Exploring vertical forest structures worldwide via remote sensing faces challenges. Recent technologies like waveform light detection and ranging (LiDAR) from NASA’s global ecosystem dynamics investigation (GEDI) and SAR tomography (TomoSAR) from future European Space Agency (ESA) BIOMASS offer promising solutions. This article assesses the performance of spaceborne GEDI and TomoSAR airborne data from an ESA’s TomoSense campaign to highlight the important role of GEDI measurements in BIOMASS algorithm training and establishing precise site-specific processing parameters. Our study in Germany’s Eifel National Park delves into the precision of GEDI and P-band TomoSAR in measuring surface [digital terrain model (DTM)] and vegetation [canopy height model (CHM)] heights. Results demonstrate that GEDI and P-band TomoSAR offer high-resolution and precise surface and vegetation heights and vertical profile measurements. While GEDI relative height (RH) at 98% (RH98) was previously recommended for tropical forests, our findings advocate for RH85 as the optimal metric for temperate forests. The research supports improving the accuracy of both DTM and CHM utilizing GEDI beams with full-power lasers coupled with high sensitivity and signal-to-noise ratio (SNR). Ground elevation measurements are more accurate than canopy height estimates for temperate forests, with DTM RMSE about 2 m and CHM RMSE about 3 m for GEDI and TomoSAR measurements. By analyzing the vertical structure of monthly GEDI data, we note a 1-m shift in the volume peak between GEDI’s leaf-on and leaf-off periods. At the same time, TomoSAR consistently exhibits a lower volume peak by about 2 m compared to GEDI during leaf-on seasons. In conclusion, our research underscores the complementary roles of TomoSAR and GEDI in accurately mapping diverse forest types, thereby bolstering the effectiveness of the BIOMASS mission.

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).

This paper evaluates GEDI's canopy height estimation accuracy in dense tropical forests located in Mayotte Island. It examines GEDI's ability to penetrate canopies and detect the ground, which is crucial for reliable estimates. The study tests the use of a single GEDI height metric (rh_95) in comparison with regression models using various GEDI metrics to enhance accuracy. Beam sensitivity plays a pivotal role, as it impacts significantly GEDI return waveforms and the subsequent derived height estimates. In the context of our study, GEDI tends to underestimate heights above 15 meters. Regression models outperform rh_95, mitigating the impact of beam sensitivity and canopy height (RMSE decreasing from 6.6 m to 5.5 m, bias going from -1.9 m to 0.0 m). They provide unbiased estimates, offering improved accuracies regardless of these factors. This study emphasizes GEDI's limitations and highlights regression models' potential to refine canopy height estimations in complex ecosystems where signal penetration is challenging.

This research presents a comprehensive canopy height map of French Guiana at 10 m spatial resolution, employing a data fusion approach integrating optical (Sentinel-2), radar (Sentinel-1 and ALOS), and ancillary data sources. The primary objective is to leverage a U-Net neural network model, trained and validated using Global Ecosystem Dynamics Investigation (GEDI) data as reference canopy height. We aim at understanding how canopy height prediction models can be improved through the integration of relevant remote sensing and environmental descriptors related to canopy structure. The accuracies of the generated canopy height maps are assessed against high-resolution airborne LiDAR (ALS) acquisitions conducted by the French National Forest Office. We observe that enriching input data with height above nearest drainage (HAND) as well as forest landscape information yielded improved accuracies for the prediction models. Moreover, accounting for GEDI database uncertainties, through filtering of usable waveforms and correction of geolocation errors, also resulted in a performance gain for canopy height estimation using a U-Net model.

Our study highlights the important role of GEDI measurements in BIOMASS algorithm training and the establishment of precise site-specific processing parameters. Combining GEDI measurements at sparse coordinates and SAR tomography (TomoSAR) estimates enables the creation of detailed canopy height maps (CHM). While relative height (RH) at 98% (RH98) was previously recommended for tropical forests, our findings advocate for RH85 as the optimal metric for temperate forests. Emphasis is placed on selecting shots with over 90% sensitivity for ground return detection and GEDI beams equipped with full-power lasers. Additionally, we show the GEDI profile data’s unique capacity to investigate annual changes, revealing significant volume contributions during leaf-on periods and increased ground importance during leaf-off seasons.

Following the 7.8 magnitude earthquake that struck Turkey and Syria on February 6, 2023, TomoSAR, an extensive software designed for SAR image processing, demonstrated its effectiveness in assessing land subsidence. It provided the initial three-dimensional displacement data, marking a significant milestone in this field. Notably, TomoSAR stands out as the first publicly accessible tool capable of jointly processing Persistent and Distributed Scatterers (https://github.com/DinhHoTongMinh/TomoSAR). Continual efforts are underway to elevate TomoSAR’s accessibility and performance. This involves integrating algorithms into a parallel version to facilitate enhanced performance and open avenues for complimentary scientific services at no cost.

This study explores the impact of Eucalyptus plantation characteristics and environmental factors on GEDI returned energy distribution. Random Forest (RF) regression was used to analyze the effect of a diverse parcel-scale Eucalyptus plantation characteristics including trees height, planting density, soil properties, understorey presence, environmental conditions and NDVI generated from Sentinel-2 as a proxy of leaf area index on the GEDI relative heights (RHn). According to the findings, as the vertical distance from the ground increases (from RH5 to RH100), the most important variable explaining a given relative height changes from "NDVI" to "Volume". Moreover, at higher quantiles of the returned energy, the behavior of the GEDI metrics becomes more dependent on a narrower set of forest and environmental characteristics. However, at low RH values, the interplay of complex canopy structures and environmental factors necessitates a combination of features to explain the observed variations.

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.

The Sentinel missions operational since 2015 for Sentinel 1 ((S1), radar C band, VV, VH polarizations) and 2016 for Sentinel 2 ((S2), optical and middle infrared range) provide data at high spatial and temporal resolution, well suitable for crop monitoring. Soil moisture products (SMP) developed from S1 and S2 using neural network techniques [1] are delivered at plot scale every 6 days via the Theia French public platform1. Up to now, these products were computed and validated mainly on cereals and grasslands. Mediterranean plots are often small and present a wide variability of agricultural practices. Among them, orchards, which require high quantify of water for irrigation are not represented in SMP because of their structure heterogeneity. The objective of our study was twofold: -i) to test SMP products for various Mediterranean crops (including orchards) with different agricultural management for 3 years, -ii) to analyze the correlation between soil moisture measurements (sm) and spectral indices derived from S1 and S2 to monitor the water status of cherry trees. Various fields of the Ouvèze basin in Southeastern France were monitored with different ground measurements. Results revealed (i) that differences appear between SMP products and soil moisture due to variability within farming systems. Beyond a specific slope and vegetation threshold, the correlation does not improve significantly; (ii) In orchards plots, different models using spectral indices from S1 and S2 were evaluated to estimate sm and showed significant correlations with ground measurements: R2=0.72 with an RMSE3/cm3.

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.

Quantification of forest’s vertical structure in the tropics using remote sensing is a challenge. NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne LiDAR data, whereas the ESA’s next Earth Explorer BIOMASS mission will acquire multiple acquisitions over the same areas to form three-dimensional images through SAR tomography (TomoSAR) technique. We show that GEDI and P-band TomoSAR can directly measure vegetation heights and vertical profiles with high resolution and precision. The GEDI vegetation height error is 5 m at the tropical sites, similar to the expected performance of the future spaceborne BIOMASS mission. These results suggest GEDI measurements, i.e., RH98 from full power shots with sensitivity greater than 98%, will provide a good reference of forest structure to calibrate the BIOMASS mission algorithms.

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.

Measuring the vertical structure of tropical forests using remote sensing technology is challenging. To overcome this, active sensors, such as P-band Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR), are used to penetrate thick vegetation layers. NASA’s Global Ecosystem Dynamics Investigation (GEDI) uses spaceborne LiDAR data. In contrast, the European Space Agency’s (ESA) BIOMASS mission uses multiple acquisitions of SAR data to create 3D images through a technique called SAR tomography (TomoSAR). The paper discusses the forest’s vertical structure, such as volume peak (or volume scattering center), penetration, and reflectivity, using GEDI and airborne P-band TomoSAR by analyzing measurements at tropical forest sites in South America and Africa. It was found that the location of the volume peak in TomoSAR is consistently lower than in GEDI, with a range of 2-4 m depending on the polarization and the height of the forest layers. Compared to GEDI, TomoSAR data has a better ground reflection for vegetation taller than 25 m. GEDI and TomoSAR data can accurately capture vertical information in the canopy levels (between 10-40 m), displaying a strong correlation in the volume layers. The highest correlation occurs around 30 m above ground level, aligning with previous research in developing algorithms for the BIOMASS mission in aboveground biomass retrieval. Together, TomoSAR and GEDI are robust and comparable in studying tropical forests and support the BIOMASS mission for global biomass mapping.

With the huge variety of earth observation satellite missions available nowadays, the collection of multi-sensor remote sensing information depicting the same geographical area has become systematic in practice, paving the way to the further breakthroughs in automatic land cover mapping with the aim to support decision makers in a variety of land management applications. In this context, along with the increase in the volume of data available, the availability of ground truth data to train supervised models, which is usually time-consuming and costly, may even be more critical. In this scenario, the possibility to transfer a model learnt on a particular time span (source domain) to a different period of time (target domain), over the same geographical area, can be advantageous in terms of both cost and time efforts. However, such model transfer is challenging due to different climate, weather or environmental conditions affecting remote sensing data collected at different time periods, resulting in possible distribution shifts between thesourceandtargetdomains. With the aim to cope with the multi-sensor temporal transfer scenario in the context of land cover mapping, where multi-temporal and multi-scale information are used jointly, we proposeM3SPADA(Multi-sensor, Multi-temporal and Multi-scale SPatially-Aware Domain Adaptation framework), a deep learning methodology that jointly exploits self-training and adversarial learning to transfer a multi-sensor land cover classifier from a time period (year) to a different one on the same geographical area. Here, we consider the case in which each domain (source and target) is described by a pair of remote sensing data sets: a satellite image time series (SITS) of optical images and a single Very High spatial Resolution (VHR) scene. Experimental evaluation on a real-world study case located in Burkina Faso and characterized by operational constraints shows the quality of our proposal to deal with the temporal multi-sensor transfer in the context of land cover mapping.

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.

In this study, water level estimates from the Global Ecosystem Dynamics Investigation Lidar (GEDI) were validated against in situ gauge station records over Lake Geneva. The performances of the first and second releases (respectively V1 and V2) of the GEDI data products were compared. The influence of the following parameters were analyzed: (1) the signal-over-noise ratio (SNR), (2) the width of water surface peak within the waveform (gwidth), (3) the amplitude of the water surface peak within the waveform (A), (4) the viewing angle of GEDI (VA), and (5) the acquiring beam. The comparison between V1 and V2 elevations showed that V2, overall, provided elevations with a more constant bias and fewer deviations to in situ data than V1. In addition, by choosing GEDI shots with VA ≤ 3.5°, the unbiased RMSE (ubRMSE) of GEDI elevations was 27.1 cm with V2 (r=0.66) and 42.8 cm with V1 (r=0.34). Results also show that the accuracy of GEDI (ubRMSE) does not seem to depend on the beam number and GEDI acquisition dates for the most accurate GEDI acquisitions (VA≤ 3.5°). Regarding the bias, a higher value was observed with V2 but with lower variability (54 cm) in comparison to V1 (35 cm). Finally, the bias showed a slight dependence on beam GEDI number.

International audience

Nowadays, Satellite Image Time Series (SITS) are employed as input to derive land cover maps (LCM) to support decision makers in several application domains like agriculture and biodiversity. The generation of LCM largely relies on available ground truth (GT) data to calibrate supervised ma-chine learning models. Unfortunately, this data are not always accessible. In this scenario, the possibility to transfer a model learnt on a particular year (source domain) to another period of time (target domain) could be a valuable tool to deal with the previously mentioned restrictions. In this paper, we provide an experimental evaluation of recent Unsupervised Domain Adaptation (UDA) methods in the specific context of temporal transfer learning for SITS-based LCM. The objective is to learn a classification model at a certain year (exploiting available GT data) and, successively, transfer such a model on a subsequent year where no labelled samples are accessible. The obtained findings reveal that UDA methods represent a promising research direction to cope with the problem of temporal transfer learning for LCM. While a model learnt on the source data and directly applied on target data achieves an weighted F1-score of 67.1, the best UDA method obtains an F1-score of 83.7 with more than 15 points of positive gap. Nevertheless, there is still room for improvement that should be explored in future works.

The Global Ecosystem Dynamics Investigation (GEDI) instrument, as all FW systems, relies on very sophisticated pre-processing steps to generate a priori metrics in order to accurately estimate forest characteristics, such as forest heights and wood volume. The ever-expanding volume of acquired GEDI data, which to September 2020 comprised more than 25 billion shots, and requiring more than 90 TB of storage space, raises new challenges in terms of adapted preprocessing methods for the suitable exploitation of such a huge and complex amount of LiDAR data. Therefore, to avoid metric computation, we leveraged deep learning techniques in order to estimate canopy dominant heights (Hdom) and wood volume (V) of Eucalyptus plantations over five different regions in Brazil. Performance comparisons were conducted between a convolutional neural network based model that uses GEDI waveform data and a previously used, metric based, Random Forest regressor (RF). Cross-validated results showed that the CNN based model compared well against the RF counterpart for both Hdomand V. Indeed, the RMSE on the estimation of Hdomfrom the CNN based model was 1.61 m with a coefficient of determination R2of 0.90, while the RF model produced an accuracy on Hdomestimates of 1.45 m(R2=0.92). For V, CNN based estimates was 27.35 m3.ha-1(R2of 0.88), while for RF, the RMSE was 27.60 m3.ha-1 (R2=0.88).

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.

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.

Estimating tropical forests vertical structure using remote sensing is a challenge. Active sensors such as low-frequency Synthetic Aperture Radar (SAR) operating at P-band, with a wavelength of ~ 69 cm wavelength, and Light Detection and Ranging (LiDAR) are able to penetrate thick vegetation layers. While NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne liDAR data, the ESA’s next Earth Explorer BIOMASS mission will acquire multiple acquisitions over the same areas to form three-dimensional images through SAR tomography (TomoSAR) technique. Our study shows the potential value of GEDI and TomoSAR acquisitions in producing accurate estimates of forests vertical structure. By analyzing airborne P-band TomoSAR, airborne LiDAR, and spaceborne GEDI LiDAR at a tropical forest site in Paracou, French Guiana, South America, we show that both GEDI and P-band TomoSAR can directly measure surface, vegetation heights, and vertical profiles with high resolution and precision. Airborne TomoSAR is of higher quality than GEDI due to better penetration properties and precision. However, the GEDI vegetation height root-mean-square error is less than 5 m, for an average forest height value around 30 m at the Paracou site, which is similar to the expected performance of the future spaceborne BIOMASS mission. These results suggest GEDI measurements, i.e. shots with sensitivity greater than 98%, will provide a good reference of forest structure to calibrate the BIOMASS mission algorithms.

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.

Radar altimetry is commonly used to derive water levels over inland water bodies. If lakes and rivers are increasingly covered with radar altimetry virtual stations, wetlands and floodplains are still poorly monitored using this technique. In this study, an unsupervised classification of Ku-band radar altimetry backscattering coefficients from ENVISAT and Jason-2 is performed in the Congo Cuvette Centrale. Comparisons performed against a classification map of the study area shows a good agreement between the water and vegetation classes of the two datasets. Based on these results, radar altimetry-derived water levels are automatically derived over the water classes. Comparisons against radar altimetry-based water stages from Hydroweb database exhibits also a good agreement.

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.

Vegetation optical depth (VOD) is used to parameterize microwave extinction effects within the vegetation layer. Many studies have showed VOD presents interesting features for applications in ecology, water and carbon cycles, and VOD is only marginally impacted by signal disturbances and artefacts from atmospheric, cloud and sun illumination effects. As soil moisture (and not VOD) has generally been the main factor of interest in retrieval studies from microwave observations, there is room for improvement in the retrieved VOD products. In this context, INRAE Bordeaux recently developed alternate VOD products from the SMOS, AMSR2 and ASCAT sensors, by addressing specifically the ill-posed problem of retrieving both SM and VOD from observations which may be strongly cross-correlated. Promising results were obtained particularly in terms of spatial correlation of these alternate VOD indices with biomass.

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.

This paper addresses the monitoring of volcanic eruption using a coherence change detection matrix constructed from a multitemporal InSAR image time series. The Piton de la Fournaise volcano (French island, La Reunion), one of the most active volcanoes worldwide, was selected as a case study. Changes on the ground related to eight volcanic eruptions were analyzed through a time series including 49 descending stripmap Sentinel-1 SAR images acquired from January 10, 2018 to August 21, 2019. The experimental results have shown the relevancy of the proposed framework.

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.

The objective of this paper is to evaluate the potential of radar images to study wetland areas on the mapping flood regime and generating digital elevation model. The analysis is carried out on Sentinel-1 data acquired over the Congo Basin.

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 assessment of yield variability at the territory scale is often difficult due to the lack of knowledge on various factors involved, e.g., the agricultural practices of farmers and phenological calendars. Remote sensing can help to provide precise and timely information on crops particularly with the unprecedented amount of free Sentinel data within the Copernicus programme. This study focuses on the evaluation of the contribution of the new Sentinel 2 data to provide phenological information on rice cropping systems in the Camargue region in the South-Eastern France. Dense time series of data acquired at high spatial resolution (10m) were analyzed for 2016 and 2017 and were used to map rice, compute Leaf Area Index. Various methods combining remote sensing information were compared with two different crop models (STICS and SAFY) to assess the yields. The performances are discussed according to surveys made with farmers.

In the global change context, an efficient management of the available resources has become one of the most important topic particularly for sustainable crop development. Many questions concern the evolution of the rice farming systems in Camargue, in the Southeastern France, which play a crucial role to control the soil salinity and whose the surfaces significantly decreased these last years from 20 000 ha in 2010 to around 14000 ha in 2014. The arrival of the new satellites Sentinel makes it possible to evaluate these crop evolutions. The objectives of this study were to propose operational methodologies to: 1) accurately assess the surfaces of the main crops, rice, wheat and grassland in 2016 and 2017 from classifications based on multispectral data, (2) map some agricultural practices (sowing and harvest residue burning), and (3) elaborate a farm typology for the Camargue region based on variables computed from remote sensing data to better understand the farmer strategies.

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.%.

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.

Landscape heterogeneity is a key factor for understanding ecosystems function and is often associated to high biodiversity. Yet, its characterization for biodiversity monitoring still remains challenging, especially for very heterogeneous landscapes mosaics. In this work, we study the potential of Fourier-based Textural Ordination (FOTO) applied on high resolution airborne optical images to provide synthetic information about landscape fragmentation of Mediterranean heterogeneous landscapes. The ability of texture indices resulting from FOTO combined with vegetation indices to predict landscape metrics was also tested using non linear SVM regression. Our research showed that FOTO methods had great potential to finely characterize different vegetation strata organization at large scale with only few synthetic indices. Best regression results were obtained for ligneous and ground strata fragmentation with coefficient of determination greater than 0.7.

This paper presents tomographic analysis using L-band NASA/JPL UAVSAR from AfriSAR data conducted over the Gabon Lope Park on February 2016. Prior to tomographic imaging, a phase residual correction methodology based on Sum Kronecker Product have been implemented. The estimated vertical structure of the forest extracted from the correct tomographic data is validated with small footprint light detection collected during the AfriSAR campaign on July 2015. The result demonstrates that L-band tomographic imaging can now be carried out even in the dense tropical forest.

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).

This study aimed to assess the potential of GLAS (Geoscience Laser Altimeter System) LiDAR data to overcome the saturation at high AGB values of existing AGB map on Madagascar (Vieilledent's AGB map [1]). First, spatially distributed estimations of AGB were obtained from GLAS data. Second, the difference between the Vieilledent's AGB map and GLAS derived AGB at each GLAS footprints location was calculated and a spatially distributed additional correction factors were obtained. Thanks to the spatial structure of these additional correction factors, an ordinary kriging interpolation was thus performed to provide a continuous correction factor map. Finally, the existing and the correction factor map were summed to improve the Vieilledent's AGB map. Results showed that the integration of GLAS data overcome the saturation at high AGB of Vieilledent's AGB map and allow AGB estimation until 650 t/ha (maximum AGB values from Vieilledent AGB map was 550 t/ha).

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.

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.

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.

LiDAR remote sensing has been shown to be a good technique for the estimation of forest parameters such as canopy heights and aboveground biomass. Whilst airborne LiDAR data are in general very dense but only available over small areas due to the cost of their acquisition, spaceborne LiDAR data acquired from the Geoscience Laser Altimeter System (GLAS) have a coarser acquisition density associated with a global cover. It is therefore valuable to analyze the integration relevance of canopy heights estimated from LiDAR sensors with ancillary data such as geological, meteorological, and phenological variables in order to propose a forest canopy height map with good precision and high spatial resolution. In this study, canopy heights extracted from both airborne and spaceborne LiDAR, were first extrapolated from available environmental data. The estimated canopy height maps using random forest (RF) regression from the airborne or GLAS calibration datasets showed similar precisions (RMSE better than 6.5 m). In order to improve the precision of the canopy height estimates regression-kriging (kriging of RF regression residuals) was used. Results indicated an improvement in the RMSE (decrease from 6.5 to 4.2 m) for the regression-kriging maps from the GLAS dataset, and from 5.8 to 1.8 m for the regression-kriging map from the airborne LiDAR dataset.

Since Lidar technology provides the most direct measurements of 3D of phenomena, it plays a critical role in a variety of applications. Forest canopy height as a main factor in forest biomass estimation is costly and time consuming to be measured on the ground. This study aims to estimate Lorey's height “Hlorey” using GLAS data based on regression models. Different metrics like waveform extent “Wext”, trail-edge extent “Htrail” and lead-edge extent “Hlead” were extracted from waveforms and a terrain index “TI” was also calculated using a digital elevation model. Hloreyestimated using multiple regression models were compared to field measurements data. A 5-fold cross validation method was used to validate the results. Best model with lowest AIC (297.440) was resulted using combination of Wextand TI (Ra2=0.72; RMSE= 5.04m). The results show capability of ICESat-GLAS to estimate Lorey's height in sloped area with a simple regression model. It is prospected to reach better result using other statistical methods and also improvement of processing techniques for LiDAR waveforms in the case of sloped terrain.

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 Geoscience Laser Altimeter System (GLAS) has provided a useful dataset for estimating forest height in many areas of the globe. Most of the studies on GLAS waveforms have focused on natural forests and only a few were conducted over forest plantations. The objective of this study was to test the best known models used for estimating canopy height and above ground biomass of intensively managed Eucalyptus plantations in Brazil using full waveform LiDAR data. Studies to estimate forest heights from LiDAR data have highlighted that the fitting coefficients of developed models are strongly dependent on environmental factors such as the region of the study site, terrain topography, and forest type. In this study, we evaluated the main models developed to predict canopy height using a combination of parameters extracted from GLAS waveforms and a digital elevation model, in order to explore which combination of parameters yields the best forest height estimates. In addition, a model to estimate above ground biomass from dominant height was calibrated.

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.

In this study, the canopy height estimation over French Guiana was analyzed using multiple linear regressions and the Random Forest technique (RF). This analysis was based on LiDAR waveform metrics extracted from the GLAS (Geoscience Laser Altimeter System) spaceborne LiDAR and terrain information derived from the SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model). Results showed that the use of statistical models based on GLAS waveforms and DEM metrics provides better canopy height estimates in comparison to that obtained by the direct method (RMSE between 3.7 and 4.9 m against 7.9 m with the direct method). The best statistical model is defined as a linear regression of waveform extent, trailing edge extent, and terrain index. Random Forest regressions showed that the waveform extent was the variable that best explained the canopy height. In addition, the estimation of GLAS canopy height by RF using only the waveform extent showed an RMSE of 4.4 m. The best configuration for canopy height estimation using RF used all the metrics: waveform extent, leading edge, trailing edge, and terrain index (RMSE=3.4 m). In our case of low relief area, the use of one or two metrics among the three used in this study in addition to the waveform extent showed a slightly lower precision on the canopy height estimation (RMSE=3.6 m). In conclusion, multiple linear regressions and RF regressions provided similar precision on the canopy height estimation.

In this study, waveforms acquired by the Geoscience Laser Altimeter System (GLAS) were combined with SRTM elevations to discriminate the five forest landscape types (LTs) in French Guiana. Two differences were calculated: (1) penetration depth, defined as the GLAS highest elevations minus the SRTM elevations, and (2) the GLAS centroid elevations minus the SRTM elevations. The results show that these differences were similar for the five LTs, and they increased as a function of the GLAS canopy height and of the SRTM roughness index. Next, a Random Forest (RF) classifier was used to analyze the coupling potential of GLAS and SRTM in the discrimination of forest landscape types in French Guiana. Results showed an overall classification accuracy of 81.3% and a kappa coefficient of 0.75. All forest LTs were well classified with an accuracy varying from 78.4% to 97.5%. Finally, differences of near coincident GLAS waveforms, one from the wet season and one from the dry season, were also analyzed. Results indicated that forests that lose leaves during the dry season were easily discriminated from the other LTs that retain their leaves.

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.

LiDAR bathymetric biases due to geometric changes at the air-water and water-bottom interfaces are investigated based on calculations made with a modified version of the waveform simulator Wa-LID. Main assumptions include a homogeneous water column and a spaceborne LiDAR having a footprint smaller than 50 m and a wavelength centered at 532 nm. Preliminary results showed major temporal modifications on second Lidar return (up to 100 cm or 10 ns) due to tilted bottoms. This shift was in average >6-fold the maximum bottom depth bias originated from capillary waves forming at the air-water surface.

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.

A new approach based on a mixture of Gaussian and quadrilateral functions was developed to process bathymetric lidar waveforms. The approach was tested on two simulated data sets obtained from the existing Water-LIDAR (Wa-LID) waveform simulator. The first simulated data set corresponds to a sensor configuration modeled after a possible future satellite bathymetric lidar sensor that was previously studied. The second simulated data set corresponds to a lidar airborne configuration modeled using the HawkEye airborne lidar parameters. In the proposed approach, the lidar waveform is fitted into a combination of three functions, two Gaussians for both the water surface and water bottom contributions and a quadrilateral function to fit the water column contribution. The results show more accurate bathymetry estimates compared with the use of a triangular function to fit the column contribution or a simple peak detection method. For the satellite configuration, the bias is improved by 16.8 and 0.8 cm compared with the peak detection method and the use of a triangular function, respectively. For the airborne configuration, the bias is improved by 10.0 and 2.4 cm compared with the peak detection method and the use of a triangular function, respectively. The proposed waveform fitting using the quadrilateral function underestimates the bathymetry by$-$5.0 and$-$6.1 cm for the simulated satellite and airborne data sets, respectively. The standard deviations of the bathymetry estimates are 6.0 and 8.2 cm, respectively. The obtained biases are inherent to overlaps between functions fitting the water surface, column, and bottom contributions.

Bathymetry is usually determined using the positions of the water surface and the water bottom peaks of the green LiDAR waveform. The water bottom peak characteristics are known to be sensitive to the bottom slope, which induces pulse stretching. However, the effects of a more complex bottom geometry within the footprint below semitransparent media are less understood. In this letter, the effects of the water bottom geometry on the shifting of the bottom peaks in the waveforms were modeled. For the sake of simplicity, the bottom geometry is modeled as a 1D sequence of successive contiguous segments with various slopes. The positions of the peaks in waveforms were deduced using a conventional peak detection process on simulated waveforms. The waveforms were simulated using the existing Wa-LID waveform simulator, which was extended in this study to account for a 1D complex bottom geometry. An experimental design using various water depths, bottom slopes, and LiDAR footprint sizes according to the design of satellite sensors was used for the waveform simulation. Power laws that explained the peak time shifting as a function of the footprint size and the water bottom slope were approximated. Peak shifting induces a bias in the bathymetry estimates that is based on a peak detection of up to 92% of the true water depth. This bias may also explain the frequent underestimation of the water depth from bathymetric airborne LiDAR surveys observed in various empirical studies.

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.

This paper gives an overview of French studies realized in the frame of the CNES (French Space Agency) working group on spaceborne lidar missions. These studies include (1) the development of forest scenery and radiative transfer models for the simulation of lidar waveforms under forest cover, (2) preliminary instrumental studies to ensure the feasibility of the scientific requirements and (3) evaluation and improvement of inversion methods to retrieve forest parameters from large footprint lidar data.

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.

This letter aimed to validate the advanced integral equation model (AIEM) through different correlation length parameterizations using radar imagery for field-scale studies in a semi-arid environment. This letter compared backscattering coefficients simulated from the AIEM and retrieved from the synthetic aperture radar imagery of a study site in Sardinia. Two treatments for the correlation length were adopted, i.e., in situ measurements and empirically based correlation length estimation. The results showed an overestimation of backscattering coefficients of 2.5 dB with a root mean square error (RMSE) of 3.1 dB for HH and VV polarizations and an underestimation of 27.7 dB and an RMSE of 31.0 dB for HV polarization from the AIEM parameterized by in situ measurements. When using the AIEM with an empirical correlation length, a bias of less than 1.0 dB was found with an RMSE of 1.7 dB for HH and VV polarizations and an overestimation of 1.1 dB and an RMSE of 5.1 dB for HV polarization. Better results were obtained with surface soil moisture (SSM) measured at 10 cm than at 5 cm. Promising soil moisture data retrieval from the SAR imagery is expected from using the empirical correlation length-parameterized AIEM for field-scale purposes in semi-arid environments.

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.

This contribution explores the potential of high-resolution satellite SAR imagery (TerraSAR-X, ALOS-PALSAR) for mapping coastal habitats in complement of optical data (SPOT-5). It addresses X- and L-band SAR signatures over intertidal flats and coastal salt-marshes by investigating the mean backscattering coefficient σ0over the major environmental units and its variation associated with physical parameters (soil roughness, soil moisture, tidal inundation) and instrumental configurations. The major findings outline a great potential of TerraSAR-X data to detect oyster beds and discriminate Zostera noltii seabed from salt-marsh vegetation species. Based on these results, a multi-sensor multi-temporal mapping strategy was tested running simple supervised classification algorithms. These tests show that mapping performance is greatly enhanced when running Mahalanobis classifier on a 6-band concatenated image. Further work is needed to account with green macro-algae deposits and microphytobenthos in order to demonstrate the full capabilities of spaceborne sensors to provide an exhaustive mapping of intertidal environments.

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.

A simulator (Wa-LiD) was developed to simulate the reflection of LiDAR waveforms from water across visible wavelengths. The specific features of the simulator include 1) a geometrical representation of the water surface properties; 2) the use of laws of radiative transfer in water adjusted for wavelength and the water's physical properties; and 3) modeling of detection noise and signal level due to solar radiation. A set of simulated waveforms was compared with observed LiDAR waveforms acquired by the HawkEye airborne and Geoscience Laser Altimeter System (GLAS) satellite systems in the near infrared or green wavelengths and across inland or coastal waters. Signal-to-noise ratio (SNR) distributions for the water surface and bottom waveform peaks are compared with simulated and observed waveforms. For both systems (GLAS and HawkEye), Wa-LiD simulated SNR conform to the observed SNR distributions. Moreover, Wa-LiD showed a good ability to reproduce observed waveforms according to some realistic water parameters fitting.

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.

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.

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.

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.

Compacted tractor wheel tracks have been recognized as key factors controlling runoff and erosion processes in agricultural landscapes. In this context, the availability of an automatic tool for wheel tracks detection and characterization would be very useful. In this letter, an original algorithm based on Radon Transform is proposed for automatic wheel tracks detection on Synthetic aperture radar images with a spatial resolution of 1 m. Compared to on-screen measurements, wheel tracks orientations and widths were accurately estimated for the images acquired with shallow incidence angles whereas poorer results were observed for sharp incidence angles.

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.

This paper presents the use of TerraSAR-X images m the context of the Kalideos programme, which aims at providing the user community with time series of multi-spectral (optical and radar) and multi-resolution remote sensing imagery. 120 TerraSAR-X acquisitions are scheduled for three distinct thematics: volcano monitoring (Reunion island site), sugarcane crop monitoring (Reunion island site) and forest monitoring (Arcachon/Landes forest site). We describe here the advancement of these studies, focusing on sugarcane crop monitoring which is the most advanced one. These studies will serve as typical examples of multi-thematic use of TerraSAR-X imagery and demonstrate the relevance of TerraSAR-X imagery for the development of scientific reference datasets.

The advent of a new generation of synthetic aperture radar (SAR) satellites, such as Advanced SAR/Environmental Satellite (C-band), Phased Array Type L-band Synthetic Aperture Radar/Advanced Land Observing Satellite (L-band), and TerraSAR-X (X-band), offers advanced potentials for the detection of urban tissue. In this letter, we analyze and compare the performance of multiple types of SAR images in terms of band frequency, polarization, incidence angle, and spatial resolution for the purpose of operational urban areas delineation. As a reference for comparison, we use a proven method for extracting textural features based on a Gaussian Markov Random Field (GMRF) model. The results of urban areas delineation are quantitatively analyzed allowing performing intrasensor and intersensors comparisons. Sensitivity of the GMRF model with respect to texture window size and to spatial resolutions of SAR images is also investigated. Intrasensor comparison shows that polarization and incidence angle play a significant role in the potential of the GMRF model for the extraction of urban areas from SAR images. Intersensors comparison evidences the better performances of X-band images, acquired at 1-m spatial resolution, when resampled to resolutions of 5 and 10 m.

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 presents a two-step algorithm to perform an unsupervised extraction of line networks from satellite images, within a stochastic geometry framework. First, we propose a new operator, providing a measure of the possibility of linear structure presence on each image pixel. Second, we propose a Bayesian model in order to extract the line network from the operator output. The prior model, a Markov object process, incorporates the topological properties of the network through interactions between objects, while the line operator answers are taken into account in the likelihood. Optimization is realized by simulated annealing using a reversible jump Monte Carlo Markov chain algorithm. An application to hydrographic network extraction is presented.

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.

This paper describes a geostatistical method for the generation of an improved accuracy digital elevation model (DEM) through the fusion of airborne laser altimeter data and a stereo-radargrammetric DEM. The results show that the accuracy of the DEM (canopy-top and ground-level) after data fusion is significantly better than that of the initial radargrammetric DEM. For the canopy-top DEM, the standard deviation of elevation errors falls from 21 m to 14 m or from 25 m to 11 m, depending on the validation source adopted.

We present the capabilities of low-frequency radar systems to sound the subsurface for a site located in south-central Egypt, the Bir Safsaf region. This site was already intensively studied since the SIR-A and SIR-B orbital radars revealed buried paleodrainage channels. Our approach is based on the coupling between two complementary radar techniques: the orbital synthetic aperture radar (SAR) in C and L bands (5.3 and 1.25 GHz) for imaging large-scale subsurface structures, and the ground-penetrating radar (GPR) at 500 and 900 MHz for sounding the soil at a local scale. We show that the total backscattered power computed from L-band SAR and 900-MHz GPR profiles can be correlated, and we combined both data to derive the geological structure of the subsurface. GPR data provide information on the geometry of the buried scatterers and layers, while the analysis of polarimetric SAR data provides information on the distribution of rocks in the sedimentary layers and at the interface between these layers. The analysis of 500-MHz GPR data revealed some deeper structures that should be detected by lower frequency SARs, such as a P-band system.

We consider the problem of multi-channel image classification. We take into account not only the radiometric information but also some textural information. The proposed algorithm is a particular case of a fission-fusion scheme. The fission step consists of defining some textural parameters and extracting them from the different channels. The fusion between the texture channels and the original radiometric channels is performed in a second step. We consider a supervised scheme in which some training areas are given. These training areas allow us to define the class parameters and to drive the fusion process. Some results are given on SIR-C images.

The retrieval of surface parameters demands the use of well calibrated models and unfortunately, none of the existing models provide consistently good agreement with the measured data. The overall objective of this paper is to propose a semi-empirical calibration of the Integral Equation Model (IEM) so as to better reproduce the backscattering coefficient measured from SAR images over bare soils. As correlation length is not only the least accurate parameter but also the most difficult to measure, we propose its empirical estimation from an experimental data set of SAR images and soil parameter measurements. Based on a first data set, relationships between optimal correlation length and rms height were found for each radar configuration. The IEM was then tested on another set of measured data in order to validate the calibration procedure. The new calibrated version of the IEM, corresponding to the original IEM (Fung, 1994) with a coupling of the empirical function of correlation length, shows a very good agreement with the backscattering measurements provided by spaceborne SAR systems.

The authors investigate the penetration capabilities of microwaves, particularly at L-band, for the mapping of subsurface heterogeneities such as lithology variations, moisture or sedimentary structures. The experiment site, the Pyla Dune, is a bare sandy area allowing high signal penetration and presenting large subsurface structures (paleosoils) at varying depths. Several radar data sets over this area are available. A polarimetric analysis of airborne synthetic aperture radar (SAR) data as web as the ground penetrating radar (GPR) sounding experiment show that subsurface scattering occurs at several places. The SAR penetration depth is estimated by inverting a scattering model for which the subsurface structure geometric and dielectric properties are determined by the GPR data analysis. These results suggest that airborne radar systems in a lower frequency range (P-band) should be able to detect subsurface moisture down to several meters, leading to innovative Earth observation systems for hydrogeology in arid regions.