M. Drusch

European Center For Medium Range Weather Forecasts, Shinfield, England, United Kingdom

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Publications (84)123.2 Total impact

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    IEEE Transactions on Geoscience and Remote Sensing 06/2015; 53(6):3507-3521. DOI:10.1109/TGRS.2014.2378913 · 2.93 Impact Factor
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    Journal of Hydrometeorology 02/2015; 16(3):150223132350009. DOI:10.1175/JHM-D-14-0052.1 · 3.57 Impact Factor
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    ABSTRACT: The Soil Moisture and Ocean Salinity (SMOS) mission observes brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) with a daily coverage of the polar regions. L-band radiometry has been shown to provide information on the thickness of thin sea ice. Here, we apply a new emission model that has previously been used to investigate the impact of snow on thick Arctic sea ice. The model has not yet been used to retrieve ice thickness. In contrast to previous SMOS ice thickness retrievals, the new model allows us to include a snow layer in the brightness temperature simulations. Using ice thickness estimations from satellite thermal imagery, we simulate brightness temperatures during the ice growth season 2011 in the northern Baltic Sea. In both the simulations and the SMOS observations, brightness temperatures increase by more than 20 K, most likely due to an increase of ice thickness. Only if we include the snow in the model, the absolute values of the simulations and the observations agree
    02/2015; 67. DOI:10.3402/tellusa.v67.24617
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    ABSTRACT: Remote estimation of sun-induced chlorophyll fluorescence emitted by terrestrial vegetation can provide an unparalleled opportunity to track spatio-temporal variations of photosynthetic efficiency. Here we provide the first direct experimental evidence that the two peaks of the chlorophyll fluorescence spectrum can be accurately mapped from high-resolution radiance spectra and that the signal is linked to variations in actual photosynthetic efficiency. Red and far-red fluorescence measured using a novel airborne imaging spectrometer over a grass carpet treated with an herbicide known to inhibit photosynthesis was significantly higher than the corresponding signal from an equivalent untreated grass carpet. The reflectance signal of the two grass carpets was indistinguishable, confirming that the fast dynamic changes in fluorescence emission were related to variations in the functional status of actual photosynthesis induced by herbicide application. Our results from a controlled experiment at the local scale illustrate the potential for the global mapping of terrestrial photosynthesis through space-borne measurements of chlorophyll fluorescence.
    02/2015; 42(6). DOI:10.1002/2014GL062943
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    ABSTRACT: A methodology to retrieve soil moisture (SM) from SMOS data is presented. The method uses a Neural Network (NN) to find the statistical relationship linking the input data to a reference SM dataset. The input data is composed of passive microwaves (L-band SMOS brightness temperatures, \Tb's) complemented with active microwaves (C-band ASCAT backscattering coefficients), and MODIS NDVI. The reference SM data used to train the NN are ECMWF model predictions. The best configuration of SMOS data to retrieve SM using a NN is using \Tb's measured with both H and V polarizations for incidence angles from 25$^\circ$ to 60$^\circ$. The inversion of soil moisture can be improved by $\sim 10 \%$ by adding MODIS NDVI and ASCAT backscattering data and by an additional $\sim 5 \%$ by using local information on the maximum and minimum record of SMOS Tb's (or ASCAT backscattering coefficients) and the associated SM values. The NN inverted SM is able to capture the temporal and spatial variability of the SM reference dataset. The temporal variability is better captured when either adding active microwaves or using a local normalization of SMOS Tb's. The NN SM products have been evaluated against in situ measurements, giving results of comparable or better (for some NN configurations) quality to other SM products. The NN used in this study allows to retrieve SM globally on a daily basis. These results open interesting perspectives such as a near real time processor and data assimilation in weather prediction models.
    IEEE Transactions on Geoscience and Remote Sensing 01/2015; in press. DOI:10.1109/TGRS.2015.2430845 · 2.93 Impact Factor
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    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 01/2015; DOI:10.1109/JSTARS.2015.2422998 · 2.83 Impact Factor
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    ABSTRACT: Microwave radiometry L-band SMOS SMAP Radiative transfer Snow Soil freeze/thaw Passive L-band (1–2 GHz) observables are sensitive to surface soil moisture and ocean salinity, which is the core of the "soil moisture and ocean salinity" (SMOS) mission of the European Space Agency (ESA). This work investigates microwave emission processes that are important to link L-band brightness temperatures with soil freeze/ thaw states and the presence and the state of snow. To this end, a ground snow radiative transfer (GS RT) model has been developed on the basis of the "Microwave Emission Model of Layered Snowpacks" (MEMLS). Our model sensitivity study revealed that L-band emission of a freezing ground can be affected significantly by dry snow, which has been mostly disregarded in previous studies. Simulations suggest that even dry snow with mostly negligible absorption at the L-band can impact L-band emission of winter landscapes significantly. We found that impedance matching and refraction caused by a dry snowpack can increase or decrease L-band emission depending on the polarization and the observation angle. Based on the performed sensitivity study, these RT processes can be compensatory at vertical polarization and the observation angle of 50°. This suggests the use of vertical polarized brightness temperatures measured at around 50° in order to achieve segregated information on soil-frost. Furthermore, our simulations demonstrate a significant sensitivity of L-band emission at horizontal polarization with respect to the density of the lowest snow layer as the result of refraction and impedance matching by the snowpack.
    Remote Sensing of Environment 08/2014; 154:180-191. DOI:10.1016/j.rse.2014.08.029 · 6.39 Impact Factor
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    ABSTRACT: The launch of the SMOS mission 2-Nov-2009 marked a milestone in remote sensing for it was the first time a radiometer capable of acquiring wide field of view images at every single snapshot, a unique feature of the synthetic aperture technique, made it to space. The technology behind such an achievement was developed thanks to the effort of a community of researchers and engineers in different groups around the world. It was only because of their joint work that SMOS finally became a reality. The fact that the European Space Agency, together with CNES (Centre National d'Etudes Spatiales) and CDTI (Centro para el Desarrollo Tecnológico e Industrial), managed to get the project through should be considered a merit and a reward for that entire community. This paper is an invited historical review that, within a very limited number of pages, tries to provide insight into some of the developments which, one way or another, are imprinted in the name of SMOS.
    06/2014; 49(6). DOI:10.1002/2013RS005230
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    ABSTRACT: We present a novel algorithm for detecting seasonal soil freezing processes using L-band microwave radiometry. L-band is the optimal choice of frequency for the monitoring of soil freezing, due to the inherent high contrast of the microwave signature between the frozen and thawed states of the soil medium. Dual-polarized observations of L-band brightness temperature at a range of observation angles were collected from a tower-based instrument, and evaluated against ancillary information on soil and snow properties over four winter seasons. During the first three winter periods the measurement site was located over mineral soil on a forest clearing, for the fourth winter the instrument was moved to a wetland site. Both sites are located in Sodankylä, Northern Finland. The test sites represent two environments typical for the northern boreal forest zone. The data were applied to derive an empirical relation between the onset and progress of soil freezing and the observed passive L-band signature. A retrieval algorithm was developed using the observations at the forest opening site. The algorithm exploits the perceived change in brightness temperature and the change in the relative difference between the signatures at horizontal and vertical polarization. With the collected experimental dataset, these features were linked optimally to the progress of soil freezing by choice of observation angle, polarization and temporal averaging. The wetland site observations provided the first opportunity for demonstrating the developed algorithm over a different soil type, giving a first estimate of the algorithm performance over larger heterogeneous targets. The future objective is to adapt the algorithm to L-band satellite observations. The present study is highly relevant for the development of freeze–thaw algorithms from current and future L-band satellite missions such as SMOS and SMAP.
    Remote Sensing of Environment 05/2014; 147:206–218. DOI:10.1016/j.rse.2014.03.007 · 6.39 Impact Factor
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    ABSTRACT: In a recent paper, Leroux et al. [1] compared three satellite soil moisture data sets (SMOS, AMSR-E, and ASCAT) and ECMWF forecast soil moisture data to in situ measurements over four watersheds located in the United States. Their conclusions stated that SMOS soil moisture retrievals represent “an improvement [in RMSE] by a factor of 2- 3 compared with the other products” and that the ASCAT soil moisture data are “very noisy and unstable”. In this clarification, the analysis of Leroux et al. is repeated using a newer version of the ASCAT data and additional metrics are provided. It is shown that the ASCAT retrievals are skillful, although they show some unexpected behavior during summer for two of the watersheds. It is also noted that the improvement of SMOS by a factor of 2-3 mentioned by Leroux et al. is driven by differences in bias and only applies relative to AMSR-E and the ECWMF data in the now obsolete version investigated by Leroux et al.
    IEEE Transactions on Geoscience and Remote Sensing 05/2014; 52(3):1901-1906. DOI:10.1109/TGRS.2013.2282172 · 2.93 Impact Factor
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    ABSTRACT: Following the launch of ESA's Soil Moisture and Ocean salinity (SMOS) mission it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In a first demonstration study, sea ice thickness has been derived using a semi-empirical algorithm with constant tie-points. Here we introduce a novel iterative retrieval algorithm that is based on a sea ice thermodynamic model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within a SMOS footprint are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS based sea ice thickness data set from 2010 on. This data set is compared and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study.
    The Cryosphere Discussions 12/2013; 7(6):5735-5792. DOI:10.5194/tcd-7-5735-2013
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    ABSTRACT: The microwave interferometric radiometer of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission measures at a frequency of 1.4 GHz in the L-band. In contrast to other microwave satellites, low frequency measurements in L-band have a large penetration depth in sea ice and thus contain information on the ice thickness. Previous ice thickness retrievals have neglected a snow layer on top of the ice. Here, we implement a snow layer in our emission model and investigate how snow influences L-band brightness temperatures and whether it is possible to retrieve snow thickness over thick Arctic sea ice from SMOS data. We find that the brightness temperatures above snow-covered sea ice are higher than above bare sea ice and that horizontal polarisation is more affected by the snow layer than vertical polarisation. In accordance with our theoretical investigations, the root mean square deviation between simulated and observed horizontally polarised brightness temperatures decreases from 20.9 K to 4.7 K, when we include the snow layer in the simulations. Although dry snow is almost transparent in L-band, we find brightness temperatures to increase with increasing snow thickness under cold Arctic conditions. The brightness temperatures' dependence on snow thickness can be explained by the thermal insulation of snow and its dependence on the snow layer thickness. This temperature effect allows us to retrieve snow thickness over thick sea ice. For the best simulation scenario and snow thicknesses up to 35 cm, the average snow thickness retrieved from horizontally polarised SMOS brightness temperatures agrees within 0.1 cm with the average snow thickness measured during the IceBridge flight campaign in the Arctic in spring 2012. The corresponding root mean square deviation is 5.5 cm, and the coefficient of determination is r2 = 0.58.
    The Cryosphere Discussions 11/2013; 7(6). DOI:10.5194/tc-7-1971-2013
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    ABSTRACT: The Fluorescence Explorer (FLEX) mission is currently subject to feasibility (Phase A) study as one of the two candidates of ESA's 8th Earth Explorer opportunity mission. The FLuORescence Imaging Spectrometer (FLORIS) will be an imaging grating spectrometer onboard of a medium sized satellite flying in tandem with Sentinel-3 in a Sun synchronous orbit at a height of about 815 km. FLORIS will observe vegetation fluorescence and reflectance within a spectral range between 500 nm and 780 nm. It will thereby cover the photochemical reflection features between 500 nm and 600 nm, the Chlorophyll absorption band between 600 and 677 nm, and the red-edge in the region from 697 nm to 755 nm being located between the Oxygen A and B absorption bands. By this measurement approach, it is expected that the full spectrum and amount of the vegetation fluorescence radiance can be retrieved, and that atmospheric corrections can efficiently be applied. FLORIS will measure Earth reflected spectral radiance at a relatively high spectral resolution of ~0.3 nm around the Oxygen absorption bands. Other spectral band areas with less pronounced absorption features will be measured at medium spectral resolution between 0.5 and 2 nm. FLORIS will provide imagery at 300 m resolution on ground with a swath width of 150 km. This will allow achieving global revisit times of less than one month so as to monitor seasonal variations of the vegetation cycles. The mission life time is expected to be at least 4 years. The fluorescence retrieval will make use of information coming from OLCI and SLSTR, which are onboard of Sentinel-3, to monitor temperature, to detect thin clouds and to derive vegetation reflectance and information on the aerosol content also outside the FLORIS spectral range. In order to mitigate the technological and programmatic risk of this Explorer mission candidate, ESA has initiated two comprehensive bread-boarding activities, in which the most critical technologies and instrument performance shall be investigated and demonstrated. The breadboards will include representative optics and dispersive elements in a configuration, which is expected to be very close to the instrument flight configuration. This approach follows the guideline to reach, before it goes into the implementation phase, a technology readiness level of at least 5. It thereby requires a demonstration of predicted performance in a configuration, where the basic technological components are integrated with reasonably realistic supporting elements such that it can be tested in a simulated environment. We will report, within the limits of the competitive nature of the industrial studies, on the currently running or planned preparatory activities. We will present the mission configuration, the imposed instrument requirements and the identified instrument concepts as derived by the Phase A studies.
    Proceedings of SPIE - The International Society for Optical Engineering 10/2013; DOI:10.1117/12.2032060 · 0.20 Impact Factor
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    ABSTRACT: Vegetation fluorescence when measured from space contributes only a tiny fraction of the signal coming on top of the reflected radiance by the Earth surface and the atmosphere. As a consequence, imaging spectrometers have to provide sufficient throughput and radiometric accuracy to enable accurate global monitoring of the daily to seasonal variations of the Earth's vegetation breath, which is particularly challenging if ground resolutions of a few hundred meters are targeted. Since fluorescence retrieval algorithms have to make corrections for atmospheric effects, it is necessary to provide sufficient spectral resolution, so that signal alterations due to the main parameters such as surface pressure, atmospheric temperature profile, vertical distribution of aerosols concentration, and water vapour content can be accurately modelled. ESA's Earth Explorer 8 candidate mission FLEX carries a Fluorescence Imaging Spectrometer (FLORIS), which has been designed and optimised to enable such measurement. The spectrometer will measure in a spectral range between 500 and 780 nm and provide high spectral resolution of 0.3 nm in particular at the Oxygen-A and -B bands. It will also cover the photochemical reflection features between 500 and 600 nm, the Chlorophyll absorption region between 600 and 677 nm, and the red-edge in the region of 697 to 755 nm. FLEX will fly in formation with Sentinel-3 in order to further enhance the spectral coverage from measurements made by the Sentinel-3 instruments OLCI and SLSTR, particularly for cloud screening and proper characterization of the atmospheric status.
    Proceedings of SPIE - The International Society for Optical Engineering 09/2013; DOI:10.1117/12.2024245 · 0.20 Impact Factor
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    ABSTRACT: The International Soil Moisture Network ( ISMN) was initiated in 2009 to support calibration and validation of remote sensing products and land surface models, and to facilitate studying the behavior of our climate over space and time. The ISMN does this by collecting and harmonizing soil moisture data sets from a large variety of individually operating networks and making them available through a centralized data portal. Due to the diversity of climatological conditions covered by the stations and differences in measurement devices and setup, the quality of the measurements is highly variable. Therefore, appropriate quality characterization is desirable for a correct use of the data sets. This study presents a new, automated quality control system for soil moisture measurements contained in the ISMN. Two types of quality control procedures are presented. The first category is based on the geophysical dynamic range and consistency of the measurements. It includes flagging values exceeding a certain threshold and checking the validity of soil moisture variations in relation to changes in soil temperature and precipitation. In particular, the usability of global model- or remote sensing-based temperature and precipitation data sets were tested for this purpose as an alternative to in situ measurements, which are often not recorded at the soil moisture sites themselves. The second category of procedures analyzes the shape of the soil moisture time series to detect outliers (spikes), positive and negative breaks, saturation of the signal, and unresponsive sensors. All methods were first validated and then applied to all the data sets currently contained in the ISMN. A validation example of an AMSR-E satellite and a GLDAS-Noah model product showed a small but positive impact of the flagging. On the basis of the positive results of this study we will add the flags as a standard attribute to all soil moisture measurements contained in the ISMN.
    Vadose Zone Journal 08/2013; 12(3). DOI:10.2136/vzj2012.0097 · 2.41 Impact Factor
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    ABSTRACT: ESA's Soil Moisture and Ocean Salinity (SMOS) mission has been designed to extend our knowledge of the Earth's water cycle. Soil Moisture and Ocean Salinity records brightness temperatures at the L-band, which over land are sensitive to soil and vegetation parameters. On the basis of these measurements, soil moisture and vegetation opacity data sets have been derived operationally since 2009 for applications comprising hydrology, numerical weather prediction (NWP), and drought monitoring. We present a method to enhance the knowledge about the temporal evolution of radiative transfer parameters. The radiative transfer model L-Band Microwave Emission of the Biosphere (L-MEB) is used within a data assimilation framework to retrieve vegetation opacity and soil surface roughness. To analyze the ability of the data assimilation approach to track the temporal evolution of these parameters, scenario analyses were performed with increasing complexity. First, the HYDRUS-1D code was used to generate soil moisture and soil temperature time series. On the basis of these data, the L-MEB forward model was run to simulate brightness temperature observations. Finally, the coupled model system HYDRUS-1D and L-MEB were integrated into a data assimilation framework using a particle filter, which is able to update L-MEB states as well as L-MEB parameters. Time invariant and time variable radiative transfer parameters were estimated. Moreover, it was possible to estimate a "bias" term when model simulations show a systematic difference as compared to observations. An application to a USDA-NRCS Soil Climate Analysis Network (SCAN) site showed the good performance of the proposed approach under real conditions.
    Vadose Zone Journal 08/2013; 12(3-3). DOI:10.2136/vzj2012.0040 · 2.41 Impact Factor
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    ABSTRACT: Sea ice thickness information is needed for climate modeling and ship operations. Here a method to detect the thickness of sea ice up to 50 cm during the freezeup season based on high incidence angle observations of the Soil Moisture and Ocean Salinity (SMOS) satellite working at 1.4 GHz is suggested. By comparison of thermodynamic ice growth data with SMOS brightness temperatures, a high correlation to intensity and an anti correlation to the difference between vertically and horizontally polarised brightness temperatures at incidence angles between 40 and 50 ° are found and used to develop an empirical retrieval sensitive to thin sea ice up to 50 cm thickness. It shows high correlations with ice thickness data from airborne measurements and reasonable ice thickness patterns for the Arctic freeze up period.
    The Cryosphere Discussions 08/2013; 7(4):4379-4405. DOI:10.5194/tcd-7-4379-2013
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    ABSTRACT: Correct parameterization of radiative transfer models is very important for high accuracy soil moisture retrievals from spaceborne L-band passive microwave sensors, such as the ESA Soil Moisture and Ocean Salinity (SMOS) Mission. In order to investigate the characteristics of radiative transfer parameters such as vegetation opacity and soil surface roughness, a dual state and parameter update data assimilation system has been developed. By assimilating SMOS brightness temperatures into the L-band Microwave Emission of the Biosphere (L-MEB) model, respective parameters can be estimated. The data assimilation system makes use of a temporal smoothing algorithm based on the Sampling Importance Resampling Particle Filter. The new approach, namely the Sampling Importance Resampling Particle Smoother, makes use of a particle weighting function valid not for a single time step, but for a specific time period. The resulting parameters estimated are more stable throughout the whole period under investigation, where the possibility to vary with time is still given. This is important to cover the seasonality of e.g. vegetation parameters.
    IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium; 07/2013
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    ABSTRACT: A new land surface analysis system based on a simplified point-wise Extended Kalman Filter (EKF) was implemented at the European Centre for Medium- Range Weather Forecasts in the global operational Integrated Forecasting System (IFS) in November 2010. This system will allow consistent and optimal analyses of land surface parameters like soil moisture, surface temperatures, snow and vegetation properties. As part of the system implementation, the surface analysis structure has been revised to permit an independent and parallel computation with the upper-air 4D-Var analysis. The new analysis system is used for the soil moisture analysis, replacing the previous Optimal Interpolation (OI) scheme. Similar to the OI system, the simplified EKF uses 2m air temperature and relative humidity observations from the SYNOP (land surface synoptic report) groundbased networks to analyse soil moisture. This paper describes the new land surface analysis, its application for analysing soil moisture, and initial verification results which supported its operational implementation at ECMWF. The performance is evaluated based on a set of one-year analysis experiments. The simplified EKF is compared to the OI, on soil moisture, 2m temperature and relative humidity, showing a consistent improvement on screen-level parameters and soil moisture forecasts. To demonstrate the potential of the new analysis scheme, soil moisture derived from ASCAT (Advanced Scatterometer) has been assimilated through the simplified EKF.
    Quarterly Journal of the Royal Meteorological Society 07/2013; 139(674). DOI:10.1002/qj.2023 · 5.13 Impact Factor

Publication Stats

1k Citations
123.20 Total Impact Points

Institutions

  • 2003–2013
    • European Center For Medium Range Weather Forecasts
      Shinfield, England, United Kingdom
  • 2012
    • European Space Agency
      Lutetia Parisorum, Île-de-France, France
  • 2004
    • University of Bonn
      Bonn, North Rhine-Westphalia, Germany
  • 1999–2000
    • Princeton University
      • • Department of Civil and Environmental Engineering
      • • Department of Operations Research and Financial Engineering
      Princeton, NJ, United States