEBS accuracy also depending on other variables, apart from Ts, but also to the representativeness of the metrics used for the evaluation given the spatial miss-matches existing between the satellite estimates and station observations. Discrepancies were observed between the EC fluxes, the measured surface available energy, and the evaporation estimates from a lysimeter also present at the station, illustrating also the difficulties of the ground observations to provide accurate heat fluxes for satellite evaluation. © 2017 Elsevier B.V.


Jimenez C.,Estellus | Jimenez C.,Paris Observatory | Prigent C.,Paris Observatory | Ermida S.L.,University of Lisbon | Moncet J.-L.,Atmospheric and Environmental Research Inc.
Journal of Geophysical Research: Atmospheres | Year: 2017

Inversions of the Earth Observation Satellite (EOS) Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (Tbs) to derive the land surface temperature (Ts) are presented based on building a global transfer function by neural networks trained with AMSR-E Tbs and retrieved microwave Ts*. The only required inputs are the Tbs and monthly climatological emissivities, minimizing the dependence on ancillary data. The inversions are accompanied by a coarse estimation of retrieval uncertainty, an estimate of the quality of the retrieval, and a series of flags to signal difficult inversion situations. For ∼75% of the land surface the root-mean-square difference (RMSD) between the training target Ts* and the neural network retrieved Ts is below 2.8 K. The RMSD when comparing with the Moderate Resolution Imaging Spectroradiometer (MODIS) clear-sky Ts is below 3.9 K for the same conditions. Over 10 ground stations, AMSR-E and MODIS Ts were compared with the in situ data. Overall, MODIS agrees better with the station Ts than AMSR-E (all-station mean RMSD of 2.4 K for MODIS and 4.0 for AMSR-E), but AMSR-E provides a larger number of Ts estimates by being able to measure under cloudy conditions, with an approximated ratio of 3 to 1 over the analyzed stations. At many stations the RMSD of the AMSR-E clear and cloudy sky are comparable, highlighting the ability of the microwave inversions to provide Ts under most atmospheric conditions. Closest agreement with the in situ Ts happens for stations with dense vegetation, where AMSR-E emissivity is less varying. ©2017. American Geophysical Union. All Rights Reserved.


Munier S.,Estellus | Belaud G.,Montpellier SupAgro | Perrin C.,IRSTEA
Journal of Hydrologic Engineering | Year: 2014

This study addresses the sensitivity of short-term flow forecasting in the Seine River basin (43,800 km2, France) to the spatial distribution using a semidistributed model [Transfer with the Génie Rural model (TGR)]. The basin was decomposed into intermediate basins depending on the gauging stations selected for this study. A lumped hydrological model was applied on each intermediate basin and a routing model was used to propagate the discharge through the river network. Discharge data at the gauging stations were assimilated using a Kalman filter and tests for flow forecasting were performed with a lead time of up to 72 h. Several spatial configurations, defined by a selection of one or several gauging stations, were tested and the performances were compared with a reference lumped model currently used operationally by the regional flood forecasting center. Results showed that the forecasting performance improves with an increase in the degree of spatialization. Nevertheless, this improvement was not systematic and the integration of some particular gauging stations degraded the model performance. In addition, it was shown that integrating some other stations (generally the most upstream) led to a negligible improvement. This suggests that in an operational context, where the model has to be robust and computationally efficient, some efforts should focus on finding the optimal spatial distribution, which is not necessarily the one using all the available stations. © 2014 American Society of Civil Engineers.


Ferraro R.R.,National Oceanic and Atmospheric Administration | Peters-Lidard C.D.,NASA | Hernandez C.,University of Maryland University College | Joseph Turk F.,Jet Propulsion Laboratory | And 17 more authors.
IEEE Transactions on Geoscience and Remote Sensing | Year: 2013

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. © 1980-2012 IEEE.


Prigent C.,Paris Observatory | Papa F.,LEGOS | Aires F.,Estellus | Jimenez C.,Paris Observatory | And 2 more authors.
Geophysical Research Letters | Year: 2012

We developed a remote sensing approach based on multi-satellite observations, which provides an unprecedented estimate of monthly distribution and area of land-surface open water over the whole globe. Results for 1993 to 2007 exhibit a large seasonal and inter-annual variability of the inundation extent with an overall decline in global average maximum inundated area of 6% during the fifteen-year period, primarily in tropical and subtropical South America and South Asia. The largest declines of open water are found where large increases in population have occurred over the last two decades, suggesting a global scale effect of human activities on continental surface freshwater: denser population can impact local hydrology by reducing freshwater extent, by draining marshes and wetlands, and by increasing water withdrawals. Copyright 2012 by the American Geophysical Union.


Tian Y.,The Interdisciplinary Center | Peters-Lidard C.D.,NASA | Harrison K.W.,The Interdisciplinary Center | Prigent C.,Paris Observatory | And 5 more authors.
IEEE Transactions on Geoscience and Remote Sensing | Year: 2014

Uncertainties in the retrievals of microwave land-surface emissivities are quantified over two types of land surfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including the Special Sensor Microwave Imager, the Tropical Rainfall Measuring Mission Microwave Imager, and the Advanced Microwave Scanning Radiometer for Earth Observing System, are studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two land-surface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors in the retrievals. Generally, these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 1%-4% (3-12 K) over desert and 1%-7% (3-20 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.5%-2% (2-6 K). In particular, at 85.5/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are most likely caused by rain/cloud contamination, which can lead to random errors up to 10-17 K under the most severe conditions. © 2013 IEEE.


McCabe M.F.,King Abdullah University of Science and Technology | Ershadi A.,King Abdullah University of Science and Technology | Jimenez C.,Estellus | Miralles D.G.,VU University Amsterdam | And 2 more authors.
Geoscientific Model Development | Year: 2016

Determining the spatial distribution and temporal development of evaporation at regional and global scales is required to improve our understanding of the coupled water and energy cycles and to better monitor any changes in observed trends and variability of linked hydrological processes. With recent international efforts guiding the development of long-term and globally distributed flux estimates, continued product assessments are required to inform upon the selection of suitable model structures and also to establish the appropriateness of these multi-model simulations for global application. In support of the objectives of the Global Energy and Water Cycle Exchanges (GEWEX) LandFlux project, four commonly used evaporation models are evaluated against data from tower-based eddy-covariance observations, distributed across a range of biomes and climate zones. The selected schemes include the Surface Energy Balance System (SEBS) approach, the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model, the Penman-Monteith-based Mu model (PM-Mu) and the Global Land Evaporation Amsterdam Model (GLEAM). Here we seek to examine the fidelity of global evaporation simulations by examining the multi-model response to varying sources of forcing data. To do this, we perform parallel and collocated model simulations using tower-based data together with a global-scale grid-based forcing product. Through quantifying the multi-model response to high-quality tower data, a better understanding of the subsequent model response to the coarse-scale globally gridded data that underlies the LandFlux product can be obtained, while also providing a relative evaluation and assessment of model performance. Using surface flux observations from 45 globally distributed eddy-covariance stations as independent metrics of performance, the tower-based analysis indicated that PT-JPL provided the highest overall statistical performance (0.72; 61Wm-2; 0.65), followed closely by GLEAM (0.68; 64Wm-2; 0.62), with values in parentheses representing the R2, RMSD and Nash-Sutcliffe efficiency (NSE), respectively. PM-Mu (0.51; 78Wm-2; 0.45) tended to underestimate fluxes, while SEBS (0.72; 101 Wm-2; 0.24) overestimated values relative to observations. A focused analysis across specific biome types and climate zones showed considerable variability in the performance of all models, with no single model consistently able to outperform any other. Results also indicated that the global gridded data tended to reduce the performance for all of the studied models when compared to the tower data, likely a response to scale mismatch and issues related to forcing quality. Rather than relying on any single model simulation, the spatial and temporal variability at both the tower- and grid-scale highlighted the potential benefits of developing an ensemble or blended evaporation product for global-scale LandFlux applications. Challenges related to the robust assessment of the LandFlux product are also discussed. © Author(s) 2016.


Pellet V.,Estellus | Aires F.,French National Center for Scientific Research
Quarterly Journal of the Royal Meteorological Society | Year: 2016

In the first article of this series, the two classical strategies to reduce satellite data dimension (i.e. compression and channel selection) were presented, together with the introduction of a new method, the so-called 'bottleneck channels' (BC). BC are a compromise between the two classical approaches and can benefit from the advantages of both. In this article, the three methodologies are tested using experiments on a synthetic dataset corresponding to a hyperspectral conceptual instrument in the microwave, for frequencies up to 500 GHz. As expected, principal component analysis (PCA) based methods are best to compress data, but their components lack the physical interpretability of real channels. Channel selection methods preserve this physical meaning but require a much larger number of channels in order to use the redundancy of information to reduce instrumental noise. The new BC method appears to be a good compromise. It can be seen as a PCA compression method where the components are constrained to be instrument channels, facilitating their understanding, inversion or assimilation. BC allows for an easy calibration of data based on radiative transfer simulations and also alleviates the mixing problem of the PCA technique, where various physical variabilities (e.g. temperature, humidity, clouds) can be mixed in the same extracted components. Furthermore, the BC compression rate is equivalent to that of PCA-based methods even with a limited number of BC. © 2016 Royal Meteorological Society.


Munier S.,Estellus | Aires F.,Estellus | Aires F.,French National Center for Scientific Research | Schlaffer S.,Vienna University of Technology | And 5 more authors.
Journal of Geophysical Research D: Atmospheres | Year: 2014

In this study, we applied the integration methodology developed in the companion paper by Aires (2014) by using real satellite observations over the Mississippi Basin. The methodology provides basin-scale estimates of the four water budget components (precipitation P, evapotranspiration E, water storage change ΔS, and runoff R) in a two-step process: the Simple Weighting (SW) integration and a Postprocessing Filtering (PF) that imposes the water budget closure. A comparison with in situ observations of P and E demonstrated that PF improved the estimation of both components. A Closure Correction Model (CCM) has been derived from the integrated product (SW+PF) that allows to correct each observation data set independently, unlike the SW+PF method which requires simultaneous estimates of the four components. The CCM allows to standardize the various data sets for each component and highly decrease the budget residual (P - E - ΔS - R). As a direct application, the CCM was combined with the water budget equation to reconstruct missing values in any component. Results of a Monte Carlo experiment with synthetic gaps demonstrated the good performances of the method, except for the runoff data that has a variability of the same order of magnitude as the budget residual. Similarly, we proposed a reconstruction of ΔS between 1990 and 2002 where no Gravity Recovery and Climate Experiment data are available. Unlike most of the studies dealing with the water budget closure at the basin scale, only satellite observations and in situ runoff measurements are used. Consequently, the integrated data sets are model independent and can be used for model calibration or validation. ©2014. American Geophysical Union. All Rights Reserved.


Mahfouf J.-F.,Meteo - France | Birman C.,Meteo - France | Aires F.,Estellus | Prigent C.,LERMA | And 2 more authors.
Quarterly Journal of the Royal Meteorological Society | Year: 2015

This study examines the information content on atmospheric temperature and humidity profiles that could be provided by a future spaceborne microwave sensor with a few hundred radiances in the millimetre and submillimetre spectral domains (ranging from 7-800 GHz). A channel selection method based on optimal estimation theory is undertaken, using a database of profiles with associated errors from the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical weather prediction model and the radiative transfer model Atmospheric Radiative Transfer Simulator (ARTS) under clear-sky conditions. The main results indicate that, by increasing the number of channels within the oxygen absorption band around 60 GHz and within the water-vapour absorption band at 183 GHz, the accuracy of temperature and humidity retrievals in the troposphere and stratosphere (for temperature) would be noticeably improved compared with present and planned microwave radiometers. The channels located in the absorption lines at 118 GHz and above 200 GHz do not bring significant additional information regarding atmospheric profiles under clear-sky conditions, partly due to greater radiometric noise. With a set of 137 selected channels that contribute to 90% of the total information content (measured by the degree of freedom for signal), it is possible to achieve almost the same performance in terms of variance error reduction as with 276 candidate channels. Sensitivity studies of various prescribed quantities defining the channel selection have been undertaken, in order to check the robustness of the conclusions. They show that none of the choices modifies the above findings. © 2015 Royal Meteorological Society.

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