Analysis of RFI Identification and Mitigation in CAROLS Radiometer Data Using a Hardware Spectrum Analyser

A method to identify and mitigate radio frequency interference (RFI) in microwave radiometry based on the use of a spectrum analyzer has been developed. This method has been tested with CAROLS L-band airborne radiometer data that are strongly corrupted by RFI. RFI is a major limiting factor in passive microwave remote sensing interpretation. Although the 1.400–1.427 GHz bandwidth is protected, RFI sources close to these frequencies are still capable of corrupting radiometric measurements. In order to reduce the detrimental effects of RFI on brightness temperature measurements, a new spectrum analyzer has been added to the CAROLS radiometer system. A post processing algorithm is proposed, based on selective filters within the useful bandwidth divided into sub-bands. Two discriminant analyses based on the computation of kurtosis and Euclidian distances have been compared evaluated and validated in order to accurately separate the RF interference from natural signals

Contribution à la méthodologie d'estimation de l'humidité du sol à partir de données micro-ondes passives en bande L

One of the aims of the future SMOS mission (Soil Moisture and Ocean Salinity) is to monitor the near-surface soil moisture over the continents. The new technology (aperture synthesis) allows us to measure the surface microwave emission at 1.41 GHz for different view angles and for the two polarizations simultaneously. This emission depends on the water present in the 3 to 5 first centimeters of the soil.However, the presence of vegetation attenuates the soil emission and adds its own contribution ; it is thus necessary to separate the two contributions to the surface emission in the view of estimating soil moisture.The microwave emission of a vegetation cover is described by the radiative transfer model tau-omega. The inversion of this model, based on a non linear regression, is used to estimate unknown parameters : the soil moisture and the vegetation microwave parameters (optical thickness and single scattering albedo).This study is based on different data sets acquired over different crop types : corn, wheat, alfalfa, grass, soybean and sorghum. First, we evaluated variations of the vegetation microwave parameters with time, view angle and polarization, for each crop type. This study allows us to establish the hypothesis which could simplify the model in order to improve the soil moisture estimation. Then, we tested the inversion of the tau-omega model with all the data sets simultaneously. Different inversion configurations were compared in the view of choosing the best soil moisture retrieval process, depending on the a priori information. We showed that it is possible to estimate soil moisture with a good accuracy (RMSE= 0.047 m3/m3) using few ancillary information on the soil and vegetation types.Un des objectifs de la future mission SMOS (Soil Moisture and Ocean Salinity) est de cartographier l'humidité de la surface du sol sur l'ensemble des continents. Les technologies récentes (interférométrie par synthèse d'ouverture) permettent de mesurer l'émission micro-onde de la surface à 1.41 GHz, pour différents angles de visée simultanément et pour les deux polarisations ; cette émission dépend principalement de la quantité d'eau présente dans les 3 à 5 premiers centimètres du sol. Cependant, la présence de végétation atténue l'émission du sol et ajoute sa propre contribution, il est donc nécessaire de séparer ces deux contributions afin d'estimer l'humidité de la surface. L'émission d'un couvert végétal dans les micro-ondes est estimée par le modèle de transfert radiatif tau-omega ; de plus, l'inversion de ce modèle par régression non linéaire permet d'estimer les variables inconnues du modèle : humidité et paramètres de végétation (épaisseur optique et albédo de simple diffusion). L'objectif de cette thèse est d'améliorer les méthodes d'inversion du modèle en utilisant la configuration originale de visée de SMOS : observations multi-angulaires et en bi-polarisation. Cette étude est basée sur des campagnes expérimentales sur différentes cultures : maïs, blé, luzerne, herbe, soja et sorgho. Dans un premier temps, nous avons évalué les paramètres micro-ondes de végétation en fonction du temps, de l'angle et de la polarisation, pour chacun de ces couverts. Cette étude nous permet de poser les hypothèses simplificatrices qui permettront d'estimer l'humidité du sol. Ensuite, nous avons testé l'inversion du modèle tau-omega sur l'ensemble des couverts simultanément. Différentes configurations d'inversion ont été comparées afin de prescrire une stratégie adéquate pour estimer l'humidité sous un couvert agricole quelconque selon les informations a priori disponibles. Nous montrons ainsi qu'il est possible d'estimer l'humidité du sol avec une précision relativement bonne (RMSE= 0.047 m3/m3) avec peu d'informations sur le type de sol et sur le type de couvert

Analysis of RFI identification and mitigation in CAROLS radiometer data using a hardware spectral analyser

International audienceA method based on the use of a spectral analyzer has been developed in order to identify and mitigate radio frequency interference (RFI) in microwave radiometry. This method has been tested with L-band CAROLS airborne data highly corrupted by interferences. RFI is a major limiting factor in passive microwave remote sensing. Although the 1.4-1.427 GHz bandwidth is protected, RFI sources close to these frequencies may still corrupt radiometer measurements. In order to reduce RFI bad effects on the brightness temperature measurements, a new instrument called spectral analyzer has been added to the CAROLS radiometer system. A post processing algorithm based on a selective filtering with the division of bandwidth in subbands is proposed. Two discriminant analysis based on the computation of kurtosis and Mahalanobis distance have been compared, evaluated and validated in order to separate accurately the RF interference with natural signal

Analysis of RFI Issue Using the CAROLS L-Band Experiment

International audienceIn this paper, different methods are proposed for the detection and mitigation of the undesirable effects of radio-frequency interference (RFI) in microwave radiometry. The first of these makes use of kurtosis to detect the presence of non-Gaussian signals, whereas the second imposes a threshold on the standard deviation of brightness temperatures in order to distinguish natural-emission variations from RFI. Finally, the third approach is based on the use of a threshold applied to the third and fourth Stokes parameters. All these methods have been applied and tested, with the cooperative airborne radiometer for ocean and land studies radiometer operating in the L-band, on the data acquired during airborne campaigns made in the spring of 2009 over the southwest of France. The performance of each approach, or of two combined approaches, is analyzed with our database. We thus show that the kurtosis method is well suited to detect pulsed RFI, whereas the method based on the second moment of brightness temperatures seems to be better suited to detect continuous-wave RFI in airborne brightness-temperature measurements

Sea Surface Salinity Retrieval from CAROLS L-Band Measurements

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Interpretation of CAROLS L-Band measurements in the Gulf of Biscaye

International audienceThe L-band Cooperative Airborne Radiometer for Ocean and Land Studies (CAROLS) radiometer (Zribi et al., IGARSS 2008) flew seven times over the Gulf of Biscay between May 04 and May 26, 2009 around 20UTC. These flights followed two airborne campaigns with the same instrument in September 2007 and November 2008. Brightness temperatures (Tb) of the surface were measured by one antenna looking at 33° on the right hand side of the aircraft and optionally by a nadir antenna. The nadir antenna is sometimes exchanged with the radar scatterometer STORM in order to get a measurement of surface roughness, related to wind, coincident in space and time with the radiometric measurement. Measurements are compared with simulations conducted with the Terrestrial Radiometry Analysis Package (TRAP) (Tenerelli et al., 2008) software run for CAROLS geometry and different observed geophysical conditions. Concomitant ship campaign and drifter deployments provide in situ ground truths for sea surface salinity and temperature. Wind speed and direction are either estimated from the QSCAT scatterometer or from the STORM scatterometer and complemented with in situ observation TRAP uses the physical modeling of atmospheric radiative transfer, sea surface emissivity and galactic glint foreseen for the processing of the Soil Moisture and Ocean Salinity satellite data. The circle flights and wing-wags movements of the CAROLS aircraft (The French research ATR42 aircraft) allow to explore a wide range of incidence angles (from 0° to about 60°) and of galactic signals reflected by the sea surface. Previous flights in 2008 and 2009 have demonstrated that on a whole, simulated and observed variations of Tb with incidence angle are very consistent, demonstrating a good sensitivity of CAROLS instrument. In this presentation we will focus on the correlation between L-band radiometer measurements and scatterometer measurements, especially in a case of strong spatial gradient of wind speed (1 to 10m/s) observed in 2007 and on the signature of coastal salinity gradients on the L-band radiometer observed in 2009 during which we observed SSS variations between roughly 32 and 35.6 pss. New CAROLS flights in the Gulf of Biscay are foreseen in April and May 2010 under the track of the SMOS satellite

Soil Moisture Estimation and Analysis in Western Africa Based on ERS Scatterometer

International audienceThe present paper presents a new methodology for the estimation of surface soil moisture over Western Africa, based on data provided by the European Remote sensing Wind SCatterometer (WSC) instrument, in which an empirical model is used to estimate volumetric soil moisture. This approach takes into account the effects of vegetation and soil roughness in the soil moisture estimation process. The proposed estimations have been validated using different methods, and a good degree of coherence has been observed between satellite estimations and ground truth measurements. Comparison with the multi-model analysis product provided by the Global Soil Wetness Project, Phase 2 (GSWP-2) indicates that their estimations are well correlated

Soil Moisture Estimation and Analysis in Western Africa Based on ERS Scatterometer

International audienceThe present paper presents a new methodology for the estimation of surface soil moisture over Western Africa, based on data provided by the European Remote sensing Wind SCatterometer (WSC) instrument, in which an empirical model is used to estimate volumetric soil moisture. This approach takes into account the effects of vegetation and soil roughness in the soil moisture estimation process. The proposed estimations have been validated using different methods, and a good degree of coherence has been observed between satellite estimations and ground truth measurements. Comparison with the multi-model analysis product provided by the Global Soil Wetness Project, Phase 2 (GSWP-2) indicates that their estimations are well correlated

Soil Moisture Estimation and Analysis in Western Africa Based on ERS Scatterometer

International audienceThe present paper presents a new methodology for the estimation of surface soil moisture over Western Africa, based on data provided by the European Remote sensing Wind SCatterometer (WSC) instrument, in which an empirical model is used to estimate volumetric soil moisture. This approach takes into account the effects of vegetation and soil roughness in the soil moisture estimation process. The proposed estimations have been validated using different methods, and a good degree of coherence has been observed between satellite estimations and ground truth measurements. Comparison with the multi-model analysis product provided by the Global Soil Wetness Project, Phase 2 (GSWP-2) indicates that their estimations are well correlated

Spatial and temporal variability of biophysical variables in Southwestern France from airborne L-band radiometry

In 2009 and 2010 the L-band microwave Cooperative Airborne Radiometer for Ocean and Land Studies (CAROLS) campaign was performed in Southwestern France to support the calibration and validation of the new Soil Moisture and Ocean Salinity (SMOS) satellite mission. The L-band Microwave Emission of the Biosphere (L-MEB) model was used to retrieve Surface Soil Moisture (SSM) and the Vegetation Optical Depth (VOD) from the CAROLS brightness temperature measurements. The CAROLS SSM was compared with in situ observations at 11 sites of the SMOSMANIA (Soil Moisture Observing System-Meteorological Automatic Network Integrated Application) network of Météo-France. For eight of them, significant correlations were observed (0.51 ≤ r ≤ 0.82), with standard deviation of differences ranging from 0.039 m3 m−3 to 0.141 m3 m−3. Also, the CAROLS SSM was compared with SSM values simulated by the A-gs version of the Interactions between Soil, Biosphere and Atmosphere (ISBA-A-gs) model along twenty flight lines, at a resolution of 8 km × 8 km. A significant spatial correlation between these two datasets was observed for all the flights (0.36 ≤ r ≤ 0.85). The CAROLS VOD presented significant spatial correlations with the vegetation water content (VWC) derived from the spatial distribution of vegetation types used in ISBA-A-gs and from the Leaf Area Index (LAI) simulated for low vegetation. On the other hand, the CAROLS VOD presented little temporal changes, and no temporal correlation was observed with the simulated LAI. For low vegetation, the ratio of VOD to VWC tended to decrease, from springtime to summertime. For 83% of ISBA-A-gs grid cells (8 km × 8 km), sampled every 5 m by CAROLS observations at a spatial resolution of about 2 km, the standard deviation of the sub-grid CAROLS SSM was lower than 0.05 m3 m−3. The presence of small water bodies within the ISBA-A-gs grid cells tended to increase the CAROLS SSM spatial variability, up to 0.10 m3 m−3. Also, the grid cells characterised by a high vegetation cover heterogeneity presented higher standard deviation values, for both SSM and VOD