The European Space Agency's (ESA) BIOMASS mission is a pioneering Earth observation satellite mission launched on April 29, 2025. Utilizing a P-band synthetic aperture radar (SAR), the objective of BIOMASS is to deliver estimates of above-ground forest biomass, forest height (FH), and forest disturbance (FD), with unprecedented accuracy. The mission's primary scientific goal is to quantify the distribution and changes in forest biomass, thereby reducing uncertainties in carbon flux estimates and informing climate models. The satellite's advanced instrumentation and innovative approach allow it to penetrate dense forest canopies, capturing data even in challenging environments. The mission will operate in two distinct phases: the tomographic phase and the interferometric phase, which will support polarimetric interferometric SAR (Pol-InSAR) and tomographic SAR (TomoSAR) processing. Additionally, BIOMASS will provide valuable observational data for ice sheets, deserts, the ionosphere, below canopy topography, and other domains.

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.

International audience

Recent, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) InSAR algorithm has been implemented as an open-source TomoSAR package (https://github.com/DinhHoTongMinh/TomoSAR). This effort aims to contribute the spatial distribution of subsidence in Ho Chi Minh City (HCMC), the most crowded city and economic hub of Viet Nam, in its horizontal and vertical components by using TomoSAR. With Sentinel-1 data, taking into account the presence of east-west horizontal motion, our findings indicate that the accuracy of the decomposed vertical velocity can be improved by up to 3 mm/year for Sentinel-1 data. The obtained results revealed that subsidence is most pronounced in the areas along the Sai Gon River, in the northwest-southeast axis, and in the southwest of the city, with a maximum value of 80 mm/yr, which is in accordance with the findings of the literature. The amplitude of east-west horizontal velocities is relatively small and large-scale eastward movement can be observed in the west of the city at a rate of 3–5 mm/year. This confirmed that the displacement in Ho Chi Minh City area is mainly vertical downward.

Above ground biomass (AGB) maps were estimated directly from microwave brightness temperatures (TB) using a machine learning approach. The accuracy of AGB retrievals from Artificial Neural Networks (ANN) is explored using both a multi-angular (using TBs products from the SMOS mission) and multi-frequency approach (using multi frequency measurements from the AMSR-E mission), an additional ANN inversion including optical indexes (MODIS-NDVI) is also discussed. Higher incidence angles have been shown to provide more information during the inversion process for AGB estimates than lower angles. Retrievals from the multi-angular lower-frequency inversion (SMOS - 1.4GHz) performed better than any individual higher-frequency retrievals. The addition of multi-frequency TBs (AMSR-E) to lower-frequency multi-angular TBs improves the performance of ANN models (from$\mathrm{R}^{2}\approx 0.94$to 0.96). Adding MODIS-NDVI to the inversion process improves the performance in an additional ~0.05%.

Fostered by the upcoming BIOMASS mission, this article explores long-term trends of P-band temporal decorrelation over a tropical forest due to a time series of 617 days acquired during the TropiScat-2 experiment. The interest in this unique time series is twofold. First, it provides consistent statistics to monitor the yearly evolution of temporal coherences according to specific time scales of the BIOMASS tomographic and interferometric phases. Second, it provides key insights to explore new processing approaches with the combination of data from different orbit directions (ascending/descending) and different mission cycles separated by about seven months according to the current acquisition plan. For the first time, this study shows that 18-day coherences (corresponding to the time interval between the first and last acquisitions of the BIOMASS tomographic processing) can vary significantly according to rainy and dry seasons (medians from 0.3 to 0.9). The extension to time intervals of up to 90 days within both seasons and over two consecutive years puts forward the key role of the typical sporadic rainfalls occurring during dry periods in tropical rainforests, with a stronger impact on temporal coherence evolution compared to the more reproducible rainy seasons. Furthermore, outstanding values significantly above zero have been obtained for the 7- and 14-month coherences (medians of 0.35 and 0.2, respectively), opening the way to new methods of change detection. Overall, this study highlights the role of P-band temporal decorrelation not only as a disturbance factor for coherent applications but also as a relevant indicator of forest changes.

The effects of dry and fresh matter on microwave remote sensing data represent a great challenge, for which vegetation permittivity models are essential. To develop this model, in situ permittivity sensors are needed. There are few commercial equipment dedicated to these in situ measurements and none of them are developed in order to leave the instrument on site for automatic measurements with specific constraints such as communication, wide frequency band, non-invasive measurements and low price. We developed an instrument addressing these challenges. In this article, we present the first collected measurements and the computed permittivity data.

This aims to compare the performance of different observations modes of the future BIOMASS mission for the characterization of tropical forests. In particular, it provides indicators of variability for different typical descriptors of the SAR response of a forest, and computes estimates using real data acquired at P band in the frame of the TropiSAR campaign. Results show that the best performance, reached in the Polarimetric and Tomographic mode, degrades slightly in the single-polarization case and significantly when the vertical tomographic resolution is reduced. Nevertheless, it is shown that in this latter case, the use of priors estimated in the high-resolution tomographic phase and consisting on the estimate of the wave extinction, leads to a spectacular improvement of the performance. This results reveals important in the frame of the BIOMASS missions which plans to phases with different acquisition configurations.

The European Space Agency's 7th Earth Explorer mission, BIOMASS, was proposed in 2005 and since then there have been major changes in the scientific and political conditions within which it was conceived, and also within the technology and methodology both of the mission itself and of the complementary systems that BIOMASS will work with. This paper describes some of the most important recent developments in this overall environment of the mission, and how they affect the likely use of data from BIOMASS mission after its launch in 2023 and over its nominal five-year lifetime.

Scheduled for launch in 2023, ESA's seventh Earth Explorer Mission, BIOMASS, will carry the first P-band synthetic aperture radar (SAR) to be flown in space, to gather fully polarimetric acquisitions over forested areas worldwide in interferometric and tomographic modes. This paper presents the algorithms developed to estimate biophysical parameters from BIOMASS measurements and their implementation in the BIOMASS level 2 (L2) prototype processor. The L2 processor will generate global maps of forest Above Ground Biomass (AGB), Forest Height (FH), Forest disturbance (FD). Accurate generation of these products requires the L2 processor to be closely inter-linked with the BIOMASS interferometric processor, in order to produce phase-calibrated interferometric stacks, retrieve sub-canopy terrain topography, and generate a 3D representation of forest structure by use of SAR tomography. AGB estimation results are here shown using BIOMASS-like acquisitions derived from campaign data acquired over six tropical forests in South America and Equatorial Africa.

Polarimetric Synthetic Aperture Radar Tomography (TomoSAR) is a technology to image the three-dimensional (3D) structure of the illuminated media. TomoSAR exploits the key feature of microwaves to penetrate into vegetation, snow, and ice, hence providing the possibility to see features that are hidden to optical and hyper-spectral systems. Several experimental studies by different research groups demonstrate that the use of the 3D information results in an accurate characterization of forested areas, providing access to a number of biophysical variables such as terrain topography below the vegetation, forest height, forest Above Ground Biomass (AGB), and forest classification. This paper is intended to provide the reader with an introduction to the use of TomoSAR for the characterization of forest areas, addressing basic imaging principles and methods, retrieval of biophysical parameters, and perspective for spaceborne missions.

This paper describes the rationale and development of the estimation techniques for the level-2 data products of the European Space Agency's 7th Earth Explorer BIOMASS mission. BIOMASS is planned for launch in 2023 and will carry the first-ever P-band synthetic aperture radar (SAR) onboard a satellite. It has been designed to produce consistent global maps of the Earth's forests during a nominal five-year lifetime. Fully polarimetric SAR data will be collected and the satellite orbit will be selected for repeat-pass interferometry and tomography in separated mission phases. Mission requirements call for three level-2 data products: above-ground biomass, forest height and forest disturbance. The paper also discusses the expected limitations of the estimation techniques and remaining problems to be addressed.

The Mekong delta is inhabited by more than 20 million people in Vietnam and is highly vulnerable to the additive effects of land subsidence and sea-level rise due to global climate change. To cover Delta-wide from 2015-2020, there are 250 multi-temporal SAR Sentinel-1 images, where each scene is stored in complex values with about 14 GB memory data. To do interferometry processing, we need to be able to intelligently handle ensemble 250× 14 GB data. Thus, although Sentinel-1 Big Data offers the best opportunity for land subsidence monitoring, it is challenge due to an unprecedented big volume of multi-temporal InSAR dataset. In this paper, the objective is to provide a discuss on a feasible method to handle the Delta-wide land subsidence by TOPS interferometry.

Following the past TropiScat and AfriScat tower-based scatterometer experiments conducted as part of the European Space Agency (ESA) BIOMASS mission preparation activities, a follow-on experiment referred to as TropiScat-2 has been undertaken in French Guiana since March 2018. Based on new capabilities including C-band acquisitions, this letter addresses the question of temporal decorrelation variability at several time scales using a period of 67 days dating back to early August 2018. Overall, C-band coherences have been found very dependent on the reference hour and higher than expected at night (15-min coherences above 0.75 and 1-h coherences above 0.5 75% of the time). Supported by meteorological data from the flux-tower, clear evidence about diurnal convective effects is shown. For the first time, the link with evapotranspiration is pointed out based on a drop of the 15-min coherences going from about 0.8 to 0.2 in less than 1 h (around 7h30) and before the rise of convective winds. Furthermore, the specific impacts of convective winds during the day have been demonstrated using very short timescales (with the spread of coherences at 1 s from 0 to 0.9), whereas it is more difficult to separate these effects from those of water transfer for longer timescales. In addition to providing new insights for the understanding of microwave interactions with dense vegetation, these results should also contribute to widen the capabilities of future companion or high revisit space-borne missions at C-band.

The ESA BIOMASS mission will be the 7th Earth Explorer measuring the above-ground biomass (AGB) in the world's forests. The current ESA Level-2 (L2) implementation study focuses on defining and implementing the main algorithms for forest parameter retrieval from BIOMASS data. After the first year of L2 study innovative results were achieved: the development of ground cancellation, in particular, has proved to be huge value, since it removes from the data the effects of environmental variability and contributions unrelated to the forest carried in the ground scattering. In this paper the current processor implementation and validation activities of the L2 team will be described.

The structure of forests, in terms of mass and the three-dimensional arrangement of individual trees, is a direct indicator of how much carbon is stored in the ecosystem, which in turn, has a profound effect on how the ecosystem functions and cycles carbon, water, and nutrients (Shugart et al., 2015). There is an increased need to understand local to global storage and dynamics of carbon in terrestrial vegetation, as carbon storage is a prerequisite to understanding the coupling of the biosphere to other components of Earth systems including the climate. The BIOMASS mission of the European Space Agency (ESA), to be launched in 2021-2022 will provide, for the first time, synthetic aperture radar measurements at P-band frequency (~70 cm wavelength) and tomographical (TomoSAR) imaging capability to quantify forest structure and above ground biomass.

This paper concerns processing techniques for the the characterization of a tropical forest using PolTomSAR data at P band. In particular, existing forest biomass estimation methods, relating biomass to sampled tomographic intensity values, are revisited using simple methodological step-sand an adaptive tomographic intensity sampling approach. The canopy reflectivity sampling location is determined as a function of the effective forest height and of the tomographic resolution, in order to compensate geometrical mismatches. Moreover, an adaptive polarimetric decomposition technique is used to further decouple the sampled intensity from ground and topographic tomographic scattering effects. The performance of the proposed techniques is assessed using TROPISAR P-band data acquired by the ONERA's SETHI sensor over the Paracou data site in French Guiana in 2009. Results indicate over this site a substantial reduction of the Above Ground Biomass (AGB) estimation error, with respect to existing techniques.

The objective of this paper is to provide a better understanding of tomographic capabilities to estimate above ground biomass (AGB) in dense forested areas at P-band. The analysis is carried out on airborne data acquired over sites in French Guyana and in Gabon during the ESA campaigns TropiSAR and AfriSAR 2015, respectively. Over both sites, P-band tomography allows us to retrieve the vertical structure of the forest, to better characterize the ground and/or volume scattering mechanisms and to provide a unique solution for the AGB retrieval over the full range of biomass. The relationship between AGB and tomography data was found to be highly similar for forests across continents and sites: Paracou (French Guiana), Lope, Rabi and Mondah (Gabon). The developed metrics derived from the tomographic data have been found highly correlated to reference in situ AGB estimates (R2=0.85) and the root mean square error was 16% (for AGB ranging from 0 to 500 t/ha). These results have strong implications for the tomographic phase of the BIOMASS spaceborne mission.

The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infra-red vegetation indices. This study is devoted to the characterisation of a new L-Band (1.4 GHz) VOD dataset (SMOS-IC L-VOD) obtained from the SMOS (Soil Moisture and Ocean Salinity) satellite. SMOS IC L-VOD is evaluated through a comparison with several vegetation-related quantities such as tree height and above ground biomass (AGB) for different land cover types. SMOS L-VOD shows monotonic relationships with respect to the variables extracted from these different datasets without signs of saturation at high values. The relationships between L-VOD and AGB were also compared to those obtained using the Normalized Difference Vegetation Index (NDVI) and K/X/C-VOD (VOD measured at 19, 10.7, and 6.9 GHz). In contrast to NDVI and K/X/C-VOD, L-VOD shows a relationship to AGB that is closer to a linear one without significant signs of saturation. SMOS L-VOD is a very promising dataset for large scale monitoring of biomass, at coarse scale spatial resolution (~ 40 km), but with high temporal resolution and with an improved sensitivity with respect to higher-frequency VOD data.

In this paper, an algorithm using Sentinel-1 (S-1) and Sentinel-2 (S-2) data to identify changes of tillage over agricultural fields at approximately ~100m resolution is presented. The methodology implements a multiscale temporal change detection on S-1 VH backscatter in order to single out VH changes due to agricultural practices only. The algorithm can be applied over bare or scarcely vegetated agricultural fields, which are identified from S-2 NDVI measurements. An initial assessment at farm scale using in situ and S-1 and SPOT5-Take5 data, acquired over the Apulian Tavoliere in southern Italy in 2015, is illustrated. A full validation of the approach is in progress over three European agricultural areas located in Italy, Spain and France. Results will be further reported in the paper.

The Earth Explorer Biomass mission will provide the scientific community with accurate maps of tropical, temperate and boreal forest biomass, including height and disturbance patterns. This information is urgently needed to improve our understanding of the global carbon cycle and to reduce uncertainties in the calculation of carbon stocks and fluxes associated to the terrestrial biosphere. It is also crucial for approaches to managing climate, such as the UNFCCC initiative known as Reducing Emissions through Degradation and Deforestation (REDD+), aimed at climate change mitigation through conservation and better management of tropical forests The required measurements are forest biomass and forest height at resolution of 200 m, and detection of deforestation at 50 m. Global maps of biomass are required with accuracy of 20% (or l0 t ha-1when above-ground biomass are less than 50 t ha-1). To achieve this Biomass will be implemented as a P-band SAR mission. It will exploit the unique sensitivity of P-band SAR together with advanced retrieval methods including polarimetric interferometry (Pol-InSAR) and SAR tomography to measure biomass, height and disturbances across the entire biomass range every 6 months. The mission will also support important secondary objectives, including sub-surface imaging in arid zones, production of a bare-earth DTM and ice applications.

The objective of this paper is to provide a better understanding of tomographic capabilities in characterization of dense forested areas at P-band. The analysis is carried out on airborne data acquired by ONERA over the site in French Guyana, and in Gabon during the ESA campaign TropiSAR 2009 and AfriSAR 2015, respectively. The results shown support the idea that ground- and -volume interactions play a significant role at P-band. For a dense forest of 30 m and more, strong ground contribution at P-band can be visible in tomograms. P-band tomography allow us to retrieve the whole forest vertical structure, better characterizing of the ground and/or volume scatterings and providing an unique solution in high biomass ranging from 0–500 t/ha.

Temporal decorrelation is a critical parameter for repeat-pass coherent radar processing, including many advanced techniques such as polarimetric SAR interferometry (PolInSAR) and SAR tomography (TomoSAR). Given the multifactorial and unpredictable causes of temporal decorrelation, statistical analysis of long time series of measurements from tower-based scatterometers is the most appropriate method for characterizing how rapidly a specific scene decorrelates. Based on the TropiScat experiment that occurred in a tropical dense forest in French Guiana, this letter proposes a comparative analysis between temporal decorrelation at P-band and at higher frequencies in the range of 800-1000 MHz (the low end of the L-band). This letter aims to support the design of future repeat-pass spaceborne missions and to offer a better understanding of the physics behind temporal decorrelation. Beyond the expected lower values that are found and quantified at the low L-band compared with the P-band, similar decorrelation patterns related to rainy and dry periods are emphasized in addition to the critical impacts of acquisition time during the day.

This paper deals with AfriScat, a long-term tower-based scatterometer measurements experiment which will take place over the Ankasa forest in Ghana, an African tropical rain forest.

The rapidly developing urbanization since the last decade of the 20th century leads to the strong groundwater extraction, resulting in the subsidence phenomena in the Ha Noi and Ho Chi Minh City, Vietnam. Recent advances in the multi-temporal spaceborne SAR interferometry, especially with Persistent Scatters Interferometry (PSI) approach, is the robust remote sensing technique for measuring ground subsidence in large scale with millimetric accuracy. This work has presented an advance PSI analysis, to provide unprecedented spatial extent and continuous temporal coverage of the subsidence in Ha Noi and Ho Chi Minh City.

The objective of this paper is to provide a better understanding of the impact of temporal decorrelation on the tomography phase of the P-band Synthetic Aperture Radar (SAR) BIOMASS mission, 7-th Earth Explorer of the European Space Agency. In this context, in the framework of the Phase A studies of the BIOMASS mission, the airborne TropiSAR 2009 and ground-based TropiScat 2011 experiments were conducted over the site of the tropical forest Paracou, French Guiana. The P-band SAR tomographic data acquired during TropiSAR campaign allowed us to reconstruct 3-D high resolution data, whereas TropiScat experiment provided vertical temporal coherence of the vegetation. These data therefore allow us to generate a BIOMASS P-band SAR data stack that accounts for both the 6 MHz bandwidth limit and temporal decorrelation. To do this, we developed a tomo-graphic simulator, which can combine 3-D high resolution data from TropiSAR and the temporal decorrelation from TropiScat data-sets, to provide the most realistic temporal BIOMASS tomographic data. The resulting tomograms and forest heights were observed to change acceptably as long as the revisit time is 4 days or less. Therefore, the revisit time for the BIOMASS tomographic phase at 3–4 days as proposed should be feasible.

Countries that have ratified and/or are signatories to the United Nations Framework Convention on Climate Change (UNFCCC) and its related Kyoto Protocol have been actively involved in developing relevant policy processes that aim at mitigating the effects of climate change, collectively known as Reducing Emissions from Deforestation and Degradation (REDD+). A key question for REDD+ measurement reporting and verification (MRV), is how much aboveground biomass (AGB) or carbon has been released. The standard method for REDD+ MRV consists in using earth observation optical data to assess change in forest cover, together with a priori knowledge of values of forest carbon per unit area. However, current results of mapping forest cover and estimating forest cover changes using optical remote sensing depend on the forest definition. Each country can select their forest definition using recommended values. In this paper, the focus is on the assessment of African tropical forests AGB that are not taken into account into REDD+ processes. To do so, the AGB map from [1] over Africa has been improved and AGB from this map has been assessed in UN-REDD partner countries over areas from the Landsat tree cover benchmark map [2] that are not considered as forests (the so-called non forest class). The results show that biomass loss estimation is clearly under-estimated when using standard methods such as changes in forest cover using spaceborne optical data, in countries mostly covered by savannas.

The objective of this paper is to provide a better understanding of tomographic capabilities in characterization of dense forested areas at P-and L-band. The analysis is carried out on airborne data acquired by ONERA over the site of Paracou, French Guyana, during the ESA campaign TropiSAR. The results shown support the idea that ground- and -volume interactions play a negligible role at L-band, whereas they are significant at P-band. For a dense forest of 30 m and more, there is very weak ground contribution at L-band. The L-band tomographic profile is quite disturbed as compared to the P-band profile in dense tropical forest areas. In this condition, the use of tomographic imaging at L-band in tropical forests appears limited. However, when the forest top height is roughly below 20 m (e.g., in forest regrowth), the tomographic results are expected to be the same as in boreal forests. Whereas P-band tomography allow us to retrieve the whole forest vertical structure, better characterizing of the ground and/or volume scatterings and providing an unique solution in high biomasss ranging from 150-600 t/ha.

The objective of this letter is to provide a better understanding of the impact of temporal decorrelation on the tomographic phase of the P-band synthetic aperture radar (SAR) mission BIOMASS, selected as the Seventh Earth Explorer by the European Space Agency. In the context of Phase A BIOMASS activities, the tropical forest site of Paracou, French Guiana, was illuminated at P-band during the airborne campaign TropiSAR 2009 and the ground-based campaign TropiScat 2011. P-band data from TropiSAR were used to generate a high-resolution 3-D reconstruction of the Paracou forest, whereas TropiScat data provided information about temporal correlation considering different time lags and different heights within the vegetation layer. The ensemble of the two datasets were used to generate a synthetic SAR data stack that emulates BIOMASS acquisitions over the Paracou forest site, accounting for BIOMASS geometry and resolution, as well as for the forest temporal decorrelation. Different data stacks were produced by varying the revisit time between two consecutive passes from 1 to 17 days. The resulting vertical structure reconstruction and forest height retrieval were observed to yield valuable results as long as the revisit time is 4 days or less.

In this letter, the temporal coherence of tropical forest scattering at P-band is addressed by means of a ground-based experiment. The study is based on the TropiScat campaign in French Guiana, designed to support the Biomass mission, which will be the ESA 7th Earth Explorer mission. For Biomass, temporal coherence is a crucial parameter for coherent processing of polarimetric synthetic aperture radar (SAR) interferometry and SAR tomography in repeat-pass acquisitions. During the experiment, data were continuously collected for six months during both the rainy and dry seasons. For the rain-free days in both seasons, the coherence exhibits a daily cycle showing a high decorrelation during daytime, which is likely due to motion in the canopy. Up to a 20-day baseline, the coherence is much higher in the dry season than in the rainy season (> 0.8). From 20 to 40 days, it presents the same order of magnitude in both seasons [0.6, 0.7]. For larger temporal baselines, it becomes lower in the dry season. The results can be used to assess the long-term coherence of repeat-pass observations over a tropical forest. However, an extension of this study to several years and over other forest spots would be necessary to draw more general conclusions.

The objective of this paper is to provide a better understanding of the capabilities of the BIOMASS tomography concerning the retrieval of forest biomass and height in tropical areas. The analysis presented in this paper is carried out on airborne data acquired by Office National d'Etudes et de Recherches Aérospatiales (ONERA) over the site of Paracou, French Guiana, during the European Space Agency campaign TropiSAR. This high-resolution data set (125-MHz bandwidth) was reprocessed in order to generate a new data stack consistent with BIOMASS as for the bandwidth (6 MHz) and the azimuth resolution (about 12 m). To do this, two different processing approaches have been considered. One approach consisted of degrading the resolution of the airborne data through the linear filtering of raw data, followed by standard SAR processing. The other approach consisted of recovering the 3-D distribution of the scatterers at a high resolution, which was then reprojected onto the BIOMASS geometry. The latter procedure allows us to obtain a data stack that is the most realistic emulation of BIOMASS imaging capabilities. In both approaches, neither ionospheric disturbances nor temporal decorrelation has been considered. The connection to the forest biomass has been examined in both cases by investigating the correlation between the backscatter at different forest heights and the above-ground biomass (AGB) values from in situ data. As expected, the reduction of the system bandwidth to 6 MHz resulted in significant vertical resolution losses compared with the original airborne data. Nevertheless, it was possible to retrieve the forest height to within an accuracy of better than 4 m, whereas the backscattered power at the volume height (30 m above the ground) exhibited a correlation higher than 0.8 with the in situ data and no bias phenomena over the AGB values ranging from 250 to 450 t/ha.

This paper deals with the overview of three ground based radar experiments: two scatterometers continuously measuring the backscattering of a temperate tree and of a tropical forest plot and a campaign imaging 10 forest plots with high resolution tomography. The results exhibited some common trends with the presence of diurnal cycles for the Radar Cross Section (RCS) and for the coherence in both temperate and tropical region. The comparison of scatterometers time series and the tomographic campaign showed a higher influence of the geometrical parameters compared with the biophysical parameters.

In this study, we use time-series of X-band Synthetic Aperture Radar (SAR) data to monitor rice fields in the Mekong Delta, Vietnam. Ten dual-polarisation (HH&VV) images of the COSMO-SkyMed constellation are used in the third rice season of 2013, and 3 images in the first season of 2014. In 2013, the season-long comparison of backscatter and biophysical parameters measured in 40 fields allowed us to develop methods to map rice fields at both seasons and to derive the sowing date and the plant biomass at the vegetative stage in 2013.

The 7-th ESA Earth Explorer, BIOMASS is a synthetic aperture radar (SAR) which will collect data from employing a multiple baseline orbit during the initial phase of its lifetime. This data can be used for tomographic SAR (TomoSAR) processing resulting in a vertical resolution of about 20 m, sufficient to decompose the backscatter from most tropical forests into two to three layers. A recent study using airborne data from the TropiSAR campaign at the site of Paracou, French Guiana, showed that this information significantly improves the retrieval of forest above-ground biomass (AGB), resulting in an accuracy of about 10% of AGB at a resolution of 1.5-ha. In this paper, we generalize this result, by applying the same algorithm to the Nouragues test site in central French Guiana. This site is characterized by a hilly terrain and an AGB ranging from 150 to 600 t/ha. The relationship between AGB and TomoSAR data at Nouragues was found to be highly similar to the one observed at Paracou. We found that the best correlation between the backscatter signal and AGB is held in the upper canopy layer (i.e. 20-40 m). Cross validation using training plots from Nouragues and validation plots from Paracou, and vice versa, resulted in an accuracy of about 16%-18% of AGB using 1-ha plots. This result suggests that the TomoSAR AGB retrieval method is generalizable to other study sites. In addition we show that, TomoSAR can be used to estimate the canopy height with an error of less than 4 m with forest height ranging from 20 m-40 m.

About 42% of tropical forests around the world are considered as seasonally dry and its removal contributes to a significant proportion of the 7-20% of the total greenhouse gas emissions known to originate from deforestation and forest degradation. This study focus on two methodologies using both optical and radar data developed and tested for dry forest deforestation/degradation assessment in Central African Republic (CAR) within the REDD+ context. The study area is located across three provinces of CAR: Sangha Mbaéré, Lobaye and Ombella-M'Poko representing an area of 47 640 km2.

Synthetic aperture radar (SAR) is one of the most promising remote sensor to map forest carbon. The unique spaceborne and long-wavelength SAR data currently available are L-band data, but their relationship with forest biomass is still under controversy, particularly for high biomass values. While many studies assume a complete loss of sensitivity above a saturation point, typically around 100 t.ha−1, others assume a continuous positive correlation between SAR backscatter and biomass. The objective of this paper is to revisit the relationship between L-band SAR backscatter and dense tropical forest biomass for a large range of biomass values, using both theoretical and experimental approaches. Both approaches revealed that after reaching a maximum value, SAR backscatter correlates negatively with forest biomass. This phenomenon is interpreted as a signal attenuation from the forest canopy as the canopy becomes denser. This result has strong implication for L-band vegetation mapping as it can lead to a more-than-expected biomass under-estimation.

The next ESA Earth Explorer Core Mission BIOMASS is envisaged to collect multiple baselines on selected areas during the initial phase of its lifetime. Such data will allow to image the vertical structure of the vegetation layer to within a vertical resolution of about 20 m, sufficient to decompose the backscattered power from a tropical forest into two-three layers. The information provided by tomography has recently been shown to be strictly linked to above ground biomass (AGB) in tropical forest, therefore providing a valuable tool for ABG estimation. The aim of this paper is to present a bird-eye overview of BIOMASS Tomography, along with the main experimental results from airborne campaigns flown during Phase-A BIOMASS activities.

For Reduced Emissions from Deforestation and forest Degradation (REDD+) purposes, the standard method consists in using optical data to assess change in forest cover. However, SAR data are sensitive to forest above-ground biomass; thus an adequate SAR system, with long wavelength, could provide mapping of biomass and its change over time to be used for estimating carbon emissions. In this paper, the focus is on the comparison of forest cover maps derived from optical data and from SAR data in the Centre Province of Cameroon (about 84 000 km2), which contains humid tropical forests and woody savannas. The forest-non forest maps from optical and SAR data have comparable validation results. However, SAR-based methods are easy to implement, do not require extensive reference samples for calibration, and answer to the key question for carbon cycles studies which is how much biomass or carbon has been disturbed.

In the frame of the Biomass mission activities, this paper presents the challenges and recent results in the retrieval of forest biomass from polarimetric (PolSAR) and interferometric (PolInSAR) P-band SAR data. During the mission Phase A, critical issues in the biomass retrieval algorithms in boreal and tropical forests have been identified and addressed. In boreal forest, multi polarization backscatter data can be used to mitigate much of the variability due to environment effects. In high biomass tropical forest, because of the low sensitivity of the backscatter to biomass, appropriate correction methods were developed to mitigate the disturbing effects. Also to enhance the retrieval results, a combination of PolSAR and PolInSAR methods was proposed.

In this letter, we present the results from the ground-based European Space Agency campaign TropiScat, which is aimed at evaluating the temporal coherence at P-band in a tropical forest in all polarizations and at different heights within the vegetation layers. The TropiScat equipment has been operated since October 2011 at the Paracou field station, French Guiana, to continuously produce height-range images of the forest with a temporal sampling of 15 min. The forest temporal behavior can be then captured by analyzing the interferometric coherence between the images gathered at different times, considering time scales on the order of hours, days, and months. The results indicate that the vegetation is likely to undergo a significant motion during day hours due to wind and temperature changes, whereas it appears to be definitively more stable during night hours. This result appears to provide a very useful input to the Biomass Monitoring Mission for Carbon Assessment (BIOMASS), as it suggests that the performance over a tropical forest could be optimized by gathering acquisitions in early morning or night hours. The long-term temporal decorrelation has been then evaluated by considering dawn-dawn acquisitions to minimize the impact of wind gusts and by excluding rainy days in order to not confuse forest and system decorrelation. As a result, the temporal coherence at the ground level was found to stay high at about 0.8 at 27 days, whereas the temporal coherence at the canopy height was found to be about 0.8 at 4 days and about 0.65 at 27 days, indicating coherence sensitivity to height.

The retrieval of above-ground biomass (AGB) in dense tropical forests using synthetic aperture radar (SAR) images is widely recognized as a challenging task. The first difficulty arises from the decrease of sensitivity of the backscattered intensity to biomass at high biomass values, often referred to as the backscatter saturation effect. At P-band, the decrease of sensitivity can occur at biomass values higher than about 300${\rm t~ha}^{-1}$, e.g., those of many dense tropical forests. Another limiting factor is associated with the ground effects, as they can change significantly the magnitude of returns from vegetation–ground interactions. As a consequence, terrain topography or ground moisture status can determine the variations of the observed signal that are not due to forest biomass. A solution to reduce the ground effects is to have access to layers inside the forest canopy where the backscatter from vegetation–ground interactions is not significant. The study presented in this paper is an attempt to overcome the issues outlined above based on direct 3-D imaging of the forest volume, which is possible through multibaseline SAR tomography. In this way, forest biomass can be investigated by considering not only the backscattered power at each slant range and azimuth location but also its vertical distribution. The data analyzed in this paper are from the P-band airborne dataset acquired by Office National d'Études et de Recherches Aérospatiales (ONERA) over French Guiana in 2009, in the frame of the European Space Agency campaign TropiSAR. The dataset is characterized by a favorable baseline distribution, resulting in a vertical resolution less than half the forest height, which made it possible to decompose the vertical distribution of the backscattered power into a number of layers by coherent focusing, i.e., without assuming any prior knowledge about the forest vertical structure. For each layer, the relationship between the backscattered power and forest AGB was then analyzed. As expected, it was found that the power from the bottom layer is very weakly correlated to AGB, whereas the power from a layer at about 30 m above the ground yields the best correlation and sensitivity to AGB in all polarizations, for actual AGB values ranging from 250 to 450${\rm t~ha}^{-1}$. An interpretation of this result is also provided, based on a forest growth model simulation. Finally, the relevance of tomographic technique in P-band spaceborne mission is discussed.

This paper deals with the temporal survey of the tropical forest electromagnetic scattering with a ground-based radar equipment. Installed on the top of a 55 m flux tower overlooking the Paracou forest in French Guiana, a dense primary tropical forest, the radar system uses a vertical antenna array and it is able to provide every 15 minutes P-band complex scattering matrix coefficients. The experiment has been successfully set up and it is operating since October 2011. The main goal of this campaign is to investigate the evolution of the backscattering coefficient and the temporal coherence of the tropical forest at different time scales range. Data are calibrated in relative and processed to take advantage of the largest number of independent looks. Three months of data are exploited in terms of polarimetric temporal coherence and backscattering coefficient in the rainy season and about two months in the dry period. The temporal coherence exhibits daily cycles during the consecutive dry days, whatever the period, and these cycles are perturbed by the presence of rain. Its overall time series appear clearly dependent on the period, dry or rainy, and also on the polarization. The backscattering coefficient time series exhibit also a daily cycle during consecutive dry days, very clearly in the dry period but less pronounced or absent during the rainy period. The backscattering coefficient presents an overall relatively high stability over the full period.

In this paper we present results from the ground-based ESA campaign TropiScat, aimed at evaluating the temporal coherence at P-band in a tropical forest in all polarizations and at different heights within the vegetation layers. The TropiScat equipment has been operated since October 2011 at the Paracou field station, French Guiana, to continuously produce height-range images of the forest below with a temporal sampling of 15 minutes. The forest temporal behaviour can then be captured by analyzing the interferometric coherence between images gathered at different times, considering time scales on the order of hours, days, and months. Temporal coherence at the ground level was found to be higher than 0.8 at 27 days in all polarimetric channels, whereas temporal coherence at canopy height was found to be about 0.8 at 4 days and about 0.65 at 27 days, witnessing coherence sensitivity to height.

In this paper we discuss the design concepts and preliminary results relating to the European Space Agency's ground-based campaign TropiScat, whose main goal is to evaluate temporal coherence at P-band in a tropical forest in quad-polarization, considering temporal lags ranging from hours to months and at different heights within the vegetation layer. The experiment has been successfully set up and operated since October 2011 at the Paracou field station, French Guiana, where the equipment was installed on top of the 55-m high Guyaflux Tower to illuminate the forest below. The system consists of a vector network analyzer connected to 20 antennas through a switchbox, which allows the use of any of them either as a transmitter or as a receiver. Vertical imaging and fully polarimetric capabilities are achieved by operating the 20 antennas in a multistatic fashion, resulting in an equivalent monostatic array consisting of 15 phase centers displaced along the vertical direction in each polarization. Such a design allows unambiguous imaging of the vegetation while yielding a minimum distance between nearby antennas on the order of 0.8 m, so as to minimize coupling effects. The equipment allows the gathering of signals with the tomographic array within a few minutes, resulting in the possibility to produce a tomographic image of the forest with a temporal sampling of 15 min. System calibration and validation was performed by employing a 2-m trihedral reflector and a rotating dihedral reflector. This allowed the evaluation of the system pulse response in all polarizations and also assessment of the extent of tower motions. As a result, tomographic images have been generated from 500 (P-band) to 900 MHz in all polarizations. Results from real data acquired in Fall 2011 confirm the feasibility of carrying out reliable coherence measurements for the whole duration of the campaign.

The retrieval of above-ground biomass in dense tropical forests using Synthetic Aperture Radar (SAR) images is widely recognized as a challenging task, because of the backscatter saturation effect at high biomass values and the ground topography effect. The study presented in this paper is an attempt to overcome these issues based on direct three-dimensional imaging of the forest volume, which is possible through multi-baseline SAR tomography. In this way, forest biomass can be investigated by considering not only the backscattered power at each slant range, azimuth location, but also its vertical distribution. It was found that the power from the a layer at 30m ± 10m above the ground yields the best correlation and best sensitivity with forest biomass in all polarizations, for biomass ranging from 250 to 450 tons/ha.

In this paper we present preliminary results from the ground-based ESA campaign TropiScat, aimed at evaluating the temporal coherence at P-band in a tropical forest in quad-polarization and at different heights within the vegetation layer. The TropiScat equipment allows to gather the signal with a multistatic array within few minutes, resulting in the possibility to produce a tomographic image of the forest with a temporal sampling of 15 minutes. Concerning short term temporal decorrelation, the most relevant phenomenon is the coherence drop during daytime, due to the action of wind moving the forest canopy. This result appears to provide a very useful input concerning the BIOMASS mission, as it suggests that performance over tropical forest could be optimized by gathering acquisitions in the early morning or night hours. A diurnal motion along the vertical direction is observed to characterize the forest phase center. Studies are being carried out to evaluate whether this variation can be imputed to forest evapotranspiration phenomena.

This paper deals with a radar ground experiment dedicated to tropical forest backscattering at P band. With polarimetric and tomographic capabilities, this system is able to provide long-term radar data over a dense tropical forest. These data will be use to improve our comprehension of backscattering mechanisms and their evolution over long periods.

The purpose of this work is to assess biomass in forest savanna and to analyse changes in biomass from 2007 to 2010 in Cameroon, by using ALOS PALSAR data. The methodology includes an inter-calibration step, and multi-image filtering for noise reduction and preservation of the resolution. An existing direct model previously developed by CESBIO over various forests, linking γhvoto biomass, is inversed using a probabilistic bayesian approach. There is a mean of 52 t.ha-1of biomass in 12 553 km2which represents 32.63 Mt of carbon not taken into account in existing carbon budget assessments. Besides, forest savanna transition and changes in AGB from 2007 to 2010 are analysed. Changes are mainly due to shifting cultivation, grazing fires, degradation and deforestation.

The BIOMASS mission is designed to provide unique information on the biomass in the world's forests at spatial and temporal resolutions suitable for characterizing their dynamics and their contribution to carbon cycle estimates. To achieve this it combines biomass estimates from direct inversion of polarimetric backscattering coefficients with Pol-InSAR forest height estimates. The mission will also support important secondary objectives, including sub-surface imaging in arid zones, production of a bare-earth DTM and ice applications, and is optimized to be robust against environmental and ionospheric disturbances.

Information on above-ground biomass and its changes is of fundamental importance for carbon cycle and climate calculations. The difficulty of making ground-based inventories of biomass, especially in the tropics, has therefore led to serious attempts to estimate biomass from space. Active technologies hold the most promise. Lidar maps of forest height derived from IceSAT data have underpinned two recent pan-tropical biomass maps, using indirect statistical regression methods. More direct measurements of biomass are possible using radar, and can be combined with height measurements by a mission that supports a Pol-InSAR mode. Physics and experiment both indicate that the longest wavelength possible should be used, which is P-band. The BIOMASS mission is driven by these considerations, and is uniquely fitted to measure biomass, but will gain significantly from combination with both spaceborne L-band data, expected to be available during the mission lifetime, and with airborne lidar measurements.

The BIOMASS mission is designed to map the full range of the world's above-ground forest biomass, for the needs of national scale inventory and global carbon flux calculations. This objective is achieved with advanced P-band SAR techniques. The P-band biomass measurement concept was based on previous work over the past two decades. During the preparatory phase, new campaigns have been conducted to address critical issues on the biomass retrieval algorithms, over tropical and boreal forests. The collected datasets comprise accurate and complete sets of in situ data and advanced P-band SAR data. This paper presents the retrieval algorithms developed using the collected datasets.

In terms of biomass information, the most critical forest regions of the Earth are in the tropical belt, where knowledge on biomass and biomass changes is poor and where biomass information is the most needed, for carbon flux calculations and for national scale inventory. The difficulty of making in situ inventories of biomass in the tropics, has therefore led to serious efforts to estimate biomass from space. P-band SAR, which is the longest wavelength radar systems possible appears the most suitable for biomass measurements, however, its performance in tropical forests still needed to be demonstrated. This was achieved by the design of the TropiSAR campaign in French Guiana, and the ensuing development of inversion algorithms, which will be presented in the paper.

Forest biomass retrieval using P-band SAR data has been demonstrated during the past 2 decades. However, little effort has been made to recover biomass in tropical forests, with biomass values typically ranges from 250 to 500 t/ha. In this high range of biomass, the radar backscatter coefficients exhibit poor sensitivity to biomass, which is often obscured by perturbing effects. The most important effect is caused by topography, which needs to be corrected before biomass inversion. In this work, we develop a new backscatter coefficient called t0to account for a) the non reflection symmetry of the forests in case of slopes and b) the change in angular variation of the backscatter following the change of scattering mechanisms due to slopes. The method is tested on TropiSAR campaign data at two forest sites in French Guiana. The results shows the relevance of proposed correction methods to reduce the effects of topography in dense forests.

The TropiSAR campaign has been conducted in August 2009 in French Guiana with the ONERA airborne radar system SETHI. The main objective of this campaign was to collect data to support the Phase A of the 7th Earth Explorer candidate mission, BIOMASS. Several specific questions needed to be addressed to consolidate the mission concept following the Phase 0 studies, and the data collection strategy was constructed accordingly. More specifically, a tropical forest data set was required in order to provide test data for the evaluation of the foreseen inversion algorithms and data products. The paper provides a description of the resulting data set which is now available through the European Space Agency website under the airborne campaign link. First results from the TropiSAR database analysis are presented with two in-depth analyses about both the temporal radiometric variation and temporal coherence at P-band. The temporal variations of the backscatter values are less than 0.5 dB throughout the campaign, and the coherence values are observed to stay high even after 22 days. These results are essential for the BIOMASS mission. The observed temporal stability of the backscatter is a good indicator of the expected robustness of the biomass estimation in tropical forests, from cross-polarized backscatter values as regarding environmental changes such as soil moisture. The high temporal coherence observed after a 22-day period is a prerequisite for SAR Polarimetric Interferometry and Tomographic applications in a single satellite configuration. The conclusion then summarizes the paper and identifies the next steps in the analysis.

This paper deals with a ground experiment and its modelization, related to the future spaceborne BIOMASS mission for global forest biomass estimation.

The TropiSAR campaign has been conducted in August 2009 in French Guiana with the ONERA airborne system SETHI. The main objective of this campaign was to collect data to support the Phase A of the Earth Explorer candidate mission, Biomass. Several specific questions need to be addressed to answer the recommendations of the ESAC group and the data collection strategy has been constructed accordingly. The first part of the paper lists these specific questions. We then describe the selected test sites, followed by a summary on the radar instrument and the radar configuration (geometry and waveform). The data acquisition plan is provided and the temporal behaviour of the P-Band data is explored.

The European Space Agency (ESA) released a Call for Proposals for the next Earth Explorer Core Mission in March 2005, with the aim to select the 7thEarth Explorer (EE-7) mission for launch in the next decade. Twenty-four proposals were received and subject to scientific and technical assessment. Six candidate missions were selected and further investigated in the preliminary feasibility studies (Phase 0). One of these missions is BIOMASS, which has recently been selected to proceed to Phase-A. BIOMASS is a response to the urgent need for greatly improved mapping of global biomass and the lack of any current space systems capable of addressing this need.

Ground topography under vegetated area is liable to bring significant changes on radar backscattering and thereby on the associated standard retrieval algorithms dedicated to forest biomass. Within the framework of the ESA BIOMASS mission, this paper evinces the evolution of P-band polarimetric intensities with a tilted underlying ground. For that purpose, electromagnetic simulations have been achieved using our model MIPERS which theoretical specificities accounting for the topographic effects are herein described. Its originality lies mainly in the 3D characterization of the ground and the volume, as well as the coupling effects between both. This description is followed by a sensitivity analysis in order to further-on quantify the possible consequences on biomass retrieval, conducted with the two standard approaches, namely the P-HV intensity technique and the Pol-InSAR one assuming the RVoG model. This investigation have been also undertaken for a better understanding of experimental data, particularly with the BioSAR and the TropiSAR airborne campaigns over boreal and tropical forests.

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.

The Random Volume over Ground (RVoG) model has been extensively applied to polarimetric synthetic aperture radar interferometry (Pol-InSAR) data for forest height inversion. The model assumes forest as a homogeneous volume of randomly oriented particles characterized by a constant extinction but does not take into account the forest vertical heterogeneity, to which interferometric coherence is sensitive. In order to integrate vertical heterogeneity in forest models, two complementary models, which take into consideration the forest natural structure, are investigated through analysis of volume interferometric coherence. The first model assumes a vertically varying extinction in the volume layer, and the second model considers predominant contributions localized in a finite height interval, modeled as a Gaussian-distributed backscatter. The two forest models are compared with constant extinction RVoG in the coherence and interferometric phase aspects. Finally, the contribution of these new models for forest height inversion using the Pol-InSAR technique is discussed in the context of a two-layer ground + canopy medium.

The vertical backscatter profile of a pine forest constituted by stands of different height is inverted from a single baseline P-band Pol-InSAR data in order to identify scatterers in the canopy. The proposed approach uses the Gaussian vertical backscatter profile model, which associates an interferometric coherence expression to a vertical scatterers' distribution characterized by relative standard deviation and elevation. The methodology, which usesin situmeasurements of forest height and unbiased ground level estimation, is applied to HV and VV channels, providing accuracy given sufficiently low ground-to-canopy power ratios. Inverted backscatter profiles show maximum power converging toward the basis of the tree crown on highest forests, where the largest branches are located, indicating the high sensitivity of P-band measurements to the forest structure and to the vertical biomass distribution. Over lower stands with larger tree densities, the power peak is located in the upper part of the canopy, which can be explained by a stronger attenuation in the canopy.

Synthetic aperture radar (SAR) data at X-, C-, and L-bands have been investigated to determine the relationship between backscatter and forest burn severity over three sites in Spain. The dependence of SAR backscatter on local incidence angle and environmental conditions has been analyzed. At HH and VV polarizations, the backscatter increased with burn severity for X- and C-bands, whereas it decreased for L-band. Cross-polarized (HV) backscatter decreased with burn severity for all frequencies. Determination coefficients were used to quantify the relationship between radar backscatter and burn severity for given intervals of local incidence angle. For X- and C-band copolarized data, higher determination coefficients were observed for slopes oriented toward the sensors, whereas for cross-polarized data, the determination coefficients were higher for slopes oriented away from the sensor. At L-band, the association strength of cross-polarized data to burn severity was high for all local incidence angles. C- and L-band cross-polarized backscatter showed better potential for burn severity estimation in the Mediterranean environment when the local incidence angle is accounted for. The small dynamic range observed for X-band data could hinder its use in forests affected by fires.

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 classifier. The two SAR intensities involved in this ratio can be measurements from different dates, polarizations or possibly also 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. The effect of calibration errors such as channel gain imbalance and radiometric stability is assessed in the general case. Results indicate that channel gain imbalance is generally not a decisive parameter, but that radiometric stability is more critical in methods based on the temporal change. This error model can be used to test the impact of other SAR system parameters (time-lapse between repeat-pass orbits, ambiguity ratio, number of looks) and constitutes a tool for the design of future SAR missions and for the development of robust classification methods using existing SAR instruments.

With the continued threat of global warming, the need to obtain consistent and accurate measurements of the carbon stored in forests is strong. L- and P-band SAR backscatter data have shown to be sensitive to forest biomass, which in turn is coupled to the stored carbon. In this paper a biomass retrieval method is developed for L- and P-band using data from the BioSAR campaign conducted in Sweden during the spring 2007 over hemi-boreal forest. The results show that the use of L-band data gives an underestimation of biomass for stands with high biomass; while for P-band no such underestimation is seen. RMSEs are found to be 30-40% of the mean biomass for L-band and about 25% for P-band for stands with biomass ranging from 10 to 290 tons/ha.

The rice cropping system in Asia is undergoing major changes to cope with increasing demography and changing climate, making rice monitoring a critical issue. Past studies have demonstrated the use of C-band synthetic aperture radar (SAR) data to map rice areas. The methods were based on the temporal change of intensity backscattering coefficient of vertically or horizontally co-polarized data (VV or HH). In this paper, we assess the use of the HH/VV polarization ratio derived from Advanced SAR (ASAR) data from ENVISAT data for the production of rice paddy maps. The approach is based on past knowledge on the polarization behavior of rice canopy, i.e., VV backscattering is much lower than HH during a large part of the rice season, due to the attenuation of the wave by the vertical structure of the plants. The methodology is developed for the Mekong Delta, Vietnam, where a complex cropping pattern is found (one to three crops of rice per year). The approach includes a statistical analysis of the HH/VV distributions of rice and non-rice classes at different dates. The analysis results confirm that HH/VV can be used as classifier and point out the need for relevant speckle filtering prior to classification. A classification method is developed and applied to single- and multidate data sets. The methods are tested at one district of the province of An Giang and extended to the whole province. Comparisons of the mapping results to geographic-information-system land-use data and official agricultural statistics show very good agreement. The method will be further applied to the entire Mekong Delta.

L- and P-band airborne polarimetric synthetic aperture radar (SAR) data acquired by the RAMSES system over different height maritime pine (Pinus Pinaster Ait.) stands of the Nezer forest (Landes, France) have been evaluated for forest biophysical parameter estimation. A pseudolinear correlation has been brought to evidence at P-band between polarimetric anisotropy and mean tree height, which is also linked to other biophysical parameters in the Nezer forest, meaning that SAR polarimetry constitutes a promising tool for forest parameter retrieval at low frequency. The spatial conditions have been evaluated through the quantification of the impact of signal-to-noise ratio diminution and resolution degradation on the forest height inversion. It has been shown that the inversion accuracy remains acceptable forN Esigma0, representing the noise level of the SAR image, which is lower than -15 dB, and for spatial resolution increasing up to 15 m.

The purpose of this paper is to assess the use of C-band HH/VV backscatter ratio for mapping winter wheat. This paper analyzes two temporal series of images acquired in 2006 and 2007 by the Advanced Synthetic Aperture Radar (ASAR) system in alternating polarization (AP) mode, over an agricultural site located in southern Italy. Results on test data show that classification accuracies between 75% and 80% can be achieved by using a single ASAR image, acquired during the peak of the wheat-growing season. To achieve accuracies close to 90%, a spatial averaging at field scale is necessary.

Radar remote sensing technology has become an important method for stable and long-time rice monitoring for its capability to operate in all weather conditions. In this letter, ENVISAT advanced synthetic aperture radar (ASAR) alternative-polarization VV/HH data were used for rice monitoring in the Xinghua rice experiment site in the middle of Jiangsu Province. First, a threshold classification method was developed for mapping rice growth area according to the different characteristic of backscatter coefficients between paddy rice and other land surface objects. Then, relational models were built for retrieving rice growth parameters from ASAR images based on correlation analysis between backscatter coefficients and field measurements. Meanwhile, an optical multispectral image was used as ancillary data for rice parameters retrieval. As expected, the retrieved rice growth parameters were consistent with those of field measurements.

The Random Volume over Ground model has been extensively used for forest height inversion using Pol-InSAR data. Two different forest models are proposed to better represent the forest structure for height inversion using Pol-InSAR. The first one takes into account a vertically varying mean extinction coefficient and the second one considers contributions localized in a finite height interval.

Multi-temporal, multi-incidence ASAR WSM data over Siberia were used to derive land cover information. In an initial step several hundred ASAR WSM scenes were integrated into a database. Then the backscattering characteristics (signatures) of different land cover types were investigated. Based on the results, methodologies to retrieve land cover classes, including different forest biomass classes, were developed and applied. Parameters considered include a temporal backscattering variability parameter and an incidence angle dependence parameter.

This paper investigates the use of EVISAT/ASAR data for deriving forest information over Central Siberia. Temporal series of alternating polarisation data (HH and HV) at low and high incidence angles acquired at the Bolshemurstinsky test area are analysed. The backscattering coefficient of different forest types and land cover is analysed as a function of time, polarisation and incident angle using a GIS forest database available within the Siberia-II project. The results of the analysis indicate that HV data are adapted to forest/non forest mapping and both HH and HV are sensitive to low biomass values up to 50 m3/ha

Summary only given. Passive microwave observations from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and from the Special Sensor Microwave Imager (SSM/I) are used to analyse the evolution of the snow pack in the Ob river basin during the snow season of 2002-03. The Ob river is the biggest Russian river with respect to its watershed area (2975000 km/sup 2/). The Ob originates in the Altai mountains and flows northward across the vast West Siberian lowland towards the Arctic Ocean. The majority of snow cover is contained in the lowlands rather than in mountainous regions and persists for six months or more. During the snow season, surface air temperatures are very cold. Therefore, the combination of cold dry snow and large areas of uniform topography is ideal for snowpack extent and water equivalent retrievals from passive microwave observations. The thermal gradient through the snow pack is estimated and used to model the growth of the snow grain size and to compute the evolution of the passive microwave derived snow depth over the region. A comparison between the AMSR-E and SSM/I estimates is performed and the differences between the snow parameters from the two satellite instruments are analysed.

Radiative transfer models have been widely used to interpret the radar backscatter from forested areas. Most of these models are based on an iterative solution of the radiative transfer equation, usually solved up to first or second order, thus taking into account single and double scattering. Although this method leads to results agreeing well with copolarized backscatter measurements, it produces less accurate estimates for horizontal-vertical (HV) polarization. This paper presents a radiative transfer backscatter model that accounts for multiple scattering by using the discrete ordinate and eigenvalue method applied to a layered medium. Using parameters derived from an architectural tree model, calculations at C- and L-band are compared with HV data acquired for a maritime pine forest in the southwest of France during the Spaceborne Imaging Radar-C missions. Good agreement is found at C-band for all values of forest biomass, and reasonable agreement at L-band for high biomass, when the soil backscatter plays a minor role. For low biomass, the L-band modeling is inadequate because of difficulties in estimating the soil backscatter. Comparison with calculations from a first-order radiative transfer model shows that multiple scattering is significant, especially at C-band.

The objective of this paper is to investigate the effect of a two-scale surface roughness description on the polarization coherence in circular polarization (i.e. P/sub RRLL/). Under the assumption of sufficiently smooth surfaces, the small-slope approximation (SSA) model is employed to derive a first order expression for P/sub RRLL/. In the paper the sensitivity of P/sub RRLL/ to azimuth slopes is presented.

This paper aims to establish the joint roughness statistics for rms height s and correlation length l for agricultural bare soil fields and a variety of tillage conditions. To do so, we make use of a unique pan-European database of profile measurements covering five different sites and containing approximately 1.5 km of profile data. A preliminary assessment of the validity of the derived roughness statistics for electromagnetic scattering models is also carried out by comparing /spl sigma//sup 0/ predictions obtained using the integral equation model with derived roughness statistics and European Remote Sensing 1 and 2 (ERS 1/2) synthetic aperture radar observations. The results are then summarized within the overall context of roughness description and we discuss the implications in terms of forward modeling and inversion. The limitations of s and l parameters as roughness descriptors are also underlined.

The objective of this paper is to quantify the impact of the type of surface-profiling instrument on the roughness measurements in radar remote sensing studies. Particularly, the use of mechanical profilers as compared to more precise laser profilers is investigated. The motivations for this study are twofold. First, simple and inexpensive mechanical profilers will probably still be used extensively for in situ ground measurements in the next few years, e.g., to investigate the use of multipolarization, multiincidence angle satellite data, i.e., Advanced Synthetic Aperture Radar (ASAR) onboard the European Space Agency Environmental Satellite. Second, a great amount of roughness data have been acquired in the past by means of mechanical profilers and, to date, a quantification of the error budget affecting these measurements is still missing. The paper focuses on modeling and quantifying the measurement errors associated with profiles a few meters long. To determine the errors, we compare soil roughness measurements obtained using laser and mechanical profilers over agricultural surfaces with different roughness characteristics. The analyzed datasets consist of roughness measurements acquired over the Matera site (Italy) and the Marestaing site, near Toulouse (France), in 1998 and 2000, respectively. Analytical expressions for first and second statistical moments of roughness parameters as a function of different sources of measurement errors are derived and compared to experimental values. The results show that mechanical measurements, once appropriately calibrated, are in overall good agreement with laser measurements. Practical indications of the most appropriate profiler length and number of independent measurements to be recorded are also derived in the paper.

This paper investigates the relationship between C-band backscatter measurements and wheat biomass and the underlying soil moisture content. It aims to define strategies for retrieval algorithms with a view to using satellite C-band synthetic aperture radar (SAR) data to monitor wheat growth. The study is based on a ground-based scatterometer experiment conducted on a wheat field at the Matera site in Italy during the 2001 growing season. From March to June 2001, eight C-band scatterometer acquisitions at horizontal-horizontal and vertical-vertical polarization, with incidence angles ranging from 23/spl deg/ to 60/spl deg/, were taken. At the same time, soil moisture, wheat biomass, and canopy structure were collected. The paper describes the experiment and investigates the radar sensitivity to biophysical parameters at different polarizations and incidence angles, and at different wheat phenological stages. Based on the experimental results, the retrieval of wheat biomass and soil moisture content using Advanced Synthetic Aperture Radar data is discussed.

This paper describes a modeling approach to interpret the C-band synthetic aperture radar (SAR) data from wheat canopies as provided by European Remote Sensing (ERS) satellites, RADARSAT, and the forthcoming Environmental Satellite/Advanced Synthetic Aperture Radar (ENVISAT/ASAR) satellite. At a first step, the results of a first-order modeling were compared to ERS data and scatterometer data over the growing season at two different test sites. The prediction by first-order approach was in disagreement with the data from stem extension stage to soft ripening stage. The first-order approach was found to overestimate the attenuation at vertical (V) polarization, resulting in a predicted backscattering coefficient one order of magnitude lower than that observed by the SAR system. To improve the prediction, a multiple-scattering modeling based on numerical solution of multiple-scattering Foldy-Lax equation was used. The multiple-scattering modeling provides better backscatter estimates at vertical-vertical (VV) polarization for both test sites. Then, the model is used to derive the prevailing interactions mechanisms at horizontal-horizontal (HH) and VV polarizations and 23/spl deg/ and 40/spl deg/ of incidence angle. Finally, the retrieval of crop parameters from C-band SAR data is addressed.

Airborne L-band fully polarimetric SAR measurements over a boreal forest were analysed as a function of forest biomass. The HV backscattering coefficient was selected for inversion up to 70 m/sup 3//ha. HH appears to have information for stem volumes above 70 m/sup 3//ha. An inversion scheme was developed from this boreal forest and applied on a temperate forest site.

Assesses the feasibility of retrieving soil moisture content over smooth bare-soil fields using European Remote Sensing synthetic aperture radar (ERS-SAR) data. The roughness conditions considered in this study correspond to those observed in agricultural fields at the time of sowing. Within this context, the retrieval possibilities of a single-parameter ERS-SAR configuration is assessed using appropriately trained neural networks. Three sources of error affecting soil moisture retrieval (inversion, measurement, and model errors) are identified, and their relative influence on retrieval performance is assessed using synthetic datasets as well as a large pan-European database of ground and ERS-1 and ERS-2 measurements. The results from this study indicate that no more than two soil moisture classes can reliably be distinguished using the ERS configuration, even for the restricted roughness range considered.

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.

Investigates the relationships between VHF data and forest biomass using data acquired by the airborne imaging radar CARABAS over two different pine plantation forests in southern France. Data are analyzed using detailed ground truth measurements available on both sites. The backscattering coefficient is strongly correlated to characteristics of the tree trunk. Signal saturation is not observed up to 900 m/sup 3//ha. However, the sensitivity to the volume is high in the range of 0-500 m/sup 3//ha (e.g. 1 to 1.5 dB for 50 m/sup 3/ /ha), whereas it is reduced beyond 500 m/sup 3//ha (<0.5 dB for 50 m/sup 3//ha). The experimental analysis is supported by theoretical modeling using a coherent backscatter model based on the distorted Born approximation coupled with a tree growth model giving a fine and precise description of the trees at both sites. The modeling results show that the trunk is the main scatterer, but that, when the branch dimensions are not insignificant compared to trunk dimension, branch scattering needs to be accounted for. However, since the two species under study are both coniferous, branch dimensions are relatively small compared to trunk dimension. This explains no significant differences observed in the backscatter behavior between both sites, except for mature stands with low stem density. Finally, the effect of topography is investigated both experimentally, using a digital elevation model, and theoretically with the coherent model. The loss of sensitivity to stem volume due to slope is clearly demonstrated and explained by the decrease of the dihedral trunk-ground interaction as the slope increases.

The surface roughness parameters commonly used as inputs to electromagnetic surface scattering models (SPM, PO, GO, and IEM) are the root mean square (RMS) height s, and autocorrelation length l. However, soil moisture retrieval studies based on these models have yielded inconsistent results, not so much because of the failure of the models themselves, but because of the complexity of natural surfaces and the difficulty in estimating appropriate input roughness parameters. In this paper, the authors address the issue of soil roughness characterization in the case of agricultural fields having different tillage (roughness) states by making use of an extensive multisite database of surface profiles collected using a novel laser profiler capable of recording profiles up to 25 m long. Using this dataset, the range of RMS height and correlation values associated with each agricultural roughness state is estimated, and the dependence of these estimates on profile length is investigated. The results show that at spatial scales equivalent to those of the SAR resolution cell, agricultural surface roughness characteristics are well described by the superposition of a single scale process related to the tillage state with a multiscale random fractal process related to field topography.

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.

Two independent experimental efforts have examined the dependence of radar backscatter on above-ground biomass of monospecie conifer forests using polarimetric airborne SAR data at P-, L- and C-bands. Plantations of maritime pines near Landes, France, range in age from 8 to 46 years with above-ground biomass between 5 and 105 tons/ha. Loblolly pine stands established on abandoned agricultural fields near Duke, NC, range in age from 4 to 90 years and extend the range of above-ground biomass to 560 tons/ha for the older stands. These two experimental forests are largely complementary with respect to biomass. Radar backscatter is found to increase approximately linearly with increasing biomass until it saturates at a biomass level that depends on the radar frequency. The biomass saturation level is about 200 tons/ha at P-band and 100 tons/ha at L-band, and the C-band backscattering coefficient shows much less sensitivity to total above-ground biomass.>

The authors present the results of an experiment defined to demonstrate the use of radar to retrieve forest biomass. The SAR data were acquired by the NASA/JPL SAR over the Landes pine forest during the 1989 MAESTRO-1 campaign. The SAR data, after calibration, were analyzed together with ground data collected on forest stands from a young stage (eight years) to a mature stage (46 years). The dynamic range of the radar backscatter intensity from forest was found to be greatest at P-band and decreased with increasing frequencies. Cross-polarized backscatter intensity yielded the best sensitivities to variations of forest biomass. L-band data confirmed past results on good correlation with forest parameters. The most striking observation was the strong correlation of P-band backscatter intensity to forest biomass.>

A method is developed for optimizing the degree of polarization of a partially polarized wave reflected by a nonstationary scattering object. The method permits, for a scattered wave and a given target characterized by its Mueller matrix, analytic computation of the maximum and minimum values of the degree of polarization, and the corresponding transmitted polarizations. A procedure for the optimization of the scattered wave intensity is also proposed. The degree of polarization and the total scattered intensity extrema are then analyzed experimentally on JPL data. It is shown that several entities such as the received intensity extrema, the coefficient of variation, the fractional polarization and the span, which are currently used for target discrimination, can be deduced from combinations of the maximum and minimum values of the degree of polarization and the scattered wave intensity. Finally, a classification of the San Francisco image, based on these indices, is conducted for a better understanding of the specific physical meaning of each index.>

A branching model is proposed for the remote sensing of vegetation. The frequency and angular responses of a two-scale cylinder cluster are calculated to demonstrate the significance of vegetation architecture. The results indicate that the architecture of vegetation plays an important role in determining the observed coherent effects. A two-scale branching model is implemented for soybean with its internal structure and the resulting clustering effects considered. At the scale of soybean fields, the relative location of soybean plants is described by a pair distribution function. The polarimetric backscattering coefficients are obtained in terms of the scattering properties of soybean plants and the pair distribution function. Theoretical backscattering coefficients evaluated using the hole-correction pair distribution are in good agreement with extensive data from soybean fields. The hole-correction approximation, which prevents two soybean plants from overlapping each other, is more realistic and improves the agreement between the model calculation and experimental data near normal incidence.>