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Gao Q.,CNRS Center for the Study of the Biosphere from Space | Gao Q.,Ramon Llull University | Zribi M.,CNRS Center for the Study of the Biosphere from Space | Escorihuela M.J.,IsardSAT | Baghdadi N.,IRSTEA
Sensors (Switzerland) | Year: 2017

The recent deployment of ESA’s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015–November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.

Escorihuela M.J.,isardSAT | Chanzy A.,French National Institute for Agricultural Research | Wigneron J.P.,French National Institute for Agricultural Research | Kerr Y.H.,CNRS Center for the Study of the Biosphere from Space
Remote Sensing of Environment | Year: 2010

The aim of this study is to analyze the influence of the soil moisture sampling depth in the parameterization of soil emission in microwave radiometry at L-band. The analysis is based on brightness temperature, soil moisture and temperature measurements acquired over a bare soil during the SMOSREX experiment. A more detailed profile of surface soil moisture was obtained with a soil heat and water flows mechanistic model. It was found that (1) the soil moisture sampling depth depends on soil moisture conditions, (2) the effective soil moisture sampling depth is shallower than provided by widely used field moisture sensors, and (3) the soil moisture sampling depth has an impact on the calibration of soil roughness model parameters. These conclusions are crucial for the calibration and validation of remote sensing data at L-band. © 2010 Elsevier Inc. All rights reserved.

Fornari M.,European Space Agency | Scagliola M.,ARESYS SRL | Tagliani N.,ARESYS SRL | Parrinello T.,Earth Observation Directorate | Mondejar A.G.,Isardsat
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2014

CryoSat's Synthetic Interferometric Radar Altimeter (SIRAL) [1] is a Ku-band pulsewidth limited radar altimeter that transmits pulses at a high pulse repetition frequency thus making the received echoes phase coherent and suitable for De-lay/Doppler processing [2]. Moreover SIRAL takes advantage of two antennas mounted across-track for interferometric capability, in order to determine the across-track direction from which the echo is received [3]. The calibration strategy for SIRAL includes both internal calibrations and external calibration [1]. Due to the fact that SIRAL is an interferometric phase coherent pulse-width limited radar altimeter, a proper calibration approach has been developed. In this paper we will describe as first the internal calibration strategy and then the different calibration corrections that are applied to science data. The internal calibration results over more than three years of mission will be presented, analysing their temporal evolution in order to highlight the stability of the instrument over its life. Finally, the external calibration measurements for SIRAL will be presented. © 2014 IEEE.

Kerr Y.H.,CNRS Center for the Study of the Biosphere from Space | Waldteufel P.,IPSL LATMOS | Wigneron J.-P.,French National Institute for Agricultural Research | Delwart S.,European Space Agency | And 11 more authors.
Proceedings of the IEEE | Year: 2010

It is now well understood that data on soil moisture and sea surface salinity (SSS) are required to improve meteorological and climate predictions. These two quantities are not yet available globally or with adequate temporal or spatial sampling. It is recognized that a spaceborne L-band radiometer with a suitable antenna is the most promising way of fulfilling this gap. With these scientific objectives and technical solution at the heart of a proposed mission concept the European Space Agency (ESA) selected the Soil Moisture and Ocean Salinity (SMOS) mission as its second Earth Explorer Opportunity Mission. The development of the SMOS mission was led by ESA in collaboration with the Centre National d'Etudes Spatiales (CNES) in France and the Centro para el Desarrollo Tecnologico Industrial (CDTI) in Spain. SMOS carries a single payload, an L-Band 2-D interferometric radiometer operating in the 14001427-MHz protected band. The instrument receives the radiation emitted from Earth's surface, which can then be related to the moisture content in the first few centimeters of soil over land, and to salinity in the surface waters of the oceans. SMOS will achieve an unprecedented maximum spatial resolution of 50 km at L-band over land (43 km on average over the field of view), providing multiangular dual polarized (or fully polarized) brightness temperatures over the globe. SMOS has a revisit time of less than 3 days so as to retrieve soil moisture and ocean salinity data, meeting the mission's science objectives. The caveat in relation to its sampling requirements is that SMOS will have a somewhat reduced sensitivity when compared to conventional radiometers. The SMOS satellite was launched successfully on November 2, 2009. © 2006 IEEE.

Escorihuela M.J.,isardSAT | Quintana-Segui P.,Ramon Llull University
Remote Sensing of Environment | Year: 2016

This paper presents the comparison of three global soil moisture products (ASCAT, AMSR and SMOS) versus a land surface model over a region representative of several Mediterranean landscapes located in the Northeast of the Iberian Peninsula. Our approach has been for agricultural and water management applications at the regional and local scale. Despite being a rather small area, we were able to observe different signal behaviours corresponding to major land cover classes in Mediterranean areas i.e.: dryland and irrigated crops, forests and natural vegetation (grass-shrubs). The area also allowed assessing the impact of topography. The first result of the study is that the results are very dependent on the normalizations used to make the data comparable, thus their impact must be carefully analysed. In this study, we applied two different normalisation methods (called ZV35 and ZV) and different moving average windows (1, 10 and 30. days) in order to enhance seasonal effects. Using no smoothing window, ASCAT is the soil moisture product that correlates best with the LSM over all cover classes, whatever the method. Using smoothing window, AMSR-E tends to outperform other soil moisture products with the ZV method. The ZV35 method is not able to identify a small heavily irrigated area. The reason for these different results is that ZV35, tends to eliminate the monthly scale soil moisture memory and therefore becomes more sensitive to precipitation and less sensitive to the monthly evolution of superficial soil moisture. The comparison shows in general good agreement for all soil moisture products with the LSM on the temporal series simulated over flat, non irrigated areas which are not close to the sea. SMOS has difficulties in areas close to the sea and in areas with steep relief and the current version of the L2 Operational Algorithm (V5.51) depicts few values in forested areas. ASCAT, in its turn, shows some limitations over agricultural and natural vegetation where it shows an increase of soil moisture from June to October probably due to increase of penetration depth in dry soil moisture conditions. AMSR-E LPRM shows a clear vegetation cycle over all the land cover classes. From all the remote sensing products, SMOS is the only one able to see irrigation and the only that does not show clear vegetation or roughness effects. In this study, we were able to assess the impact of higher resolution soil moisture products to map irrigated areas. © 2016.

Merlin O.,CNRS Center for the Study of the Biosphere from Space | Escorihuela M.J.,IsardSAT | Mayoral M.A.,Applus Inc. | Hagolle O.,CNRS Center for the Study of the Biosphere from Space | And 2 more authors.
Remote Sensing of Environment | Year: 2013

A disaggregation algorithm is applied to 40. km resolution SMOS (Soil Moisture and Ocean Salinity) surface soil moisture using 1. km resolution MODIS (MODerature resolution Imaging Spectroradiometer), 90. m resolution ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer), and 60. m resolution Landsat-7 data. DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) distributes high-resolution soil moisture around the low-resolution observed mean value using the instantaneous spatial link between optical-derived soil evaporative efficiency (ratio of actual to potential evaporation) and near-surface soil moisture. The objective is three-fold: (i) evaluating DISPATCH at a range of spatial resolutions using readily available multi-sensor thermal data, (ii) deriving a robust calibration procedure solely based on remotely sensed data, and (iii) testing the linear or nonlinear behavior of soil evaporative efficiency. Disaggregated soil moisture is compared with the 0-5. cm in situ measurements collected each month from April to October 2011 in a 20. km square spanning an irrigated and dry land area in Catalunya, Spain. The target downscaling resolution is set to 3. km using MODIS data and to 100. m using ASTER and Landsat data. When comparing 40. km SMOS, 3. km disaggregated and 100. m disaggregated data with the in situ measurements aggregated at corresponding resolution, results indicate that DISPATCH improves the spatio-temporal correlation with in situ measurements at both 3. km and 100. m resolutions. A yearly calibration of DISPATCH is more efficient than a daily calibration. Assuming a linear soil evaporative efficiency model is adequate at kilometric resolution. At 100. m resolution, the very high spatial variability in the irrigated area makes the linear approximation poorer. By accounting for non-linearity effects, the slope of the linear regression between disaggregated and in situ measurements is increased from 0.2 to 0.5. Such a multi-sensor remote sensing approach has potential for operational multi-resolution monitoring of surface soil moisture and is likely to help parameterize soil evaporation at integrated spatial scales. © 2012 Elsevier Inc.

Wigneron J.-P.,French National Institute for Agricultural Research | Chanzy A.,French National Institute for Agricultural Research | Kerr Y.H.,CNRS Center for the Study of the Biosphere from Space | Lawrence H.,French National Institute for Agricultural Research | And 8 more authors.
IEEE Transactions on Geoscience and Remote Sensing | Year: 2011

In the forward model [L-band microwave emission of the biosphere (L-MEB)] used in the Soil Moisture and Ocean Salinity level-2 retrieval algorithm, modeling of the roughness effects is based on a simple semiempirical approach using three main "roughness" model parameters: HR, Q R, and NR. In many studies, the two parameters Q R and NR are set to zero. However, recent results in the literature showed that this is too approximate to accurately simulate the microwave emission of the rough soil surfaces at L-band. To investigate this, a reanalysis of the PORTOS-93 data set was carried out in this paper, considering a large range of roughness conditions. First, the results confirmed that Q R could be set to zero. Second, a refinement of the L-MEB soil model, considering values of NR for both polarizations (namely, N RV and NRH), improved the model accuracy. Furthermore, simple calibrations relating the retrieved values of the roughness model parameters HR and (NRH - NRV) to the standard deviation of the surface height were developed. This new calibration of L-MEB provided a good accuracy (better than 5 K) over a large range of soil roughness and moisture conditions of the PORTOS-93 data set. Conversely, the calibrations of the roughness effects based on the Choudhury approach, which is still widely used, provided unrealistic values of surface emissivities for medium or large roughness conditions. © 2006 IEEE.

Stefan V.G.,CNRS Center for the Study of the Biosphere from Space | Merlin O.,CNRS Center for the Study of the Biosphere from Space | Merlin O.,Cadi Ayyad University | Er-Raki S.,Cadi Ayyad University | And 2 more authors.
Remote Sensing | Year: 2015

Due to their image-based nature, "contextual" approaches are very attractive to estimate evapotranspiration (ET) from remotely-sensed land surface temperature (LST) data. Their application is however limited to highly heterogeneous areas where the soil and vegetation temperature endmembers (Tends) can be observed at the thermal sensor resolution. This paper aims to develop a simple theoretical approach to estimate Tends independently from LST images. Soil Tends are simulated by a soil energy balance model forced by meteorological data. Vegetation Tends are obtained from soil Tends and air temperature. Model-derived soil Tends are first evaluated with in situ measurements made over an irrigated area in Morocco. The root mean square difference (RMSD) between modeled and ground-based soil Tends is estimated as 2.4 °C. Model-derived soil Tends are next compared with the soil Tends retrieved from 90-m resolution ASTER (AdvancedSpaceborne Thermal Emission and Reflection Radiometer) data collected over two irrigated areas in Mexico and Spain. Such a comparison reveals a strong consistency between model-derived and high-resolution image-based soil Tends. A recent contextual ET model (SEB-1S) is then applied to 90-m resolution and to 1-km resolution (aggregated) ASTER data using the model-derived or image-based Tends as the input. The RMSD between 90-m resolution SEB-1S and in situ ET is estimated as 65 and 82 W·m-2, and the RMSD between 1-km resolution SEB-1S and aggregated SEB-1S ET is estimated as 78 and 56 W·m-2 for the image-based and model-derived Tends, respectively. In light of the above results, Tends should be estimated a priori when contextual models are applied to low resolution images. Moreover, the consistency over highly heterogeneous areas between model-derived and high-resolution image-based Tends provides a meaningful basis for developing mixed modeling observational approaches. © 2015 by the authors; licensee MDPI, Basel, Switzerland.

Ray C.,Saint Mary's College of California | Martin-Puig C.,IsardSAT | Clarizia M.P.,University of Michigan | Ruffini G.,Starlab Barcelona SL | And 4 more authors.
IEEE Transactions on Geoscience and Remote Sensing | Year: 2015

The backscatters power single-look waveform recorded by a synthetic aperture radar altimeter is approximated in a closed-form model. The model, being expressed in terms of parameterless functions, allows for efficient computation of the waveform and a clear understanding of how the various sea state and instrument parameters affect the waveform. © 2014 IEEE.

The inflow of Atlantic Water (AW) into the Nordic Seas is of fundamental importance for the Arctic, European and global climate. The West Spitsbergen Current (WSC) is a main source of heat for the Arctic, greatly affecting its sea ice conditions and air temperatures. The aim of this study is to analyse the vertical structure of the current and reconstruct it along its path in 2000-2013 using a synergy of data: i) in situ measurements collected along the WSC by the Institute Oceanology of Polish Academy of Sciences (IOPAS) regularly every July in 2000-2013, ii) satellite altimetry, and iii) an information about the WSC vertical structure simulated in the NEMO (1/12°) numerical model in the same time period. The preliminary results are presented based on the in situ mooring measurements and geostrophic currents calculated using satellite altimetry.

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