CNRS Center for the Study of the Biosphere from Space
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Peng J.,Max Planck Institute for Meteorology | Loew A.,Ludwig Maximilians University of Munich | Merlin O.,CNRS Center for the Study of the Biosphere from Space | Verhoest N.E.C.,Ghent University
Reviews of Geophysics | Year: 2017

Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed. ©2017. American Geophysical Union.

Zribi M.,CNRS Center for the Study of the Biosphere from Space
2016 International Symposium on Signal, Image, Video and Communications, ISIVC 2016 | Year: 2017

Physical soil and vegetation characteristics play an essential role in the functioning of continental water and carbon cycles [1,2]. Over the last three decades, various different remote sensing techniques based on the use of microwave backscattering have demonstrated considerable potential for the estimation of soil and vegetation parameters, and several instrumental studies, making use of theoretical and experimental inversion techniques, have been developed for the retrieval of land surface parameters. Despite these significant scientific developments, the operational use of microwave remote sensing has been hampered, in particular as a consequence of the limitations from which it suffers in terms of coverage and/or resolution. Three techniques are presented in this paper: radar, passive microwave and GNSS-R remote sensing. A discussion of the historical development of these techniques, and a description of some of their applications, are included. © 2016 IEEE.

Garestier F.,University of Caen Lower Normandy | Le Toan T.,CNRS Center for the Study of the Biosphere from Space
IEEE Transactions on Geoscience and Remote Sensing | Year: 2010

The vertical backscatter profile of a pine forest constituted by stands of different height is inverted from a single baseline P-band Pol-InSAR data in order to identify scatterers in the canopy. The proposed approach uses the Gaussian vertical backscatter profile model, which associates an interferometric coherence expression to a vertical scatterers' distribution characterized by relative standard deviation and elevation. The methodology, which uses in situ measurements of forest height and unbiased ground level estimation, is applied to HV and VV channels, providing accuracy given sufficiently low ground-to-canopy power ratios. Inverted backscatter profiles show maximum power converging toward the basis of the tree crown on highest forests, where the largest branches are located, indicating the high sensitivity of P-band measurements to the forest structure and to the vertical biomass distribution. Over lower stands with larger tree densities, the power peak is located in the upper part of the canopy, which can be explained by a stronger attenuation in the canopy. © 2006 IEEE.

Garestier F.,University of Caen Lower Normandy | Toan T.L.,CNRS Center for the Study of the Biosphere from Space
IEEE Transactions on Geoscience and Remote Sensing | Year: 2010

The Random Volume over Ground (RVoG) model has been extensively applied to polarimetric synthetic aperture radar interferometry (Pol-InSAR) data for forest height inversion. The model assumes forest as a homogeneous volume of randomly oriented particles characterized by a constant extinction but does not take into account the forest vertical heterogeneity, to which interferometric coherence is sensitive. In order to integrate vertical heterogeneity in forest models, two complementary models, which take into consideration the forest natural structure, are investigated through analysis of volume interferometric coherence. The first model assumes a vertically varying extinction in the volume layer, and the second model considers predominant contributions localized in a finite height interval, modeled as a Gaussiandistributed backscatter. The two forest models are compared with constant extinction RVoG in the coherence and interferometric phase aspects. Finally, the contribution of these new models for forest height inversion using the Pol-InSAR technique is discussed in the context of a two-layer ground + canopy medium. © 2009 IEEE.

Petitjean F.,Computer science and Remote Sensing Laboratory LSIIT | Inglada J.,CNRS Center for the Study of the Biosphere from Space | Gancarski P.,Computer science and Remote Sensing Laboratory LSIIT
IEEE Transactions on Geoscience and Remote Sensing | Year: 2012

Satellite Image Time Series are becoming increasingly available and will continue to do so in the coming years thanks to the launch of space missions which aim at providing a coverage of the Earth every few days with high spatial resolution. In the case of optical imagery, it will be possible to produce land use and cover change maps with detailed nomenclatures. However, due to meteorological phenomena, such as clouds, these time series will become irregular in terms of temporal sampling, and one will need to compare time series with different lengths. In this paper, we present an approach to image time series analysis which is able to deal with irregularly sampled series and which also allows the comparison of pairs of time series where each element of the pair has a different number of samples. We present the dynamic time warping from a theoretical point of view and illustrate its capabilities with two applications to real-time series. © 2012 IEEE.

Bouvet A.,CNRS Center for the Study of the Biosphere from Space | Le Toan T.,CNRS Center for the Study of the Biosphere from Space
Remote Sensing of Environment | Year: 2011

Because of the importance of rice for the global food security and because of the role of inundated paddy fields in greenhouse gases emissions, monitoring the rice production world-wide has become a challenging issue for the coming years. Local rice mapping methods have been developed previously in many studies by using the temporal change of the backscatter from C-band synthetic aperture radar (SAR) co-polarized data. The studies indicated in particular the need of a high observation frequency. In the past, the operational use of these methods has been limited by the small coverage and the poor acquisition frequency of the available data (ERS-1/2, Radarsat-1). In this paper, the method is adapted for the first time to map rice at large scale, by using wide-swath images of the Advanced SAR (ASAR) instrument onboard ENVISAT. To increase the observation frequency, data from different satellite tracks are combined. The detection of rice fields is achieved by exploiting the high backscatter increase at the beginning of the growing cycle, which allows the production of rice maps early in the season (in the first 50 days). The method is tested in the Mekong delta in Vietnam. The mapping results are compared to existing rice maps in the An Giang province, with a good agreement (higher than 81%). The rice planted areas are retrieved from the maps and successfully validated with the official statistics available at each province (R2=0.92). These results show that the method is useful for large scale early mapping of rice areas, using current and future C band wide-swath SAR data. © 2011 Elsevier Inc.

Leroux D.J.,CNRS Center for the Study of the Biosphere from Space | Kerr Y.H.,CNRS Center for the Study of the Biosphere from Space | Richaume P.,CNRS Center for the Study of the Biosphere from Space | Fieuzal R.,CNRS Center for the Study of the Biosphere from Space
Remote Sensing of Environment | Year: 2013

SMOS (Soil Moisture and Ocean Salinity) data have now been available for over two years and, as part of the validation process, comparing this new dataset to already existing global datasets of soil moisture is possible. In this study, SMOS soil moisture product was evaluated globally by using the triple collocation method. This statistical method is based on the comparison of three datasets and produces global error maps by statistically inter-comparing their variations. Only the variable part of the errors are considered here, the bias errors are not treated by triple collocation. This method was applied to the following datasets: SMOS Level 2 product, two soil moisture products derived from AMSR-E (Advanced Microwave Scanning Radiometer)-LPRM (Land Parameter Retrieval Model) and NSIDC (National Snow and Ice Data Center), ASCAT (Advanced Scatterometer) and ECMWF (European Center for Medium range Weather Forecasting). The resulting errors are not absolute since they depend on the choice of the datasets. However this study showed that the spatial structure of the SMOS was independent of the combination and pointed out the same areas where SMOS performed well and where it did not. This global SMOS error map was then linked to other global parameters such as soil texture, RFI (Radio Frequency Interference) occurrence probabilities and land cover in order to identify their influences in the SMOS error. Globally the presence of forest in the field of view of the radiometer seemed to have the greatest influence on SMOS error (56.8%) whereas RFI represented 1.7% according to the analysis of variance from a multiple linear regression model. These percentages were not identical for all the continents and some discrepancies in the proportion of the influence were highlighted: soil texture was the main influence over Europe whereas RFI had the largest influence over Asia. © 2013 Elsevier Inc.

Grau E.,CNRS Center for the Study of the Biosphere from Space | Gastellu-Etchegorry J.-P.,CNRS Center for the Study of the Biosphere from Space
Remote Sensing of Environment | Year: 2013

The atmosphere strongly affects satellite measurements of Earth surfaces in the optical domain. Modeling this influence is complex. This is typically the case of the "Earth-Atmosphere" radiative coupling in the presence of Earth surfaces with spatially variable optical properties. In that case, it may be very difficult to couple Earth and cloud-free atmosphere radiative transfer models. This explains why an atmosphere module was input into the Earth radiative transfer (R.T.) model DART (Discrete Anisotropic Radiative Transfer) in order to simulate accurately satellite images of natural and urban Earth surfaces. This paper presents how DART simulates the atmosphere R.T. in the short wave and thermal infrared domains. The atmosphere is divided into 3 zones: bottom atmosphere (BA), mid atmosphere (MA) and high atmosphere (HA). The 3D distribution is arbitrary in BA and horizontally constant with any vertical distribution in MA and HA. The "Earth-Atmosphere" R.T. is modeled in 5 stages. 1) Atmosphere R.T. (i.e., atmosphere thermal emission and/or sun radiation scattering). 2) Earth surface R.T. (i.e., Earth thermal emission and/or atmosphere and direct sun radiation scattering). 3) Atmosphere R.T. (i.e., Earth radiation scattering). 4) Earth surface R.T. (i.e., scattering of downward atmosphere radiation). 5) Simulation of satellite reflectance and/or brightness temperature images. The approach takes into account the earth curvature and the atmosphere non-Beer law behavior in the presence of strongly varying spectral properties. It uses optimally located scattering points for improving atmosphere R.T. accuracy, and it reduces computer time through the use of pre-computed transfer functions that transfer radiation between the different atmosphere levels (BA, MA, HA). Moreover, it can simulate automatically an atmosphere geometry that optimizes the trade-off "Computer time-Accuracy" of simulations. The robustness and accuracy of the DART atmosphere modeling were successfully validated with theoretical cases and with the MODTRAN atmosphere R.T. model. © 2013 Elsevier Inc.

The space defined by the pair surface temperature (T) and surface albedo (α), and the space defined by the pair T and fractional green vegetation cover (fvg) have been extensively used to estimate evaporative fraction (EF) from solar/thermal remote sensing data. In both space-based approaches, evapotranspiration (ET) is estimated as remotely sensed EF times the available energy. For a given data point in the T-α space or in the T-fvg space, EF is derived as the ratio of the distance separating the point from the line identified as the dry edge to the distance separating the dry edge and the line identified as the wet edge. The dry and wet edges are classically defined as the upper and lower limit of the spaces, respectively. When investigating side by side the T-α and the T-fvg spaces, one observes that the range covered by T values on the (classically determined) wet edge is different for both spaces. In addition, when extending the wet and dry lines of the T- α space, both lines cross at α ≈ 0.4 although the wet and dry edges of the T-fvg space never cross for 0 ≤ fvg < 1. In this paper, a new ET (EF) model (SEB-1S) is derived by revisiting the classical physical interpretation of the T-α space to make its wet edge consistent with that of the T-fvg space. SEB-1S is tested over a 16 km by 10 km irrigated area in northwestern Mexico during the 2007-2008 agricultural season. The classical T-α space-based model is implemented as benchmark to evaluate the performance of SEB-1S. Input data are composed of ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) thermal infrared, Formosat-2 shortwave, and station-based meteorological data. The fluxes simulated by SEB-1S and the classical T-α space-based model are compared on seven ASTER overpass dates with the in situ measurements collected at six locations within the study domain. The ET simulated by SEB-1S is significantly more accurate and robust than that predicted by the classical T-α space-based model. The correlation coefficient and slope of the linear regression between simulated and observed ET is improved from 0.82 to 0.93, and from 0.63 to 0.90, respectively. Moreover, constraining the wet edge using air temperature data improves the slope of the linear regression between simulated and observed ET. © 2013 Author(s).

Hyvernat P.,CNRS Center for the Study of the Biosphere from Space
Mathematical Structures in Computer Science | Year: 2014

We present a categorical model for intuitionistic linear logic in which objects are polynomial diagrams and morphisms are simulation diagrams. The multiplicative structure (tensor product and its adjoint) can be defined in any locally cartesian closed category, but the additive (product and coproduct) and exponential ( -comonoid comonad) structures require additional properties and are only developed in the category Set, where the objects and morphisms have natural interpretations in terms of games, simulation and strategies. Copyright © Cambridge University Press 2013.

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