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

The digital elevation model (DEM) reconstruction accuracy of single-channel interferometric synthetic aperture radar (SC-InSAR) is limited by the SAR side-looking imaging geometry, decorrelations, phase unwrapping (PU), and so on. With the availability of increasing InSAR data, to overcome the limitations of SC-InSAR, a multi-source InSAR DEM reconstruction framework based on a complexity factor is proposed in this article. To simultaneously take the effects of noise level and terrain slope into account, a complexity factor for each interferometric pair is constructed. Next, to reduce the PU failure rate for each pair, this factor is used to guide the two-stage programming approach (TSPA) PU method. Then, to avoid the adverse effects of PU failure on elevation fusion, unreliable pixels of each pair are detected by exploiting the complexity factor. Finally, after multiple elevations from different side-looking directions are obtained, the elevation-weighted fusion is performed to reconstruct the final DEM in the map projection coordinate system. Experimental results on real multi-source InSAR data demonstrate that the complexity factor can effectively guide the steps of TSPA PU, detection of unreliable pixels, and elevation-weighted fusion in the proposed framework, thereby improving the DEM reconstruction accuracy for mountainous areas with complex and steep terrain.

Tomographic SAR (TomoSAR) at low frequency, i.e., P/L band, has become a promising tool for forest structure study. Forest canopy height and underlying topography are two of the most important parameters one can estimate using TomoSAR technique. One simple way to estimate these two parameters is to detecting the peaks of the tomographic profile, which, however, can lead to large biases due to complicated forest structure, sidelobes or insufficient TomoSAR resolution. Polarimetric TomoSAR (Pol-TomoSAR) provides a solution to this by exploring the polarimetric diversity to separate the ground and canopy components and then conduct independent TomoSAR analysis. However, Pol-TomoSAR technique suffers from low ground-to-volume ratio (GVR), which often leads to unsuccessful ground and canopy separation. To mitigate this propblem, in this paper, we provide a deep-learning solution to ground and canopy height estimation from 3D tomographic profile through the identification of the patterns of ground and canopy peaks. A 3D U-net model is introduced in our solution to grasp as much three-dimensional characteristics of the tomographic profile as possible. Moreover, our model can be well trained using only synthetic TomoSAR dataset, making it easy to implement when we don’t have enough real data with LiDAR references. The proposed method is validated on P-band real TomoSAR dataset from multiple test sites in AfriSAR campaign, showing that it can achieve more accurate ground and canopy height estimation than the state-of-the-art Pol-TomoSAR techniques. The maximum RMSE improvement reaches as high as 66.4% and 63.2% for ground and canopy top height, respectively.

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

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

Recently, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) radar interferometry technique has been implemented as an open-source TomoSAR package (https://github.com/DinhHoTongMinh/TomoSAR). TomoSAR offers state-of-the-art algorithms to capture your movement best. However, it is easy to make you crazy with memory requirements. Due to so many images to calculate, it says for only the covariance matrix with 200 images of 500x2000 size, 45 GB should be allocated for that. For a small computer, it can be a task impossible. For our cluster, the RAM is capacity up to TB. The good news is we can process free of charge for you under a scientific collaboration.

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.

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

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.

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

Modern Synthetic Aperture Radar (SAR) missions provide an unprecedented massive interferometric SAR (InSAR) time series. The processing of the Big InSAR Data is challenging for long term monitoring. This paper introduces a novel ComSAR algorithm based on a compression technique for reducing computational efforts while maintaining the performance robustly. The algorithm divides the massive data into many mini-stacks and then compresses them. The compressed estimator is close to the theoretical Cramer-Rao lower bound under a realistic C-band Sentinel-1 decorrelation scenario. The ComSAR performance is validated via simulation and application to Sentinel-1 data to map land subsidence of Mexico City.

SAR tomography, a technique employing multiple acquisitions over the same areas to form a three-dimensional image, has been demonstrated to improve SAR's capability in many applications. Our study shows the potential value of SAR tomography acquisitions to improve forest classification. By using P-band tomographic SAR data from the German Aerospace Center F -SAR sensor during the AfriSAR campaign in February 2016, the vertical profiles of five different forest types at a tropical forest site in Mondah, Gabon (South Africa) were analyzed and exploited for the classification task. We demonstrated that the high sensitivity of SAR tomography to forest vertical structure enables the improvement of classification performance by up to 33 %. Interestingly, by using the standard Random Forest technique, we found that the ground (i.e., at 5–10 m) and volume layers (i.e., 20–40 m) play an important role in identifying the forest type. Together, these results suggested the promise of the TomoSAR technique for mapping forest types with high accuracy in tropical areas and could provide strong support for the next Earth Explorer BIOMASS spaceborne mission which will collect P-band tomographic SAR data.

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

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.

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

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

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

A major issue affecting optical imagery is the presence of clouds. The need of cloud-free scenes at specific date is crucial in a number of operational monitoring applications. On the other hand, the cloud-insensitive SAR sensors are a solid asset and they provide orthogonal information with respect to optical satellite, that enable the retrieval of information lost in optical images due to cloud cover. In the context of an increasing availability of both optical and SAR images, thank to the Sentinel constellation, we propose a deep learning method to reconstruct (gap-fill) optical data, polluted by cloud phenomena, exploiting multi-temporal SAR and optical images.

Radar and Optical Satellite Image Time Series (SITS) are sources of information that are commonly employed to monitor earth surfaces for tasks related to ecology, agriculture, mobility, land management planning and land cover monitoring. Many studies have been conducted using one of the two sources, but how to smartly combine the complementary information provided by radar and optical SITS is still an open challenge. In this context, we propose a new neural architecture for the combination of Sentinel-1 (S1) and Sentinel-2 (S2) imagery at object level, applied to a real-world land cover classification task. Experiments carried out on the Reunion Island, a overseas department of France in the Indian Ocean, demonstrate the significance of our proposal.

In this paper, the impact of changing weather conditions on repeat pass SAR tomography is addressed to support the upcoming spaceborne mission BIOMASS. In recent years it has been demonstrated that forest biomass retrieval can be improved by using P-band SAR tomography in tropical forest. Yet, these results were obtained by using campaign data acquired in a single day, while the revisit time of BIOMASS mission will be 3-4 days. To fill this gap, we simulate BIO-MASS repeat pass tomography using ground-based TropiS-CAT data with revisit time of 3 days and rainy days included. It is observed that the backscattered power within canopy layer, which is significantly correlated to the forest biomass, stays stable under changing weather conditions. The backscattered power variation of canopy layer are within 1.5 dB. For this forest site, this error is translated into an AGB error of about 50-80 t/ha, which is 20% or less of forest AGB.

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.

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

The 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.

Mapping winter vegetation quality is a challenging problem in remote sensing. This is due to cloud coverage in winter periods, leading to a more intensive use of radar rather than optical images. The aim of this letter is to provide a better understanding of the capabilities of Sentinel-1 radar images for winter vegetation quality mapping through the use of deep learning techniques. Analysis is carried out on a multitemporal Sentinel-1 data over an area around Charentes-Maritimes, France. This data set was processed in order to produce an intensity radar data stack from October 2016 to February 2017. Two deep recurrent neural network (RNN)-based classifiers were employed. Our work revealed that the results of the proposed RNN models clearly outperformed classical machine learning approaches (support vector machine and random forest).

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

Developing and improving methods to monitor both natural and non-natural environments such as forest and urban in space and time is a timely challenge. To overcome this challenge, we created a software platform - TomoSAR. The kernel of this platform supports the entire processing from SAR, Interferometry, Polarimetry, to Tomography (so called TomoSAR). The objective of this paper is to introduce this platform about its capability in Persistent Scatterers Interferometry (PSI) technique to estimate subsidence using TOPS Sentinel-1 data.

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.

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.

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.

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.

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.

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.

In this paper we discuss some relevant features observed concerning wave penetration at P-band in boreal and tropical forests. The discussion will be based on results obtained from the multi-polarimetric and multi-baseline data-sets relative to the forest sites within the Krycklan river catchment, Sweden, and the area of Paracou in French Guyana, collected in the frame of the ESA campaign BioSAR 2008 and TropiSAR 2009, respectively. The analysis is carried out by exploiting the SAR tomography technique, which allows to separate backscattering contributions from different heights within the vegetation layer. One first relevant result is relative to the difference between the vertical distribution of the backscattered power in the two investigated test sites. In the boreal forest site the most relevant scattering contributions are observed at the ground level, not only in copolarized channels but also in HV, whereas in the tropical forest the presence of scattering from the ground is poorer and the vegetation volume is well visible. Most relevant features of the investigated tropical forest site are those relative to the dependency of the vertical backscattering distribution with respect to topographic slope and forest biomass. In particular, the innermost forest layer is observed to be substantially invariant to topographic slopes, whereas the backscattered power at 30 m above the ground is observed to yield the best connection with forest biomass, resulting, in this case, in a correlation factor of 0.82 with respect to in-situ measurements at 125 m spatial resolution.