Most current on-board raw SAR data reduction techniques are based on adaptive quantization. Next-generation SAR systems will be drastically more data intensive, with increased swath sizes, improved spatial resolution and multi-frequency acquisitions. Thus, new data compression approaches that significantly decrease the bits to be downlinked as well as stored on-board are paramount. Neural data compression is an emerging scheme where the encoder, decoder and entropy model are learned simultaneously and tailored for a given dataset. In this work, we apply this to raw SAR data from the Sentinel-1 stripmap mode. We utilize convolutional neural networks to represent the analysis and synthesis transforms and learn an entropy model such that the data are encoded with low bit-rate. Preliminary results on this scenario enable a significant reduction in the average bits-per-sample utilized, while still maintaining high quality reconstructions with respect to amplitude and phase metrics. This illustrates the great potential of neural data compression for raw SAR data towards more efficient data systems.

We present two novel experimental setups for automated measurement of coherent backscatter enhancement (also known as the coherent backscatter opposition effect – CBOE) of Ku-band radio waves in seasonal snow cover. The experimental setups were developed in order to enable fast and repeatable measurements of the effect’s properties, through quickly altering the bistatic baseline of the radar system between zero and eight meters. The experimental setups were tested in December 2023 at the High Altitude Research Station Jungfraujoch in Switzerland, yielding a novel dataset exploring the properties of the early-winter snow cover on top of the Great Aletsch Glacier, as well as allowing a comparison of the benefits and drawbacks of the two experimental setups. We present preliminary results of these experiments, as well as discuss the potential of such measurements for improving our understanding of radio wave scattering in snow.

This study focuses on the retrieval of soil moisture (SMC) and forest Aboveground Biomass (AGB), and on the classification of fire disturbances in forests by using the NASA’s Cyclone GNSS (CyGNSS) data over land. Retrieval and classification algorithms, based on machine learning (ML) techniques, as Supported vector machines (SVM), Artificial Neural Networks (ANN) and Random Forests are implemented and validated against reference data from in-situ measurements and EO products.The research, which was carried out in the framework of two ESA project, has the twofold aim of further assessing the potential of GNSS-R for land applications and of defining retrieval concepts to be applied to the ESA’s SCOUT 2 HydroGNSS satellite mission.

The GNSS reflectometry (GNSS-R) potential for the monitoring of hydrological parameters as soil moisture (SM) and forest aboveground biomass (AGB) has been largely proved in recent years. In this study, algorithms based on Artificial Neural Networks (ANN) have been developed for the retrieval of both SM and AGB from GNSS-R observations. This activity has been carried out in view of the ESA's HydroGNSS mission. Waiting for HydroGNSS data, the algorithms have been implemented and validated by using the NASA's Cyclone GNSS (CyGNSS) land observations, confirming a promising potential of GNSS-R for the monitoring of both SM and AGB.

The modular Snow Microwave Radiative Transfer (SMRT) model simulates microwave scattering behavior in snow via different selectable theories and snow microstructure representations, which is well suited to intercomparisons analyses. Here, five microstructure models were parameterized from X-ray tomography and thin-section images of snow samples and evaluated with SMRT. Three field experiments provided observations of scattering and absorption coefficients, brightness temperature, and/or backscatter with the increasing complexity of snowpack. These took place in Sodankylä, Finland, and Weissfluhjoch, Switzerland. Simulations of scattering and absorption coefficients agreed well with observations, with higher errors for snow with predominantly vertical structures. For simulation of brightness temperature, difficulty in retrieving stickiness with the Sticky Hard Sphere microstructure model resulted in relatively poor performance for two experiments, but good agreement for the third. Exponential microstructure gave generally good results, near to the best performing models for two field experiments. The Independent Sphere model gave intermediate results. New Teubner–Strey and Gaussian Random Field models demonstrated the advantages of SMRT over microwave models with restricted microstructural geometry. Relative model performance is assessed by the quality of the microstructure model fit to micro-computed tomography (CT) data and further improvements may be possible with different fitting techniques. Careful consideration of simulation stratigraphy is required in this new era of high-resolution microstructure measurement as layers thinner than the wavelength introduce artificial scattering boundaries not seen by the instrument.

The estimation of aboveground biomass is commonly recognized for global relevance because of the vegetation role in the carbon cycle. Both active and passive microwave sensors can significantly contribute to this goal because of their high sensitivity to water content and high penetration at lower frequencies (L-/P-bands). In particular, Global Navigation Satellite Systems (GNSSs) are recently receiving increasing interest as source of opportunity to be employed as illuminator for L-band remote sensing, since they could provide low-cost sensors for nondestructive forest biomass estimation over large areas. In this article, we suggest a method to extract forest information using the GNSS direct signals collected in clear sky and below the vegetation canopy at both circular polarizations. An experimental campaign, carried out in the framework of an European Space Agency (ESA) project, was conducted over three poplar forests with different biomass to verify the feasibility of this technique. The relationships between the GNSS measurements and the tree parameters were first assessed and then interpreted and supported by statistical analysis and a theoretical model. The signal collected under the canopy is affected by attenuation and depolarization with respect to the one collected in open air, and this article demonstrated that both direct line-of-sight propagation and volume scattering play a role in the signal magnitude and its fluctuation in time. Although the experimental data set is limited in size and environmental conditions, two inversion algorithms were also tested with the encouraging retrieval results.

Biomass is a spaceborn polarimetric P-band (435 MHz) synthetic aperture radar (SAR) in a dawn-dusk low Earth orbit. Its principal objective is to measure biomass content and change in all the Earth's forests. The ionosphere introduces the Faraday rotation on every pulse emitted by low-frequency SAR and scintillations when the pulse traverses a region of plasma irregularities, consequently impacting the quality of the imaging. Some of these effects are due to total electron content (TEC) and its gradients along the propagation path. Therefore, an accurate assessment of the ionospheric morphology and dynamics is necessary to properly understand the impact on image quality, especially in the equatorial and tropical regions. To this scope, we have conducted an in-depth investigation of the significant noise budget introduced by the two crests of the equatorial ionospheric anomaly (EIA) over Brazil and Southeast Asia. This paper is characterized by a novel approach to conceive a SAR-oriented ionospheric assessment, aimed at detecting and identifying spatial and temporal TEC gradients, including scintillation effects and traveling ionospheric disturbances, by means of Global Navigation Satellite Systems ground-based monitoring stations. The novelty of this approach resides in the customization of the information about the impact of the ionosphere on SAR imaging as derived by local dense networks of ground instruments operating during the passes of Biomass spacecraft. The results identify the EIA crests as the regions hosting the bulk of irregularities potentially causing degradation on SAR imaging. Interesting insights about the local characteristics of low-latitudes ionosphere are also highlighted.

The interest on aboveground biomass measurements is raised from its relation with the understanding of the carbon cycle. This paper proposes a method for the estimation of aboveground biomass (AGB) exploiting the interaction of L-band electromagnetic waves with forest vegetation. The proposed method uses the Global Navigation Satellite Systems (GNSS) direct signals in clear sky and below the vegetation to extract the attenuation and de-polarization versus satellite elevation. An experimental campaign was conducted over three forests with different level of biomass. The proposed inversion algorithm is based on artificial neural networks showing a correlation with ground truth above 96%.

This paper deals with a ground experiment based on a P-band scatterometer that measured the evolution of the radar cross section (RCS) and coherence of a temperate tree in HV polarization, during four periods spread over nine months, over a cedar tree. Watering of the tree has limited influence in time on the RCS, i.e., limited to around 30 min, but impacts the coherence over a longer period of time. Analysis of the series shows that according to the season considered, clear daily cycles from 1 to 2 dB may appear on the coherence only (autumn) or on both coherence and RCS (spring), whereas in winter, they are absent on both results. It was analyzed in the literature that the variations in RCS are strongly correlated to the variations in the dielectric constant in trunks and branches. In addition, it was shown that the HV RCS presents seasonal trends with a yearly cycle of roughly 3 dB following similar trends reported for trunk moisture content time series.

SAOCOM is an Argentinian L band system formed by two satellites (SAOCOM-1A and SAOCOM-1B). ESA is investigating the possible applications of a companion satellite (SAOCOM-CS) carrying a passive receiver working in concert with one of the SAOCOM-1 satellites. During the mission there will be cycles with a long along-track bistatic baseline, suitable for deformation monitoring. Together with the combination of ascending and descending orbits, this geometry will produce four measurements from different viewing geometries, enabling us to estimate the 3D motion vector. Here we investigate the sensitivity of such a configuration, and the opportunities for increasing the density of persistent scatterers.

This paper aims at identifying suitable geometrical configurations of a passive satellite radar flying in convoy with an active spaceborne SAR. The work has been performed in the frame of the SAOCOM-CS scientific investigations. It is a small satellite that ESA is conceiving to fly in convoy with the Argentinian SAOCOM 1B to acquire bistatic radar data at L-band. The applications foreseen in the paper are the retrieval of soil moisture and crop biomass. It is shown by a model based investigation that retrieval performances can be improved by combining monostatic and bistatic measurements in geometric configurations requiring very high along track and across track baselines.

This paper reviews the simulation tool developed to predict the GNSS-R signal over land (bare soil and vegetation). The tool is presently being used to test the capability to retrieve the main land parameters, namely soil moisture and vegetation biomass. An ongoing research is using the simulations to assess a multistatic concept which aims to take advantage of the combination of GNSS-R and radar backscattering data provided by a SAR or the GNSS receiver itself. Preliminary performance assessments are presented.

Very recently, it has been observed that GNSS-R can provide a significant contribution to agricultural and forestry applications, since the use of GNSS signals as sources of opportunity enables bistatic radar measurements at L-band, which showed to be sensitive to soil moisture and vegetation parameters. This perspective has been investigated in two experimental activities funded by the European Space Agency: the LEiMON and GRASS campaigns. This work has been carried out with the aim of interpreting the data collected during the two campaigns over land. This requires to model the coherent component associated to the mean surface, but at the same time the diffuse incoherent component due to roughness at wavelength scale. In presence of vegetation, both components must be taken into account. The paper presents the approach followed to develop a simulator of GNSS-R data over land, aiming to support potential applications of GNSS-R for soil moisture and biomass retrieval.

In this paper an experiment concerning the capabilities of GNSS-R sensors for land applications was described. An airborne campaign was performed in summer and fall 2011 over two areas close to Florence (Italy): an agricultural zone and a forest plot of poplars. A detailed comparison of the GNSS-R signals with ground truth data was performed. Both LR and RR reflection coefficients have been found to be sensitive to changes in the surface soil moisture, with a total variation of about 6 dB between dry and wet conditions. Regarding the sensitivity to vegetation, it was observed that the measured LR coefficients have a moderate power variation due to the presence of woody vegetation. It was observed that the LR coefficient experienced a monotonic decrease with increasing biomass, up to an estimated forest dry biomass of more than 150 t/ha.

This paper deals with the modelling of forest pine scattering at P Band. A growth model for forest description and an electromagnetic model for the scattering are used. First, canonical studies are carried out about single and multiple branches scattering behaviour with the goal of highlighting further results. Then, a sensitivity study is carried out on the influence of scene description parameters and system ones on the relationship between polarimetric intensity and biomass. It has been shown that the most influent parameters are branches moisture content and branches orientation, the other parameters playing an important role but limited to some observables.

The detection of the land surface scattering of the signal radiated by navigation satellites may help estimating geophysical parameters such as soil moisture and vegetation biomass. In fact, the modulation of the GNSS signal and its frequency (L band) are particularly effective to sense vegetation attenuation and change of soil permittivity due to moisture. An experiment has been carried out in Italy using a GNSS reflectometer (GNSS-R) developed by STARLAB, mounted on top of a crane and looking toward a couple of agricultural fields by two downlooking antennas operating at right and left circular polarization. The data collected during the experiment have been interpreted by comparing them to the output of a theoretical simulator, with the purpose of interpreting from an electromagnetic point of view the scattering mechanisms involved in the experiment. A summary of the simulator main feature and some comparison results is presented in this paper.

Mesoscale ocean altimetry remains a challenge in satellite remote sensing. Conventional nadir-looking radar altimeters can make observations only along the satellite ground track, and many of them are needed to sample the sea surface at the required spatial and temporal resolutions. The Passive Reflectometry and Interferometry System (PARIS) using Global Navigation Satellite Systems (GNSS) reflected signals was proposed as a means to perform ocean altimetry along several tracks simultaneously spread over a wide swath. The bandwidth limitation of the GNSS signals and the large ionospheric delay at L-band are however issues which deserve careful attention in the design and performance of a PARIS ocean altimeter. This paper describes such an instrument specially conceived to fully exploit the GNSS signals for best altimetric performance and to provide multifrequency observations to correct for the ionospheric delay. Furthermore, an in-orbit demonstration mission that would prove the expected altimetric accuracy suited for mesoscale ocean science is proposed.

When considering a bistatic system made up of GNSS satellites and a receiver, the power at the receiver is modeled taking into account the matched filtering of the incoming signal with the PRN code modulation and Doppler filtering. In this paper, the simulator developed in the framework of the LEIMON Project supported by ESA, will be presented. The simulator is able to predict the power reflected by land taking as input the system and observation parameters, as well as the land surface parameters. The earth surface (represented by bare and vegetated soils) leaves its signature through the bistatic scattering coefficient which has been modeled by means of well established electromagnetic theories applicable at L-band. Experimental data collected during the LEIMON campaign will be compared with simulated data.

This paper aims at defining the expression of the probability of error of classification methods using a synthetic aperture radar (SAR) intensity ratio as a classification feature. The two SAR intensities involved in this ratio can be measurements from different dates, polarizations, or, also possibly, frequency bands. Previous works provided a baseline expression of the probability of error addressing the two-class problem with equal a priori class probabilities and no calibration error. This study brings up a novel expression of the error, providing the possibility to assess the effect of class probabilities and calibration errors. An extended expression is described for the$n$-class problem. The effect of calibration errors such as channel gain imbalance, radiometric stability, and crosstalk is assessed in the general case. The results indicate that, for the applications under study, channel gain imbalance is usually not a decisive parameter, but radiometric stability is more critical in methods based on the temporal change. Crosstalk has a negligible effect in the case of copolarizations. The impacts of other system parameters, such as ambiguity ratio, time-lapse between repeat-pass orbits, spatial resolution, and number of looks, are illustrated through a set of assumptions on the backscattering values of the considered classes. The model is validated by comparing some of its outputs to experimental results calculated from the application of rice fields mapping methods on real data. This error model constitutes a tool for the design of future SAR missions and for the development of robust classification methods using existing SAR instruments.

This paper presents the results of the processing of the data acquired with the Airborne Microwave Imaging Radiometer with Aperture Synthesis, the European Space Agency's airborne 2-D aperture synthesis radiometer, in a circular flight over the sea in the vicinity of Helsinki (Finland). The goal of the analysis of these data is the detection of the galactic glint on the sea surface. These results are the first measurements with an L-band 2-D aperture synthesis radiometer that show the signature of the galaxy signal reflected on the sea surface.

The ESA Sentinels constitute the first series of operational satellites responding to the Earth Observation needs of the EU-ESA Global Monitoring for Environment and Security (GMES) programme. The GMES space component relies on existing and planned space assets as well as on new complementary developments by ESA. This paper describes the Sentinel-1 mission, an imaging synthetic aperture radar (SAR) satellite constellation at C-band. It provides an overview of the mission requirements, its applications and the technical concept for the system.

Mesoscale ocean altimetry remains being a challenging area for satellite observations. Conventional nadir looking radar altimeters can make observations only along the satellite ground track and many of them are needed to sample the sea surface at the required spatial and temporal resolutions. The Passive Reflectometry and Interferometry System (PARIS) using GNSS reflected signals was proposed as a means to perform ocean altimetry along several tracks simultaneously spread over a wide swath. The present paper describes such an instrument specially conceived to fully exploit the GNSS signals for best altimetric performance, and to provide multi-frequency observations to correct for the ionospheric delay. Instrument calibration strategy is also discussed. Furthermore an in-orbit demonstration mission is proposed that would prove the expected altimetric accuracy suited for mesoscale ocean science.

This paper describes a methodology to extrapolate spaceborne quality SAR image products from long wavelength airborne polarimetric and interferometric SAR data. The methodology is applied to E-SAR data of DLR, partially acquired under ESA contract especially for the development and validation of bio/geo-retrieval algorithms in forested regions. For this purpose not only system (sensor) related parameters are altered, but also those relating to the propagation path (ionosphere) and to temporal decorrelation. Examples for future spaceborne products are presented and the potential of Pol-InSAR methods for the retrieval of forest heights from these data is discussed.

The choice of synthetic aperture radar (SAR) system design parameters such as radiometric calibration uncertainty and noise-equivalent sigma zero has a significant impact on applications exploiting SAR image data. However, methods for quantitatively translating the impact of system and mission parameter choices into the application context are lacking. This letter addresses this subject and derives a closed-form algebraic expression for translating radiometric biases due to calibration uncertainties-an important SAR engineering specification-into estimates of classification uncertainties for the restricted case of two homogeneous classes characterized by different backscatter intensity levels. The expression is exploited within this letter to estimate the potential impact of radiometric uncertainty on SAR-derived thematic maps. The results indicate that classification results based on the joint use of data from future SAR constellations in particular may be significantly degraded due to calibration uncertainties.

This IEEE Transactions on Geoscience and Remote Sensing (TGRS) special issue is dedicated to the advances in synthetic aperture radar (SAR) techniques and applications presented at the 5th International Symposium on Retrieval of Bio- and Geophysical Parameters from SAR Data for Land Applications (BioGeoSAR), which was organized by Consiglio Nazionale delle Ricerche, with active support from the European Space Agency, and held in Bari, Italy, on September 25-28, 2007. The purpose of the symposium has been to provide a forum for researchers to review current trends in the field of retrieval of quantitative information from SAR data and to present and discuss new ideas and new research directions. The BioGeoSAR symposia are usually held every three years in Europe with worldwide participation.

The ESA Sentinels constitute the first series of operational satellites responding to the Earth observation needs of the EU-ESA Global Monitoring for Environment and Security (GMES) programme. The GMES space component relies on existing and planned space assets as well as on new complementary developments by ESA. This paper describes the Sentinel-1 mission, an imaging synthetic aperture radar (SAR) satellite constellation at C-band. It provides an overview of the mission requirements, its applications and the preliminary technical concept for the system.

We examine how the rough sea surface scattering of L-band celestial sky radiation might affect the measurements of the future European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. For this purpose, we combined data from several surveys to build a comprehensive all-sky L-band celestial sky brightness temperature map for the SMOS mission that includes the continuum radiation and the hydrogen line emission rescaled for the SMOS bandwidth. We also constructed a separate map of strong and very localized sources that may exhibit L-band brightness temperatures exceeding 1000 K. Scattering by the roughened ocean surface of radiation from even the strongest localized sources is found to reduce the contributions from these localized strong sources to negligible levels, and rough surface scattering solutions may be obtained with a map much coarser than the original continuum maps. In rough ocean surface conditions, the contribution of the scattered celestial noise to the reconstructed brightness temperatures is not significantly modified by the synthetic antenna weighting function, which makes integration over the synthetic beam unnecessary. The contamination of the reconstructed brightness temperatures by celestial noise exhibits a strong annual cycle with the largest contamination occurring in the descending swaths in September and October, when the specular projection of the field of view is aligned with the Galactic equator. Ocean surface roughness may alter the contamination by over 0.1 K in 30% of the SMOS measurements. Given this potentially large impact of surface roughness, an operational method is proposed to account for it in the SMOS level 2 sea surface salinity algorithm.

A study has been undertaken which objective is to contribute to the investigation of the validity of microwave surface scattering models used in remote sensing applications, particularly when applied to realistic representations of natural surfaces. These investigations are based on recent implementations of rigorous methods (MoM and FDTD) and cover a wide range of configurations of observation (mono- and bi-static). Both land (bare soils) and sea surfaces are being investigated.

Ionospheric effects are a potential error source for the estimation of surface quantities such as sea surface salinity, using L-band radiometry. This study is carried out in the context of the SMOS future space mission, which uses an interferometric radiometer. We first describe the way the Faraday rotation angle due to electron content along the observing path varies across the two-dimensional field of view. Over open ocean surfaces, we show that it is possible to retrieve the total electron content (TEC) at nadir from radiometric data considered over the bulk of the field of view, with an accuracy better than 0.5 TEC units, compatible with requirements for surface salinity observations. Using a full-polarimetric design improves the accuracy on the estimated TEC value. The random uncertainty on retrieved salinity is decreased by about 15% with respect to results obtained when using only data for the first Stokes parameter, which is immune to Faraday rotation. Similarly, TEC values over land surfaces may be retrieved with the accuracy required in the context of soil moisture measurements. Finally, direct TEC estimation provides information which should allow to correct for ionospheric attenuation as well.

In this paper is proposed an alternative description of the roughness state of bare soil surfaces at the scale of the "homogenous" agricultural field. As a first step, the roughness power spectra of experimental profiles acquired over various test-sites in Europe are thoroughly investigated to characterize the main features of the different tillage states. Then, a novel roughness parameter is obtained by integrating the filtered power spectral density of the surface over a frequency band centered on the resonant Bragg frequency. This parameter can describe the roughness effectively seen by the SAR sensor at the scale of the field. Finally, the roughness parameter stability and its impact on the backscattered /spl sigma//spl deg/ are assessed with a view on investigating issues of parameters retrieval.

In order to improve a better quantitative understanding of the effect of soil moisture of bare soils on C-band SAR data, five dedicated "weather" stations with soil moisture probes, rain gauges, and temperature thermometers have been installed through the Flevoland test site (The Netherlands) since April 2000. In addition to the in-situ data, spaceborne SAR data at C-band measured by ERS-2 are also collected (3 images every 70 days), taking advantage that the test site is close-by the locations of the ESA transponders used for calibration of the SAR data. In this paper, the variations of the backscattering coefficient due to soil moisture changes are shown as well as the effect of rain precipitation's on the soil moisture at different depths. Diurnal variations of the soil moisture have been observed during drying periods and soil temperature measurements allow a precise monitoring of this effect.

The size, structure, spatial homogeneity and temporal thermal stability of some regions in Antarctica such as Dome-C, make them good candidates for the external calibration or validation of upcoming spaceborne L-band radiometers such as SMOS. As experimental observations are lacking at this frequency, an electromagnetic modelling approach is applied to qualitatively assess the potential of these areas.

A new approach using an architectural plant model to feed various theoretical scattering models is presented as a better interpretation of future remote sensing data acquired over natural media. The method is based on the architectural plant model (AMAP), which integrates knowledge of botanical growth processes and real plant measurements. AMAP is encapsulated in a flexible interface software called AMAP2SAR that allows one to (1) simulate a three-dimensional (3-D) plant such as a tree, (2) transform the tree into a collection of cylinders, and (3) feed theoretical models such as radiative transfer (RT) models. The method is illustrated by an example of Austrian black pine plantations in southern France. Simulated characteristics of black pines are validated for stands up to 50 years old and for a given environment. The results show the ability to derive classical forest parameters as well as those needed for electromagnetic models (such as geometry) as a function of age. Vertical profiles of canopy elements are derived and point out the vertical heterogeneity of the stands after they are 20 years old for parameters having an impact on the backscatter such as diameter and number of branches. Consequently, the crown layer variability with age and canopy depth should be considered in RT models. An RT model is modified in order to take account of accurate canopy descriptions and deal with encouraging modeling results at Cand L-band over the same test site.

Presents the results of analysis and modeling of the airborne ranging Helsinki University of Technology Scatterometer (HUTSCAT) data obtained over an Austrian pine forest in southern France. The objective is to use high vertical resolution backscatter profiles to validate a model that is subsequently used to determine the scattering sources within a canopy and to understand the wave/tree interaction mechanisms. The backscatter coefficients derived from HUTSCAT measurements at X-band at near-normal incidence and polarizations HH, VV, and VH are analyzed. The tree crown backscatter separated from the ground backscattering shows a sensitivity of about 3 dB between 0 and 200 m/sup 3//ha. The estimation of tree height using HUTSCAT profiles gives very good results, with a mean precision of 1 m. The vertical backscatter profiles are compared with the output from the MIT/CESBIO radiative transfer (RT) model coupled with a tree growth architectural model, AMAP, which recreates tree architecture using botanical bases. An a posteriori modification to the RT model is introduced, taking into account the vertical and horizontal variability of the scattering area in order to correctly estimate the backscatter attenuation. The results show good agreement between both simulated and HUTSCAT-derived vertical backscatter distribution within the canopy. The penetration depth at near normal incidence is studied. Both simulated and experimental penetration depth are compared and appear to be of several meters, varying with the stand's age.

Examination of the physical background underlying the ERS response of forest and analysis of time series of ERS data indicates that the greater temporal stability of forest compared with many other types of land cover presents a means of mapping forest area. The processing chain necessary to make such area estimations involves reconstruction of an optimal estimate of the backscattering coefficient at each pixel using temporal and spatial filtering so that classification rules derived from large scale averaging are applicable. The rationale behind the filtering strategy and the level of averaging needed is explained in terms of the observed multitemporal behavior of forest and nonforest areas, much of this analysis is generic and applicable to a wide range of situation in which significant information is carried by multitemporal features of the data. The choice of decision rules is based on the forest observations, with the added requirement for robustness. The performance of a classifier based only on change is assessed on a range of test sites in the UK, Finland, and Poland. Error sources in this classifier are identified, and the possibility of improving performance by including radiometric information in the mapping strategy is discussed. Brief discussions of how the classification is affected by the addition of coherence and how the processing chain would need to be modified for other forms of satellite data are included.

In this paper, multifrequency, polarimetric SAR data acquired during the second SIR-C/XSAR mission over the Matera (Italy) test site are analyzed. The main objective of the study is to assess the possibility of extracting relevant information about surface roughness using polarimetry. The methodology is expected to be of interest either for deriving maps of surface roughness or for setting up soil moisture retrieving procedure based on a preliminary estimation of soil roughness. After a description of ground data, experimental, and theoretical backscattering coefficients are investigated. In a further step, the dependence of copolarized correlation coefficient on the roughness and moisture states is addressed. In order to investigate the polarization effect, the correlation coefficient is described for any set of orthogonal polarizations. The results indicate an enhanced sensitivity of the correlation coefficient on roughness using circular polarizations. The analysis is subsequently extended to additional data sets acquired in the frame of SIR C/XSAR experiment (Les Landes forest, France) and AIRSAR experiment (Chickasha, USA). These further investigations confirm the trend observed on Matera data.

Information on rice growing areas and on rice growth conditions are necessary in rice monitoring programs and in studies on the emission of methane from flooded rice fields. The objective of this paper is to assess the use of ERS-1 SAR data to map rice growing areas and to retrieve rice parameters. The approach includes first a synthesis of experimental results at two different test areas followed by a development of a theoretical model to interpret the observations. The synthesis of experimental data at two test areas, a tropical site with short cycle rice (Semarang, Indonesia) and a temperate site with long cycle rice (Akita, Japan), has shown that flooded rice fields have characteristic increasing temporal radar responses. When the radar backscattering coefficients are expressed as a function of the rice biomass, the effect of cultural practices and climate (long cycle versus short cycle) is reduced. The observations have been interpreted by a theoretical model, which relies on a realistic description of rice plants and which considers the backscattering enhancement and clustering effects of the scatterers. Good agreement has been obtained between experimental data and theoretical results. The strong temporal variation of the radar response of rice fields is due to the wave-vegetation-water interaction, which increases from the transplanting stage to reproductive stage. By simulations using the validated model, the length of the rice cycle or the rice varieties have shown minor effects on the temporal curve. A method for rice fields mapping has been developed, based on the temporal variation of the radar response between two acquisition dates. Inversion of SAR images into plant height and plant biomass has also been performed. The results appear promising for the use of ERS-1 and RADARSAT data for rice monitoring.