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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.

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.

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.

Ray C.,Saint Marys 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.

Ablain M.,Collecte Localisation Satellite CLS | Cazenave A.,CNRS Geophysical Research and Oceanographic Laboratory | Larnicol G.,Collecte Localisation Satellite CLS | Balmaseda M.,ECMWF | And 16 more authors.
Ocean Science | Year: 2015

Sea level is one of the 50 Essential Climate Variables (ECVs) listed by the Global Climate Observing System (GCOS) in climate change monitoring. In the past two decades, sea level has been routinely measured from space using satellite altimetry techniques. In order to address a number of important scientific questions such as "Is sea level rise accelerating?", "Can we close the sea level budget?", "What are the causes of the regional and interannual variability?", "Can we already detect the anthropogenic forcing signature and separate it from the internal/natural climate variability?", and "What are the coastal impacts of sea level rise?", the accuracy of altimetry-based sea level records at global and regional scales needs to be significantly improved. For example, the global mean and regional sea level trend uncertainty should become better than 0.3 and 0.5 mm yearg'1, respectively (currently 0.6 and 1-2 mm yearg'1). Similarly, interannual global mean sea level variations (currently uncertain to 2-3 mm) need to be monitored with better accuracy. In this paper, we present various data improvements achieved within the European Space Agency (ESA) Climate Change Initiative (ESA CCI) project on "Sea Level" during its first phase (2010-2013), using multi-mission satellite altimetry data over the 1993-2010 time span. In a first step, using a new processing system with dedicated algorithms and adapted data processing strategies, an improved set of sea level products has been produced. The main improvements include: reduction of orbit errors and wet/dry atmospheric correction errors, reduction of instrumental drifts and bias, intercalibration biases, intercalibration between missions and combination of the different sea level data sets, and an improvement of the reference mean sea surface. We also present preliminary independent validations of the SL-cci products, based on tide gauges comparison and a sea level budget closure approach, as well as comparisons with ocean reanalyses and climate model outputs. © 2015 Author(s).

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