This paper analyzes the accuracy on the detection of rapeseed fields using Sentinel-1 (S1) time series. Random Forest (RF) and three deep learning (DL) algorithms namely Long Short-Term Memory Fully Convolutional Network (LSTM-FCN), InceptionTime, and Multi-layer Perceptron (MLP) were tested in this study. All four algorithms were used to classify the S1 time series with a large number of ground samples. To test the transferability of classification models, the algorithms were trained on a given year, and then tested on different years. The results demonstrated the high performance of all four algorithms in mapping rapeseed fields when using different years in training and testing phases (F1 between 85.5% and 92.7%, kappa between 0.85 and 0.93).

In recent decades, the Earth Observation (EO) wave interaction modelling domain has witnessed a proliferation of both forward and inverse models. These models are developed by the scientific community to understand the relationship between electromagnetic waves and natural surfaces, and to support methodologies for extracting bio-geophysical variables from remotely sensed data. However, the current landscape exposes certain limitations such as the absence of systematic implementation, validation on limited datasets, and a scarce integration with emerging Artificial Intelligence (AI)-based inversion techniques. This manuscript introduces the Wave Interaction Models Exploitation Framework (WIMEX), developed to address these challenges in the frame of an ESA-funded project. Leveraging EO data available today, and exploiting Graphical Processing Unit and parallel computing, the framework proposes a systematic approach to create, validate, and disseminate forward and inverse models. WIMEX aims to offer a flexible development environment supporting the evolving needs of the scientific community.

In view of the launch of the ESA HydroGNSS mission, whose receiver will measure both left and right-polarized global navigation satellite system reflectometry (GNSS-R) signal, this study analyses the features of dual-polarized signals by using simulations provided by the soil and vegetation reflection simulator (SAVERS) over both bare soil and forest. The reliability of GNSS-R dual-polarized simulations of SAVERS over land is first assessed by comparison with data collected in the frame of the GLObal navigation satellite system reflectometry instrument (GLORI) airborne campaigns. Then, the simulator is used to carry out a sensitivity analysis of left–right (LR) and right–right (RR) circularly polarized spaceborne GNSS-R signals to soil moisture (SM), soil roughness (SR), and forest biomass (BIO). The combinations of the two polarizations, such as ratio, difference, and normalized difference, are included in the analysis as well. The study evaluates also the SM effects on the horizontal-right (HR) and vertical-right (VR) polarized GNSS-R signal. The results show that the combination of the two circular polarizations can reduce the small-scale roughness effect in the SM monitoring as well as the effect of topography, and it can extend the sensitivity to large values of BIO. A critical point assessed by this study is the low value of the RR signal power, which may be difficult to detect over the noise floor, especially over land regions with low depolarization effects.

The aim of this study is to retrieve the NDVI values during the wheat cycle using the radar data over reference fields located in the Kairouan plain in the center of Tunisia. The developed approach is based on the use of C-band Sentinel-1 acquisition specifically the cross-polarization ratio and the estimated coherence as features of curve fitting equations and machine learning algorithms such as the random forest and the support vector machine regressors. The NDVI retrieve according to the wheat growth stage, at a field scale, was marked by RMSE values lower than 0.13 and bias values under -0.03

This study presents the potential of the Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data to detect irrigation events over summer crops, including Maize, Soybean, Sorghum and Potato. The potential of the S1 to detect the irrigation events was carried out using the Irrigation Event Detection Model (IEDM) in five study sites in south Europe and the Middle East. The IEDM is a decision tree model initially developed to detect irrigation events using the change detection algorithm applied to the S1 time series data. Results showed generally good overall accuracy for irrigation detection using the S1 data, reaching 67% for all studied sites together. This accuracy varied according to the studied area, with the highest accuracy for semi-arid areas and lowest for temperate areas. In addition, the accuracy of irrigation detection decreases as the vegetation becomes well developed.

This study aims to map surface soil moisture (SSM) using airborne measurements from the GLORI instrument, which utilizes GNSS-R technology. Measurements were obtained in July 2021 at the Urgell site in Spain. In situ measurements of soil moisture, roughness, and vegetation cover were collected concurrently with the flight measurements. The study analyzes the reflectivity behavior of copolarization (ΓRR) and cross-polarization (ΓRL) as a function of incidence angle, normalizing the reflectivity with respect to the incidence angle. The sensitivity of reflectivities to surface soil moisture is then examined. An empirical model, incorporating soil moisture and the normalized difference vegetation index (NDVI), based on the tau-omega model, is used to invert GNSS-R reflectivity (Γ_RL) and estimate soil moisture. The model is calibrated and validated using a threefold cross-validation approach. Finally, SSM mapping at a 100 m resolution is proposed using data from the studied site and three flights.

We propose a methodology combining a change detection approach with a neural network algorithm to monitor soil moisture. The methodology utilizes Sentinel-1 and Sentinel-2 data, incorporating various metrics such as radar signals (VV and VH polarization), surface soil moisture index (I_SSM), radar incidence angle, normalized difference vegetation index (NDVI), and VH/VV ratio. In situ data from the International Soil Moisture Network (ISMN) across diverse climatic contexts are used for testing. The results demonstrate improved soil moisture estimations using the hybrid algorithms.

The objective of this study is to analyze GNSS-R data variations as a function of land cover using airborne measurements obtained with the GLObal Navigation Satellite System Reflectometry Instrument (GLORI), which is a polarimetric instrument. GNSS-R measurements were acquired at the agricultural Urgell site in Spain in July 2021. In situ measurements describing the soil and vegetation properties were then obtained simultaneously with flight measurements. The behavior of the observable copolarization (right-right) reflectivity ΓRRand the cross-polarization (right-left) reflectivity ΓRLas a function of land use is discussed. The distribution of coherent and incoherent components in the reflected power is estimated for different types of land cover.

International audience

In this paper, we investigate the potential of the modified water cloud model to estimate soil moisture in pepper crop fields with drip irrigation in a semiarid area in Tunisia using cross-polarized L-band data (ALOS-2) and C-band data (Sentinel-1) data in Horizontal-Horizontal (L-HH) and Vertical-Vertical (C- VV) polarization, respectively. Within the context of spatially heterogeneous soil moisture, the total backscattering is the sum of pepper row scattering weighted by the vegetation fraction cover (Fe) and the inter-row soil scattering weighted by (1-Fc). The vegetation row contribution is calculated as the sum of volume scattering contribution of pepper and underlying soil components attenuated by the vegetation cover. Due to the presence of drip irrigation, the underlying soil zone is divided into two parts: irrigated and non-irrigated parts. To assess the calibrated model performance, various simulations are performed under different conditions of soil moisture and vegetation biophysical properties. Under various conditions of soil moisture, the results revealed the potential of the suggested model to simulate SAR signal where cover fraction and pepper height values are under 0.4 and 0.5 m, respectively, using L-HH and cover fraction value under 0.3 and vegetation height value 0.3 m, using C-VV data.

International audience

Despite the current short temporal coverage of high spatial resolution SM maps estimated from Synthetic Aperture Radars such as Sentinel-1(S1), their evaluation is important in the context of the ESA CCI as potential future high resolution (HR) SM long time series, and also as benchmarking references for HR SM data sets that could be obtained by downscaling coarser resolution sensors. In this context, 1 km HR SM maps obtained making a synergistic use of S 1 and Sentinel 2 (or Sentinel 3) using the$S^{2}MP$algorithm were compared to the HR SM data sets from the Copernicus Global Land Service produced from S 1 data over three regions in Europe and one in Tunisia. In addition, the$S^{2}MP$maps were also compared to the SMAP+S 1 downscaled product in those regions and in two additional ones in North America and Australia. The HR SM maps show an overall good agreement for croplands and herbaceous land covers while showing significant differences for other land cover classes. All the 1 km SM maps data sets, in addition to coarse scale SMAP, SMOS and CCI data, were evaluated against in-situ measurements. The results show that the HR products are in good agreement but they show a lower correlation with respect to in-situ data than the coarse resolution products.

International audience

Radar remote sensing has shown a high potential for soil surface parameters estimation in different pedo-climatic context. In the present study, we investigated Sentinel-l radar signal in order to analyze its behavior as function of soil moisture and soil roughness. In addition, we evaluated the approach combining the modified Integral Equation Model (IEM-B) and the Water Cloud Model (WCM) for estimating soil moisture in western France. Soil surface parameters were acquired over 4 campaigns during which composite soil samples were collected simultaneously to Sentinel-l acquisition dates. The dates of those campaigns were defined according to the evolution of the soil surface condition, during the agricultural season. The sensitivity of radar signal$\sigma 0$to soil moisture was studied over the 22 reference fields and over the Thiessen polygons created around the measurement points. Linear relationships are observed between the radar signal and volumetric soil moisture less than 35 vol. % with higher sensitivity for VH polarization (0.41 dB/vol.% in VH against 0.26 dB/vol.% in VV). The best correlation coefficients (R) were observed for the VH polarization with the Zs roughness parameter$(\mathrm{R}={}$0.53 and 0.29 for reference fields and Thiessen polygons, respectively). Following that, a comparison of in situ soil moisture with that predicted based on approach proposed by [1], using Neural network algorithm with a training using the two models IEM-B and Water Cloud Model (WCM) allowed an accuracy with an RMSE ranging between 6.1 and 6.5 vol. % for reference fields and Thiessen polygons respectively. These results confirm that the proposed algorithm is accurate to estimate soil moisture.

In this study, we propose a new approach to surface soil moisture (SSM) monitoring using the change detection technique applied to Sentinel-1 radar dataset. This involves testing a new reflectivity index (IR) deduced from the Fresnel coefficients, directly linked to soil moisture. It is between 0 and 1, 0 for the driest context (weakest radar signal), and 1 for the wettest context (highest radar signal). Simulations using the Integral Equation Model (IEM) model are proposed to confirm the usefulness of this index before real applications on different sites in Africa. Comparisons are also made with the classical index based on the detection approach habitually used.

In this paper, we discuss the potential of L-band Advanced Land Observing Satellite-2 (ALOS-2) images for retrieving soil moisture over cereal fields in a semi -arid area (Merguellil- Tunisia). SAR signal sensitivity was studied as function of in-situ measurements: roughness and soil moisture. Sensitivity to soil moisture was illustrated for three classes of Normalized Difference Vegetation Index (NDVI). Results reveal the impact of soil moisture on L-band data even in dense vegetation class (NDVI > 0.6). High correlations characterize linear relationships between radar signal and vegetation biophysical properties (Leaf Area Index, vegetation height and Vegetation Water Content). Signal modeling over bare soils was evaluated through empirical equation, modified Dubois model (Dubois-B) and modified Integral Equation Model (IEM-B). For covered fields, Water Cloud Model (WCM) was parametrized for HH and HV polarizations (with and without soil-vegetation interactions component) coupled with the best accuracy bare soil backscattering models: IEM-B for co-polarization and empirical models for the entire dataset. WCM coupled to IEM - B illustrates the best performance to estimate soil water content in HH polarization. The integration of soil-vegetation interaction component provides a stable accuracy of soil moisture estimation in HH polarization and improve soil moisture accuracy in HV polarization mode.

Better management of water consumption in irrigated agriculture is essential in order to save water resources. The objective of this study is to propose a new model capable of detecting the irrigation events using the Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) in a near real-time approach. The proposed irrigation detection model relies on the change detection in the S1 backscattering coefficients at plot scale. A tree-based approach has been constructed to detect irrigation events by studying the behavior of the S1 backscattering coefficients following irrigation events at plot scale over three study sites located in Montpellier (southeast France), Tarbes (southwest France) and Catalonia (northeast Spain). Auxiliary data such as the NDVI (Normalized Difference Vegetation Index) and the soil moisture estimations were integrated as additional filters to reduce ambiguities related to vegetation growth and surface roughness. The results shows that the proposed model was capable of detecting 84% of the irrigation events over Montpellier. Over Catalonia site, 90.2% of the non-irrigated plots had no detected irrigation events whereas 72.4% of the irrigated plots had one and more detected irrigation events. In Tarbes, the analysis shows that irrigation events could still be detected even in the presence of abundant rainfall events during the summer season.

Full waveform (FW) LiDAR systems have gained momentum to map forest biophysical variables in the last two decades, owing to their ability to accurately estimate canopy heights and aboveground biomass. Currently, the Global Ecosystem Dynamics Investigation (GEDI) system on board of the International Space Station (ISS) is the most recent FW spaceborne LiDAR instrument for the continuous observation of earth's forests. Here, we assess the accuracy of GEDI FW data for the estimation of stand-scale dominant heights ($H_{dom}$), and stand volume (V) using linear and nonlinear regression models based on several GEDI metrics. The models were calibrated and validated using in-situ data from Eucalyptus plantations in Brazil. Overall, the most accurate estimates of$H_{dom}$and V were obtained using the stepwise regression, with an RMSE of 1.44 m (R2 of 0.92) and 24.39 m3.ha−1(R2 of 0.90) respectively. The principal metric explaining more than 87% and 84% of the variability (R2) of$H_{dom}$and V was the metric representing the height above the ground at which 90% of the waveform energy occurs.

This study presents an analysis of radar signature at Ku-band for incidences ranging from 0° to 10° over the major bioclimatic zones, soil and vegetation types encountered in West-Africa, using data from Jason-3 and SWIM. Time-series of radar responses were built over the following environments: stone and sand deserts, Sahelian savannah and floodplain, flooded and non-flooded equatorial forests. Deserts and non-flooded equatorial forest exhibit almost constant responses, decreasing as the incidence angle increases. Similar seasonal variations of the backscattering coefficient between the dry and the wet season are observed at nadir for Jason-3 and SWIM with a decrease in dry season level and amplitude with the increase of the incidence angle. Backscattering at Ku-band can be related to soil roughness, vegetation cover and soil wetness.

Global and long-term vegetation optical depth (VOD) dataset are very useful to monitor the dynamics of the vegetation features, climate and environmental changes. In this study, the radar-based global ASCAT (Advanced SCATterometer) IB (INRAE-BORDEAUX) VOD was retrieved using a model which was recently calibrated over Africa. In order to assess the performance of IB VOD, the Saatchi biomass and three other VOD datasets (ASCAT V16, AMSR2 LPRM V5 and VODCA LPRM V6) derived from C-band observations were used in the comparison. The preliminary results show that IB VOD has a promising ability to predict biomass$(\mathrm{R}=0.74,\ \text{RMSE} =44.82\ \text{Mg}\ \text{ha}^{-1})$, which is better than V16 VOD$(\mathrm{R}=0.64,\ \text{RMSE} =51.27\ \text{Mg} \text{ha}^{-1})$and VODCA VOD$(\mathrm{R}=0.72,\ \text{RMSE} =47.14\ \text{Mg}\ \text{ha}^{-1})$. Some retrieval issues for IB VOD were found in boreal regions (e.g., Eastern America, Russia). In the future, we will focus on improving our algorithm in those regions, and produce a global and long-term dataset.

The aim of this study is to assess the potential use of data recorded by the Global Navigation Satellite System Reflectometry (GNSS-R) CYGNSS constellation to characterize desert surface roughness. The study is applied over the Sahara, the largest non-polar desert in the world. This is based on a spatio-temporal analysis of variations in Cyclone Global Navigation Satellite System (CYGNSS) data, expressed as changes in reflectivity (Γ). In general, the reflectivity of each type of land surface (reliefs, dunes etc.) encountered at the studied site is found to have a high temporal stability. A grid of CYGNSS Γ measurements has been developed, at the relatively fine resolution of 0.03° x 0.03°, and the resulting map of average reflectivity, computed over a 2.5-year period, illustrates the potential of CYGNSS data for the characterization of the main types of desert land surface (dunes, reliefs, etc.). A discussion of the relationship between aerodynamic roughness and CYGNSS reflectivity is reported. An aerodynamic roughness (Z0) map of the Sahara is proposed, using four distinct classes of terrain roughness.

The objective of this study is to analyze the potential of global navigation satellite system reflectometry (GNSS-R) data from the Cyclone Global Navigation Satellite System (CYGNSS) constellation to map the subsurface of a desert environment, investigate fossil river systems, and identify geological structures hidden beneath dry sand. This analysis is based on reflectivity estimates considering the main component of the coherent signal using an average of the signal over a period of 2.5 years on a grid with a resolution of 0.03°. Two sites are analyzed: Kufrah in the Libyan Desert and the Bir Safsaf region located in the southern Egyptian Desert. In both cases, we observe strong similarity with past observations derived from L-band synthetic aperture radar (SAR) data. Although the spatial resolution of CYGNSS data is lower than that of SAR data, different structures of the subsurface can be identified by the former.

The TREIA land data and services center was created with the objective of increasing the use of space data in complementarity with other types of data (in particular in situ, airborne data) by the scientific community and more generally the public actors. TREIA is structuring the French science community through 1) a mutualized Service and Data Infrastructure (SDI) distributed between several centers, allowing access to a variety of products; 2) the setup of Regional Animation Networks (RAN) to federate users (scientists and public / private actors) and 3) Scientific Expertise Centers (SEC) clustering virtual research groups on a thematic domain. A strong relationship between SECs and RANs is being developed to both disseminate the outputs to the user communities and aggregate the user needs. The research works carried out in two SECs are presented. They are organized around the design and development of value-added products and services.

The obj ective of this paper is to present an approach for mapping irrigated areas at plot scale using the Sentinel-1 radar time series. Over a study site located in Catalonia region of north Spain, a dense temporal series of S1 backscattering coefficients were first obtained at plot scale and grid scale (10km x 10km). The S1 time series at plot and grid scales were conjointly used to remove the ambiguity between rainfall events and irrigation events. The principal component analysis (PCA) and the wavelet transformation were applied to the SAR temporal series. Then, to classify irrigated/non-irrigated plots the random forest (RF) classifier was employed using the obtained principal components (PC) and the wavelet coefficients (WT). A convolutional neural network was also tested using the prepared S1 temporal series. The result of the classification reaches 90.7% and 89.1% using the PC and the WT in a random forest classifier respectively. The accuracy of the classification reaches 94.1% using the CNN.

Soil texture estimation is important in several applications. However, field sampling or laboratory analyzes are very expensive and not very representative. Besides, existing soil maps are neither exhaustive nor sufficiently precise for modeling and meeting needs at field scale. This study aimed to explore the potential of Sentinel satellites to predict topsoil texture, and more precisely to produce clay content map at fine spatial resolution. With its components and its porosity, soil texture is directly linked to soil moisture. In this context and with the arrival of Sentinel constellation, data are acquired with high spatial and temporal resolution. And soil moisture is retrieved from a synergetic use of Sentinel-l (S-1) and Sentinel-2 (S-2) data between July and early December 2017, over a semi-arid area in central Tunisia. Relationship between soil moisture and clay content is studied and used to produce texture map. Classification algorithm based on random forest (RF) is used for the mapping of clay content classes. The results showed the potential of S-1 and S-2 products to predict soil texture.

This article aims to propose an estimation of the soil moisture at a spatial resolution of 0.5 km and 6 days temporal resolution using Sentinel-1 data. The proposed approach is based on the change detection technique applied over a series of four-year measurements. The algorithm output, a moisture index is between 0 and 1, 0 for the driest soils, 1 for the wettest soils. The methodology is tested on different sites in Central Tunisia. Soil moisture estimations are compared to soil moisture in-situ measurements. Results show a good correlation between estimation and true measurements, in particular for the African regions.

Vegetation Optical Depth (VOD) plays an important role in monitoring the earth ecosystems. There are many VOD products released based on different satellites and frequencies. But most of the VOD products are derived from passive microwave data, and very few active VOD products have been released to date. This study investigated retrievals of the active microwave VOD product from C-band ASCAT (Advanced SCATterometer) observations using the water cloud model in large areas. To achieve this, the ASCAT backscatter data and ECMWF soil moisture data were used as inputs to retrieve ASCAT VOD over the whole Africa. The correlation between the retrieved VOD product and proxies of vegetation density (Saatchi biomass) were used to evaluate the model performance.

This letter proposes a deep learning model to deal with the spatial transfer challenge for the mapping of irrigated areas through the analysis of Sentinel-1 data. First, a convolutional neural network (CNN) model called “Teacher Model” is trained on a source geographical area characterized by a huge volume of samples. Then, this model is transferred from the source area to the target area characterized by a limited number of samples. The transfer learning framework is based on a distill and refine strategy, in which the teacher model is first distilled into a student model and, successively, refined by data samples coming from the target geographical area. The proposed strategy is compared with different approaches including a random forest (RF) classifier trained on the target data set and a CNN trained on the source data set and directly applied on the target area as well as several CNN classifiers trained on the target data set. The evaluation of the performed transfer strategy shows that the “distill and refine” framework obtains the best performance compared with other competing approaches. The obtained findings represent a first step toward the understanding of the spatial transferability of deep learning models in the Earth observation domain.

Monitoring the surface soil moisture (SSM) in agricultural areas at plot scale helps in many applications such as irrigation planning and crop management. Over the last decade, SAR (Synthetic Aperture Radar) data have shown great potential in estimating SSM over agriculture areas. Today, Sentinel-1 (S1) and Sentinel-2 (S2) satellites present a good opportunity for operational SSM estimates in agricultural areas because they provide free and open access data at high spatial resolution (10 m x 10 m) and high revisit time (6 days over Europe). The aim of this paper is to present an operational approach for mapping soil moisture at high spatial resolution (plot scale) in agriculture areas by coupling S1 and S2 images. The proposed approach is based on the inversion of the Water Cloud Model (WCM) using the neural network technique.

Identifying the irrigated areas is essential for waters managers who are in charge of distributing this resource over a large scale. The monitoring of water soil content and irrigation is a powerful tool for water resource management. The potential of Sentinel-1 (S1) and Sentinel-2 (S2) data for estimating the soil moisture and irrigation is studied over covered surfaces. An inversion algorithm of the Water Cloud Model (WCM) was developed after calibrating and validating the model over the Kairouan plain, a semi-arid region in Tunisia. The aim is to restitute soil moisture over the whole region. The developed algorithm used a synergy between S1, radar data in VV polarization, and NDVI derived from S2 optical data at high spatial resolution. The results showed good accuracy between retrieved and measured soil moisture with a Root Mean Square Error (RMSE) lower than 6 vol.%. Then, the resulting soil moisture maps were used for irrigation mapping. The process used a combination of Support Vector Machine (SVM) and Decision Tree classifications to distinguish between irrigated and non-irrigated agricultural fields. Results from the annual irrigation map show that the overall accuracy on the classification is about 77%.

The main objective of this study is to propose an inversion algorithm for the estimation of soil moisture from CYGNSS data. The algorithm based on the change detection technique is applied to CYGNSS data after several corrections taking in account the incidence effects as well as different noises. The algorithm is validated on a one study site in North Africa. Comparisons with field data and ASCAT products illustrate a strong potential for CYGNSS products.

The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture in agricultural tropical areas. Simultaneously to several radar acquisitions made between June and October 2018, using ALOS2-PALSAR sensor over the Berambadi site (south of India), ground measurements of soil roughness, soil water content, LAI were recorded. The sensitivity of the ALOS-2 measurements to variations in soil moisture, which has been reported in several scientific publications, is confirmed in this study, even for dense crops. The radar signals are simulated using different types of backscattering models (physical and semi-empirical) over bare soil and vegetation cover for different types of crops (tumeric, etc). WCM model parameterized with LAI for vegetation contribution allows a good estimation of soil moisture for tumeric.

The potential of Sentinel-1 C-band SAR data in VV polarization for estimating the surface roughness (Hrms) over bare agricultural soils was studied. First, a neural network (NN) is used for estimating the soil moisture (mv). Then, a second neural network is used for retrieving the soil roughness in using as an input to the network the soil moisture that was estimated by the first network. The neural networks are trained using simulated dataset generated from the radar backscattering model IEM (Integral Equation Model). The inversion approach is then validated using Sentinel-1 images collected over two study sites, one in France and one in Tunisia. Results show that the use of C-band in VV polarization does not allow a reliable estimate of the soil roughness. Results show that the accuracy on the estimates of Hrms is about 0.8 cm (RMSE).

The objective of the present paper is to develop an operational approach for soil moisture mapping in agricultural areas at a high spatial resolution over bare soils, as well as soils with vegetation cover. The developed approach is based on the synergic use of radar and optical data and uses the neural network technique to invert the radar signal. Three inversion SAR (Synthetic Aperture Radar) configurations were tested: (1) VV polarization, (2) VH polarization, and (3) both VV and VH polarization, all in addition to the NDVI information extracted from optical images. Neural networks were developed and validated using synthetic and real databases. The results showed that the soil moisture could be estimated in agricultural areas with an accuracy of approximately 5 vol.%.

The water level of inland water bodies plays an essential role in water balance management. Satellite altimeters can play an important role in monitoring water level, namely in remotely access places. However, satellite altimeters are normally designed to monitor homogeneous surfaces such as oceans or ice sheets, which results in poor performance over small inland water bodies because of the land contamination contribution in the returned waveforms. This paper presents three specialized algorithms or retrackers to retrieve water levels from radar altimeter data over inland water bodies dedicated to restricting the land contamination of the signal. The performances of the waveform portion selection method and the three retrackers which are the threshold retracker, Offset Center of Gravity (OCOG) retracker and 2-step analytical retracker are compared. Time series of water level results are retrieved over water bodies in Ebre river basin (Catalunya, Spain) and Lake Volta (West Africa). The standard deviation for 2-step analytical retracker combined with the waveform portion selection ranges from 0.02m to 0.05m, for OCOG retracker ranges from 0.07m to 0.13m, and for threshold re-tracker, it ranges from 0.05m to 0.1m over Lake Volta. The results show good accuracy with the insitu measurements over lake Ebre and Ribarroja reservoir with RMSE about 0.44 m and 0.18 m separately. All performances of the three retrackers are compared with the onboard retracker as well.

This paper presents the methodology for irrigation mapping using the Sentinel-1 SAR data. The study is performed using VV polarization over an agricultural site in Urgell, Catalunya (Spain). From the time series for each field, the indices including the mean value and variance of the signal, the correlation length, the fractal dimension which are derived from the backscatter time series are analyzed. The classification of irrigated and nonirrigated fields is done with the indices vector formed by the parameters analyzed. The result is compared with the supervised classification from Sentinel-2 multi-band data. The accuracy is 77%. The methodology uses only SAR data, which makes it usable for all areas even with cloud cover most times of the year.

In this study we present techniques that have been developed to optimize the processing of airborne GNSS-R data, with the goal of improving its accuracy and robustness under nonoptimal conditions. This approach is based on the detailed analysis of data produced by the instrument GLORI, which was recorded during an airborne campaign in the south west of France in June 2015. Our technique relies on the improved determination of reflected waveform peaks in the delay dimension, which is related to the loci of the signals contributed by the zone surrounding the specular point. It is shown that when developing techniques for the correct localization of waveform maxima under conditions of surfaces of low reflectivity, and/or contamination from the direct signal, it is possible to correct and extract values corresponding to the real reflectivity of the zone in the neighborhood of the specular point.

The applications of spaceborne GNSS reflectometry data over land are investigated in this work using the data collected by the UK TechDemoSat experimental mission. In particular, the sensitivity of the reflection (including specular coherent reflection and to some extend diffuse incoherent scattering) to soil moisture and forest biomass are preliminary considered. In order to quantify the biomass and moisture sensitivity it is necessary to extract a quantity, like the surface reflectivity, as much as possible independent from the system parameters. We have tried to exploit the direct signal from the uplooking antenna for this purpose and we show differences with respect to other approaches. To understand the scattering mechanisms and potentialities and limitations of GNSS-R over land, an electromagnetic simulator is used and compared to the experimental data. Although the simulator was tuned on ground based and airborne data, the satellite platform poses additional problems due to the low magnitude of the reflected signal and to topography effects. These are discussed in the paper.

The main objective of this study is to analyze the potential use of Sentinel-l (S1) radar data for the estimation of soil moisture in agricultural areas. Simultaneously to several S1 acquisitions made between 2015 and 2017 over different sites, ground measurements of soil roughness, soil water content, LAI and crop height were recorded. The sensitivity of S1 signal to variations in soil moisture is discussed. A modelling based on Water Cloud Model was proposed to simulate radar signal backscattered over covered vegetation surfaces. Three inversion approaches were proposed to retrieve surface soil moisture at field scale.

The GLORI Campaign performed in June-July 2015 to investigate the sensitivity of airborne GNSS-R measurements to land parameters is presented. In this paper data obtained on forest areas are analyzed. Ground truth measurements of tree height, density and diameter at breast height, AGB etc were measured over 100 maritime pine forest plots of various ages. The correlation between forest parameters and GNSS reflectivity in LHCP polarization (ΓLR) or polarization ratio (PR) yields to high sensitivity for high elevation angles (70°-90°). Results show that PR illustrates highest potential than the reflectivity in LHCP.

The objective of this paper is to propose a new semi-empirical radar backscattering model for bare soil surfaces based on the Dubois model. A wide dataset of backscattering coefficients extracted from SAR (synthetic aperture radar) images and in situ soil surface parameter measurements (moisture content and roughness) is used. This dataset contains a wide range of incidence angles (18°-57°) and radar wavelengths (L, C, X), well distributed geographically for regions with different climate conditions (humid, semi-arid and arid sites) and involving many SAR sensors. The proposed model, developed in HH, HV and VV polarizations, uses a formulation of radar signals based on physical principles that validated in numerous studies. The results show that the new model shows a very good performance for different radar wavelength (L, C, X), incidence angles, and polarizations (Root Mean Square Error “RMSE” about 2 dB).

The main goal of this paper is to assess the radiometric stability of the new Sentinel-1A (S-1A) SAR (Synthetic Aperture Radar) sensors. The S-1A radiometric stability was assessed by analyzing the temporal variations of the backscattering coefficient (σ°) returned from invariant targets. The results show three stable sub-time series of S-1A data. The first (between 1 October 2014 and 19 March 2015) and third (between 25 November 2015 and 1 February 2016) sub-time series have almost the same mean σ°-values (a difference lower than 0.3 dB). The mean σ°-value of the second sub-time series (between 19 March 2015 and 25 November 2015) is higher than that of the first and the third sub-time series by roughly 0.9 dB. Moreover, our results show that the stability of each sub-time series is better than 0.48 dB.

The inland water level plays an essential role in water balance management. This paper presents methodology to retrieve water level from radar altimeter data including Cryosat-2 and Jason-2 over lakes in Western Africa. The idea is to combine both Level-1 and Level-2 data to retrieve water level more precisely and automatically over small lakes. From Level-1 data, the waveform is analyzed for data filtering and the possibility of water level retrieval. Then an iteration method is proposed to retrieve the height from Level-2 altimeter data with a strict water mask. The preliminary results of water level time series, derived from Level-2 data, in Lake Volta and Lake Kainji are presented in this paper, and the results are compared with the data from DAHITI (Database for Hydrological Time Series of Inland Waters). The standard deviation is controlled below 0.3 meters.

This paper presents two methodologies retrieving soil moisture from SAR remote sensing data. The study is based on Sentinel-1 data in the VV polarization, over a site in Urgell, Catalunya (Spain). By modeling the backscatter difference w.r.t. NDVI, the soil moisture corresponding to a specific NDVI value can be retrieved. The first algorithm is already developed in West Africa[1] from ERS scatterometer data to estimate soil water status. In this study, it is adapted to Sentinel-1 data and take into account the high repetitiveness of data in optimizing the inversion approach. Another new method is developed based on the backscatter difference between two adjacent days of Sentinel-1 data w.r.t. NDVI, with smaller vegetation change, the backscatter difference is more sensitive to soil moisture. The validation of the two methods is done with field data acquired in study site, with an rms error about 0.08 m3/m3for method 1 and 0.07 m3/m3for method 2 in volumetric moisture.

In this paper, we investigate the estimation of crop biophysical properties from small footprint LiDAR waveforms inversion. Due to crop heterogeneity within the same agricultural field, a classification on similar waveform clusters is performed before inversion. A Look up table (LUT) approach was adapted then, to derive the height and LAI of maize and wheat crops. The LUT was generated using the Discrete Anisotropic Radiative Transfer (DART) by simulating the LiDAR observations which are used to search for the suitable set of biophysical properties describing the different crops clusters. The results are promising. Crops height is accurately estimated with a root mean square error (RMSE) of 0.06m and 0.03m for maize and wheat, respectively. LAI was well estimated with RMSE of 0.07 and 0.43 for maize and wheat, respectively.

The GLORIE Campaign was performed in June-July 2015 in order to investigate the sensitivity of airborne GNSS-R measurements to land parameters. In this paper we present the first results focusing on agricultural areas. For this purpose ground truth measurements of soil moisture, roughness, plant water content, leaf area index and plant height were measured over 20 agricultural plots of various crops (cereals, vegetables, bare soil). The correlation with GNSS reflectivity in LHCP polarization confirms noticeable sensitivity to soil moisture, and plant-related parameters especially vegetation cover height.

The aim of this research is to analyze the potential use of Global Navigation Satellite System (GNSS) signals for the monitoring of local vegetation characteristics. A new instrument, based on the use of a pair of low-cost receivers and antennas, providing continuous measurements of all the available Global Positioning System (GPS) satellite signals is proposed for the determination of signal attenuation caused by vegetation cover. Experimental campaigns with this instrument, combined with ground-truth measurements of the vegetation, were performed over a non-irrigated sunflower test field for a period of more than two months, corresponding to a significant portion of the vegetation cycle. A high correlation is observed between the vegetation's water content and the GPS signals attenuation, and an empirical modeling is tested for the retrieval of signal behavior as a function of vegetation water content.

The main objective of this study is to develop an inversion technique based on neural networks to estimate soil surface moisture and leaf area index (LAI) in irrigated grasslands by combining fully polarimetric RADARSAT-2 C-band SAR and optical data (LANDSAT). The benefits of having data in dual-polarization or in full-polarization mode for the SAR images were evaluated in comparison to the single-polarization mode. In addition, the use of polarimetric parameters, mainly Shannon entropy and Pauli components, was also studied. In addition, configurations using in situ measurements of the fraction of absorbed photosynthetically active radiation (FAPAR) and the fraction of green vegetation cover (FCover) were also tested. The results showed that HH is the polarization most relevant to soil moisture estimates (RMSE~6 vol.%). The use of in situ FAPAR and FCover only improved the estimate of LAI (RMSE~0.37 m2/m2. The use of polarimetric parameters did not improve the estimate of soil moisture and vegetation parameters.

The aim of this paper is to estimate geometric, water and physical surface soil parameters from typical semi-arid regions made over bare study area (North Africa) using multi-temporal X-band SAR images (TerraSAR-X). For spatial and temporal surface roughness estimation, empirical relationships between radar and soil roughness parameters (rms height “Hrms”, and Zg parameter) were proposed. Two roughness classes are identified through radar signal inversion (smooth and ploughed soils). For the retrieval of surface soil moisture at a high spatial resolution, an algorithm combing TerraSAR-X images with continuous thetaprobe measurements was proposed. Two assumptions were studied: (1) roughness variations during the radar acquisition campaigns were not accounted for; (2) a simple correction for temporal variations in roughness was included. Finally, an empirical relationship was established between the mean moisture values retrieved from the SAR images and the percentage of clay over several test fields. Results showed that highly accurate clay estimations can be achieved.

The aim of this study is to assess the interests of integrating remote-sensing-derived parameters (LAI, harvest and irrigation dates) in a crop model (PILOTE) that simulates vegetation growth for hay crop.

The GLORIE GNSS-R airborne campaign was conducted in the late spring 2015 with the GLORI polarimetric receiver. More than 15 hours or raw data was gathered during 5 flights that spanned over a 3-week period. The aircraft flew over several areas if interest including: 1) agricultural plots with coincident in-situ measurements of soil moisture, vegetation biomass and roughness, 2) in situ monitored forest plots with a wide range of above ground biomass values and 3) inland water bodies in order to test phase altimetry retrievals. Apparent reflectivity was computed from the data, showing a good dynamics above various types of terrains. Phase altimetry was performed over calm water, showing a precision in the range of the centimeter level.

In this paper, the potential use of P-band radar signal for the estimation of soil roughness parameters is analyzed. The Integral Equation Model (IEM) is used to study the sensitivity of backscattered P-band signal to soil surface parameters. A new roughness parameter referred to as Zp, combining the root mean square surface height and the correlation length, is proposed to describe the behavior of P-band radar signal as a function of soil roughness. The IEM model is validated using real data covering a large range of roughness values, derived from experimental airborne P-band SAR campaigns made over agricultural fields. Discrepancies between the measurements and simulations led to the analysis of the influence of low frequency roughness structures on backscattering simulations. The analysis of the behavior of P-band radar signal as a function of multi-scale soil roughness (micro topography and large roughness structures) reveals the complexity of using P-band data for the analysis of bare surface soil parameters.

The behavior of the Ka-band backscattering coefficient at nadir and close-to-nadir angles for land applications is poorly documented. The measurements made during a ground-based campaign at Ka-band were performed at nadir and close-to-nadir angles over bare soils for different surface roughness and soil moisture conditions. The resulting backscattering levels exhibited a dynamic range of approximately 23 dB at nadir for soil moisture contents between 5% and 50% m3/m3over both smooth and rough surfaces. These results were then compared to the geometric optics (GO) and millimeter microwave (MMW) models. Generally, GO finely fits the backscattering coefficients close to nadir, and MMW appeared to fit for larger incidence angles or rough surfaces. The results obtained in this letter can address prelaunch science and engineering considerations for the interferometry-altimetry Surface Water and Ocean Topography mission operating at Ka-band.

The objective of this paper is to extend the semi-empirical calibration of the backscattering Integral Equation Model (IEM) initially proposed for SAR data at C- and X-bands to SAR data at L band. A large dataset of radar signal and in situ measurements (soil moisture and surface roughness) over bare soil surfaces were used. A semi-empirical calibration of the IEM was performed at L band in replacing the correlation length derived from field experiments by a fitting parameter. Better agreement was observed between the backscattering coefficient provided by the SAR and that simulated by the calibrated version of the IEM.

The aim of this study was to develop an inversion approach to estimate surface soil moisture from X band SAR data over irrigated grassland areas. This approach is based on the coupling between Synthetic Aperture Radar and optical images through the Water Cloud Model. An inversion technique based on multi-layer perceptron neural networks was used to invert the WCM for soil moisture estimation. Three inversion configurations were defined: (1) HH polarization, (2) HV polarization, and (3) both HH and HV polarizations, all including the Leaf Area Index derived from optical images. For the three inversion configurations, the NNs were trained and validated using a noisy synthetic dataset generated by the WCM for a wide range of soil moisture and LAI values. The trained NNs were then validated from a real dataset. The use of X band SAR measurements in HH polarization yields more precise results on soil moisture estimates.

GLORI (GLObal navigation satellite system Reflectometry Instrument) is a new receiver dedicated to the airborne measurement of surface parameters such as soil moisture and biomass above ground, as well as sea state (wave height and direction) above oceans. The instrument is based on the PARIS concept [1] using both the direct and surface-reflected L-band signals from the GPS constellation as a multistatic radar source. A test campaign has been performed in November 2014, and the preliminary results show a great potential for reflectometry applications.

The objective of this study was to analyze the L-band SAR backscatter sensitivity to forest biomass for Eucalyptus plantations. The results showed that the radar signal is highly dependent on biomass only for values lower than 50 t/ha, which corresponds to plantations of approximately three years of age. Next, Random Forest regressions were performed to evaluate the potential of PALSAR data to predict the Eucalyptus biomass. Regressions were constructed to link the biomass to both radar signal and age of plantations. Results showed that the age was the variable that best explained the biomass followed by the PALSAR HV polarized signal. For biomasses lower than 50 t/ha, HV signal and plantation age were found to have the same level of importance in predicting biomass. For biomasses higher than 50 t/ha, plantation age was the main variable in the random forest models. The use of PALSAR signal alone did not correctly predict the biomass of Eucalyptus plantations (R2lower than 0.5 and RMSE higher than 46.7 t/ha). The use of plantation age in addition to the PALSAR signal improved slightly the prediction results (R2increased from 0.88 to 0.92 and RMSE decreased from 22.7 to 18.9 t/ha).

The objective of this study was to analyze the sensitivity of radar signal in X-band to irrigated grassland soil conditions. Time series of radar (TerraSAR-X and Cosmo-SkyMed) images were acquired at a high temporal frequency in 2013 over a small agricultural region in South Eastern France. Simultaneously to satellite data acquisitions, ground measurements were conducted during several growing cycles of the grassland in order to monitor evolution in soil and vegetation characteristics. Results show that radar signal is clearly dependent on the soil moisture with a higher sensitivity to soil moisture for biomass lower than 1 kg/m2. HH and HV polarizations showed almost similar sensitivity to soil moisture. The penetration depth of the radar wave in X-band was high even for dense and high vegetation: flooded areas were clearly visible on the images with higher detection potential in HH polarization than in HV polarization even for vegetation heights reaching 1 m. These results showed that it is possible to track gravity irrigation and soil moisture variation from SAR X-band images acquired at high spatial resolution and medium incidence angle.

The goal of this paper is to analyze the potential of COSMO-SkyMed and TerraSAR-X SAR measurements over bare soils in order to estimate correctly soil parameters. We analyzed statistically the relationships between X-SAR backscattering signals function of soil moisture and different roughness parameters (the root mean square height Hrms, the Zs parameter and the Zg parameter) at HH polarization and for an incidence angle about 35.5°. Results have shown a high sensitivity of real radar data to the two soil parameters: roughness and moisture. A linear relationship is obtained between volumetric soil moisture and radar signal with the strongest correlation observed with gravimetric moisture measurements. A logarithmic correlation is observed between backscattering coefficient and all roughness parameters. The highest dynamic sensitivity is obtained with Zg parameter.

In this paper, a new description of soil surface roughness is proposed for microwave applications. This is based on an original roughness parameter, Zg, which combines the three most commonly used soil parameters: root mean surface height, correlation length, and correlation function shape, into just one parameter. Numerical modelling, based on the moment method and integral equations, is used to evaluate the relevance of this approach. It is applied over a broad dataset of numerically generated surfaces characterised by a large range of surface roughness parameters. A strong correlation is observed between this new parameter and the radar backscattering simulations, for the HH and VV polarizations in the C and X bands. It is proposed to validate this approach using data acquired in the C and X bands, at several agricultural sites in France. It was found that the parameter Zg has a high potential for the analysis of surface roughness using radar measurements. An empirical model is proposed for the simulation of backscattered radar signals over bare soil.

The aim of this letter is to discuss the influence of radar frequency on the relationship between surface soil moisture and the nature of radar backscatter over bare soils. In an attempt to address this issue, the advanced integral equation model was used to simulate backscatter from soil surfaces with various moisture vertical profiles, for three frequency bands, namely, L, C, and X. In these computations, we investigated the influence of the vertical heterogeneity of soil moisture on the characteristics of the backscattered signals. The influence of radar frequency is clearly demonstrated. A database produced from Envisat ASAR and TerraSAR-X data, which was acquired over bare soils with in situ measurements of moisture content and ground surface roughness, was used to validate the utility of taking the soil moisture heterogeneity into account in the backscatter model.

The potential of polarimetric synthetic aperture radar data for the soil surface characterization of bare agricultural soils was investigated by using air- and spaceborne data acquired by Radar Aéroporté Multi-Spectral d'Etude des Signatures (RAMSES), Système Expérimental de Télédétection Hyperfréquence Imageur (SETHI), and RADARSAT-2 sensors over several study sites in France. Fully polarimetric data at ultrahigh frequency, X-, C-, L-, and P-bands were compared. The results show that the main polarimetric parameters studied (entropy, α angle, and anisotropy) are not very sensitive to the variation of the soil surface parameters. Low correlations are observed between the polarimetric and soil parameters (moisture content and surface roughness). Thus, the polarimetric parameters are not very relevant to the characterization of the soil surface over bare agricultural areas.

Soil surface characteristics, namely the soil moisture content and roughness, play an important role in different applications such as hydrology, agronomy or meteorology. Currently, except for RADARSAT-2 which is polarimetric sensor, the current satellite SARs allow acquisitions only with one or two polarizations. There is currently a great challenge in the use of polarimetric parameters in order to estimate surface roughness and soil moisture. Only a few studies have analysed the potential uses of polarimetric SAR data for the estimation of surface roughness and soil moisture over bare agricultural fields ([1], [2], [3] and [4]). The objective of the present study is to investigate the sensitivity of polarimetric parameters at the C-band to bare agricultural soil parameters (soil moisture and surface roughness). Indeed, the potential of polarimetric parameters in the C-band was studied little and the available SAR studies use especially high radar wavelengths as the L-band. Only the following polarimetric descriptors that were considered to be important for the characterisation of the soil surface parameters were analysed: the angle α1, the entropy (H), the anisotropy (A), and the eigenvalue relative differences (SERD and DERD). These polarimetric parameters are resulting from the studies carried out with L-band polarimetric data over bare agriculture fields.

The objective of this paper is to present the contribution of a new dielectric constant characterization for the modelling of radar backscattering behavior. Our analysis is based on a large number of radar measurements acquired during different experimental campaigns (Orgeval'94, Pays de Caux'98, 99). We propose a dielectric constant model, based on the combination of contributions from both soil and air fractions. This modelling clearly reveals the joint influence of the air and soil phases, in backscattering measurements over rough surfaces with large clods. A relationship is established between the soil fraction and soil roughness, using the Integral Equation Model (IEM), fitted to real radar data. Finally, the influence of the air fraction on the linear relationship between moisture and the backscattered radar signal is discussed.

In this paper, it is proposed to use TERRASAR-X data for analysis and estimation of soil surface texture. Our study is based on experimental campaigns carried out over a semi-arid area in North Africa. Simultaneously to TERRASAR-X radar acquisitions, ground measurements (texture, soil moisture and roughness) were made on different test fields. A strong correlation is observed between soil texture and a processed signal from two radar images, the first acquired just after a rain event and the second corresponding to dry soil conditions, acquired three weeks later. An empirical relationship is proposed for the retrieval from radar signals of clay content percent. Soil texture mapping is proposed over the study site, which includes bare soils and olive groves.

The retrieval of the bare soil moisture content from TerraSAR-X data is discussed using empirical approaches. Two cases were evaluated: (1) one image at low or high incidence angle and (2) two images, one at low incidence and one at high incidence. This study shows by using three databases collected between 2008 and 2010 over two study sites in France (Orgeval and Villamblain) that TerraSAR-X is a good remote sensing tool for the retrieving of surface soil moisture with accuracy of about 3% (rmse). Moreover, the accuracy of the soil moisture estimate does not improve when two incidence angles (26°-28°or 50°-52°) are used instead of only one. When compared with the result obtained with a high incidence angle (50°-52°), the use of low incidence angle (26°-28°) does not enable a significant improvement in estimating soil moisture (about 1%).

In this letter, it is proposed to use TerraSAR-X data for analysis and estimation of soil surface texture. Our study is based on experimental campaigns carried out over a semiarid area in North Africa. Simultaneously with TerraSAR-X radar acquisitions, ground measurements (texture, soil moisture, and roughness) were made on different test fields. A strong correlation is observed between soil texture and a processed signal from two radar images, with the first acquired just after a rain event and the second corresponding to dry soil conditions, acquired three weeks later. An empirical relationship is proposed for the retrieval from radar signals of clay content percent. Soil texture mapping is proposed over the study site, which includes bare soils and olive groves.

Spatial and temporal variations of soil moisture strongly affect flooding, erosion, solute transport, and vegetation productivity. Their characterization offers numerous possibilities for the improvement of our understanding of complex land-surface–atmosphere interactions. In this paper, soil moisture dynamics at the soil's surface (the first centimeters) and in its root zone (at depths down to 1 m) are investigated using$25 \times 25\ \hbox{km}^{2}$scale data (Advanced Scatterometer (ASCAT)/METorological OPerational (METOP) scatterometer), for a semiarid region in North Africa. Our study highlights the quality of the surface and root-zone soil moisture products, derived from ASCAT data recorded over a two-year period. Surface soil moisture tends to be highly variable because it is strongly influenced by atmospheric conditions (rain and evaporation). On the other hand, root-zone moisture is considerably less variable. A statistical drought-monitoring index, referred to as the “moisture anomaly index,” is derived from ASCAT and European Remote Sensing (ERS) time series. This index was tested with ERS and ASCAT products during the 1991–2010 study period. A strong correlation is found between the proposed index and the standardized precipitation index.

The objective of this study was to analyze the potential of the C-band polarimetric synthetic aperture radar (SAR) parameters for the soil surface characterization of bare agricultural soils. RADARSAT-2 data and simulations using the integral equation model were analyzed to evaluate the polarimetric SAR parameters' sensitivities to the soil moisture and surface roughness. The results showed that the polarimetric parameters in the C-band were not very relevant to the characterization of the soil surface over bare agricultural areas. Low dynamics were often observed between the polarimetric parameters and both the soil moisture content and the soil surface roughness. These low dynamics do not allow for the accurate estimation of the soil parameters, but they could augment the standard inversion approaches to improve the estimation of these soil parameters. The polarimetric parameter$\alpha_{1}$could be used to detect very moist soils ($>$30%), while the anisotropy could be used to separate the smooth soils.

A renewal of interest for the radiometric L-band Sea Surface Salinity (SSS) remote sensing appeared in the 1990s and led to the Soil Moisture and Ocean Salinity (SMOS) satellite launched in November 2009 and to the Aquarius mission (launched in June 2011). However, due to low signal to noise ratio, retrieving SSS from L-band radiometry is very challenging. In order to validate and improve L-band radiative transfer model and salinity retrieval method used in SMOS data processing, the Cooperative Airborne Radiometer for Ocean and Land Studies (CAROLS) was developed. We analyze here a coastal flight (20 May 2009), in the Gulf of Biscay, characterized by strong SSS gradients (28 to 35 pss-78). Extensive in-situ measurements were gathered along the plane track. Brightness temperature$(T_{b})$integrated over 800 ms correlates well with simulated$T_{b}$(correlation coefficients between 0.80 and 0.96; standard deviations of the difference of 0.2 K). Over the whole flight, the standard deviation of the difference between CAROLS and in-situ SSS is about 0.3 pss-78 more accurate than SSS fields derived from coastal numerical model or objective analysis. In the northern part of the flight, CAROLS and in-situ SSS agree. In the southern part, the best agreement is found when using only V-polarization measured at 30$^{\circ}$incidence angle or when using a multiparameter retrieval assuming large error on$T_{b}$(suggesting the presence of biases on H-polarization). When compared to high-resolution model SSS, the CAROLS SSS underlines the high SSS temporal variability in river plume and on continental shelf border, and the importance of using realistic river run-offs for modeling coastal SSS.

The potential of TerraSAR-X (X-band) in monitoring sugarcane growth was investigated on Reunion Island. Multi temporal TerraSAR data acquired at various incidence angles (17°, 31°, 37°, 47°, 58°) and polarizations (HH, HV, VV) were analyzed in order to study the behaviour of SAR (synthetic aperture radar) signal as a function of sugarcane height. The potential of TerraSAR for mapping the sugarcane harvest was also studied. Radar signal increased quickly with crop height until a threshold height, which depended on polarization and incidence angle. Beyond this threshold, the signal increased only slightly, remained constant, or even decreased. TerraSAR data showed that after strong rains the soil contribution for the backscattering of sugarcane fields can be consequent for canes with heights of terminal visible dewlap (htvd) less than 50cm (total cane heights around 155cm). Finally, TerraSAR data at high spatial resolution were shown to be useful for monitoring sugarcane harvest when the fields are of small size or when the cut is spread out in time. The radar incidence of 37° is more suitable to monitor the sugarcane harvest.

Soil salinization is one of the most hazardous phenomenon accelerating the land degradation processes. Map ping and tracking soil salinity changes is fundamental for anticipating natural disaster, such as desertification, in arid and semi-arid regions. In this work, we establish an empirical model for soil salinity mapping based on a gaussian mixture and using field electrical conductivity (EC) measures. The developed model is tested on saline soil samples collected from the semi-arid region of Kairouan located in central Tunisia. It is based on statistical moments derived from multiband (HH and VV) intensity synthetic aperture radar (SAR) data of the Envisat satellite. The resulting salinity map is composed of three classes of salinity (Low, Medium and High) with respect to the EC measurements. The developed model is validated for low salinity distribution, whereas, it needs more samples to be generalized for medium and high soil salinity content.

The estimation of surface soil parameters (moisture and roughness) from synthetic aperture radar (SAR) images requires the use of well-calibrated backscattering models. The objective of this letter is to extend the semiempirical calibration of the backscattering integral equation model (IEM) initially proposed by Baghdadifor HH and VV polarizations to HV polarization. The approach consisted in replacing the measured correlation length by a fitting/calibration parameter so that model simulations would closely agree with radar measurements. This calibration in C-band covers radar configurations with incidence angles between 24$^{\circ}$and 45.8$^{\circ}$. Good agreement was found between the backscattering coefficient provided by the SAR and that simulated by the calibrated version of the IEM.

The objective of this letter is to evaluate the surface radar backscattering models, namely, integral equation model (IEM), Oh, and Dubois, for synthetic aperture radar data in X-band over bare soils. This analysis uses a large database of TerraSAR-X images and in situ measurements (soil moisture “mv” and surface roughness “h_rms”). Oh's model correctly simulates the radar signal forHHandVVpolarizations, whereas the simulations performed with the Dubois model show a poor correlation between TerraSAR-X data and model. The backscattering IEM simulates correctly the backscattering coefficient only forh_rms; 1.5 cm in using Gaussian function. However, the results are not satisfactory for the use of IEM in the inversion of TerraSAR-X data. A semiempirical calibration of IEM was done in X-band. Good agreement was found between the TerraSAR-X data and the simulations using the calibrated version of the IEM.

In 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.

The objective of this paper is to analyze the sensitivity of very high resolution TerraSAR-X radar data taken over bare soils to surface soil parameters and to study the spatial variability of these parameters at a fine scale (within a field plot). The relationship between the backscattering coefficient and the soil's parameters (moisture, surface roughness, texture, and local topography) was examined by means of four satellite images, as well as ground truth measurements, of each of the three agricultural plots, recorded during several field campaigns in the winter and spring of 2008. TerraSAR images demonstrate high potential for the identification of local variations of roughness and texture. An approach for the estimation of local moisture is proposed using an empirical method adapted to the scale of an individual field. The results show that, by using TerraSAR-X data to study bare agricultural fields, local variations in soil moisture can be retrieved with a root-mean-square error of 0.05$\hbox{cm}^{3} \cdot \hbox{cm}^{-3}$.

In recent years, the presence of a new type of agricultural-surface tillage used for the sowing of wheat and corn has been observed with increasing frequency. It illustrates less roughly ploughed soils, with a greater quantity of small clods distributed over the soil surface. In this paper, a new description of such rough agricultural surfaces is proposed. It is based on a composite model, including a classical surface represented by an exponential correlation function, together with a random cloddy structure. This description enables volumetric structures to be introduced over the soil's surface. A numerical moment-modeling method, based on integral equations, is used to evaluate the contribution of clods to the radar backscattering behavior of agricultural surfaces. It is found that the presence of clods explains the very small correlation lengths which are often found in cloddy agricultural fields. The classical approach, in which the surface is described by a correlation function only based on two statistical parameters, rms height and correlation length, overestimates the backscattering coefficients when compared with an approach that includes the clods. This overestimation is often observed with real radar data for such fields.

The CAROLS L-band radiometer, which is built and designed as a copy of DTU EMIRAD II instrument will be used in conjunction with other airborne instruments (in particular the C-Band scatterometer STORM) in coordination with in situ field campaigns for futur SMOS CAL/VAL activities. A validation campaign with four flights was made over the South West of France and the Bay of Biscay (Atlantic Ocean) in September 2007. Different instrumented sites were over ocean and land surfaces were coverecd. Moreover, in order to qualify the radiometric data, different types of aircraft maneuvers were performed over ocean: circle flights, wing and nose wags. We present in this paper the first analysis of the data quality using these ocean measurements. We show a very good sensitivity of both channels.

The CAROLS, L band radiometer, is built and designed as a copy of EMIRAD II radiometer of DTU team. It is a Correlation radiometer with direct sampling and fully polarimetric (i.e 4 Stockes). It will be used in conjunction with other airborne instruments (in particular the C-Band scatterometer (STORM) and IEEC GPS system, Infrared CIMEL radiometer, one visible camera), in coordination with in situ field campaigns for SMOS CAL/VAL. The instruments are implemented on board the French research airplane ATR42. A validation campaign with four flights was made over south west of France, Hourtin Lake and Bay of Biscay (Atlantic Ocean) in September 2007. In order to qualify the radiometer data, different types of aircraft movements were realized: circle flights, wing and nose wags. Simultaneously to flights, different ground measurements were made over continental surfaces and ocean. First results show a good quality of data over ocean surfaces. For continental surfaces, important Radio-Frequency Interferences (RFI) were observed over a large part of the studied region.

The analysis of feedback phenomena, which occur between continental surfaces and the atmosphere, is one of the keys to an improved understanding of African monsoon dynamics. For this reason, the monitoring of surface parameters, particularly soil moisture, is very important. This paper presents a new methodology for the estimation of surface soil moisture over Western Africa based on the data provided by the European Remote Sensing wind scatterometer 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 over the Banizambou site in Niger. Moisture and rainfall estimations for the same site are shown to be strongly correlated. Comparison with the multimodel analysis product provided by the Global Soil Wetness Project, Phase 2, indicates that their estimations are well correlated, although land surface models provide slightly overestimated levels of soil moisture.

The use of a theoretical backscatter model to analyse medium to low spatial resolution microwave data is still very complicated, particularly because of the difficulty in defining a unique roughness parameter, capable of adequately representing heterogeneous terrain. In this paper, an approach is proposed for roughness analysis and the modelling of backscattering, under conditions of surface heterogeneity. The proposed backscattering model has been validated with IEM (integral equation model) simulations and radar data , for high radar incidence angles, and within its domain of roughness validity.

The use of a theoretical backscatter model to analyze medium to low spatial resolution microwave data is still very complicated, particularly because of the difficulty in defining a unique roughness parameter, capable of adequately representing heterogeneous terrain. In this paper, an approach is proposed for roughness analysis and the modeling of backscattering, under conditions of surface heterogeneity. This paper is based on the use of a semiempirical backscattering model, defined with a single roughness parameter Zs=s2/l (s being the root mean square surface height and l the correlation length). The proposed backscattering model has been validated with integral equation model simulations, for high radar incidence angles, and within its domain of roughness validity. A range of experimental measurements was used to validate the model expressions. The effective low spatial resolution roughness, inferred from signals backscattered from a surface of heterogeneous roughness, is defined for different roughness classes

This paper is devoted to the comparison of high resolution (/spl ap/ 1 squared-meter pixels) images acquired in the solar (visible and near infrared) and radar domains over crops.

Natural soil surfaces present multiple, random roughness scales (or frequency components). Proper estimation of the correlation length, L, of a low frequency (LF) roughness component (typically L > 20 cm) requires excessively long profiles (>10 m). Consequently, usually applied sampling protocols were not suitable for an accurate determination of L. The objective of the study was to establish to which extent the low frequency component has a significant impact on the soil microwave backscattering. We implemented a model based on the Method of Moments (MM) to generate profiles presenting arbitrarily chosen multiple random roughness scales. It is shown that for angle of incidence lower than 40/spl deg/, adding a low frequency component with a component greater than 4 times th e wave length does not affect the MM results. If profiles of 2 meters are used, we obtained much better results when the low frequency is removed.

This paper discusses the potential of radar signal to characterise the bare surface roughness in arid or semi-arid regions. The used microwave sensor is the SAR of ERS. Ground truth measurements were acquired over different arid sites in the South of Tunisia. An empirical approach is proposed to derive the surface roughness from SAR measurements. The relationships with two different kinds of roughness have been studied: the geometric roughness, which is characterised by a rather new parameter called Zs, and the classical aerodynamic roughness Z/sub 0/.

This paper presents an original methodology to retrieve surface (< 5 cm) soil moisture over low vegetated regions using the two active microwave instruments of ERS satellite. The developed algorithm takes advantage of the multi-angular configuration and high temporal resolution of the Wind Scatterometer (WSC) combined with the SAR high spatial resolution. High correlations (R/sup 2/ greater than 0.8) are observed for three studied watersheds in France with an rms error smaller than 4% between real and retrieved moistures.

This paper presents a numerical computation of backscattering signal over simulated cloddy soil surfaces. A new generation approach of this type of surfaces with Monte Carlo method is proposed. The moment method is used to compute backscattering for these surfaces. The horizontal and vertical polarisation simulation levels and comparison with IEM model are discussed.

A new empirical model for the retrieval, at a field scale, of the bare soil moisture content and the surface roughness characteristics from radar measurements is proposed. The derivation of the algorithm is based on the results of 3 experimental radar campaigns conducted under natural conditions over agricultural areas. Radar data were acquired by means of several C-band space borne (SIR-C, RADARSAT) or helicopter borne (ERASME) sensors. This algorithm is more specifically developed using the radar cross-section /spl sigma//sup 0/ (HH polarization and 39/spl deg/ incidence angle off nadir), namely, /spl sigma//sub 0(HH,39)/, and the differential (HH polarization) radar cross-section /spl Delta//spl sigma//sub 0/=/spl sigma//sub 0,23/spl deg//-/spl sigma//sub 0,39/spl deg// in terms of an original roughness parameter, Zs, and Mv. An inversion technique is proposed to retrieve Zs and Mv from radar measurements.

Previous studies have shown the possibility of using European Remote Sensing/synthetic aperture radar (ERS/SAR) data to monitor surface soil moisture from space. The linear relationships between soil moisture and the SAR signal have been derived empirically and, thus, were a priori specific to the considered watershed. In order to overcome this limit, this study focused on two objectives. The first one was to validate over two years of data the empirical sensitivity of the radar signal to soil moisture, in the case of three agricultural watersheds with different soil compositions and land cover uses. The slope of the observed relationship was very consistent. Conversely, the offset could change, making the soil moisture retrieval only relative (and not absolute). The second one was to propose an "operational" methodology for soil moisture monitoring based on ERS/SAR data. The implementation of this methodology is based on two steps: the calibration period and the operational period. During the calibration period, ground truth campaigns are performed to measure vegetation parameters (to correct the SAR signal from the vegetation effect), and the ERS/SAR data is processed only once a field land cover map is established. In contrast, during the operational period, no vegetation field campaigns are performed, and the images are processed as soon as they are available. The results confirm the relevance of this operational methodology, since no loss of performance (in soil moisture retrieval) is observed between the calibration and operational periods.