Estimating consistent large-scale tropical forest height using remote sensing is essential for understanding forest-related carbon cycles. The Global Ecosystem Dynamics Investigation (GEDI) light detection and ranging (LiDAR) instrument employed on the International Space Station has collected unique vegetation structure data since April 2019. Our study shows the potential value of using remote-sensing (RS) data (i.e., optical Sentinel-2, radar Sentinel-1, and radar PALSAR-2) to extrapolate GEDI footprint-level forest canopy height model (CHM) measurements. We show that selected RS features can estimate vegetation heights with high precision by analyzing RS data, spaceborne GEDI LiDAR, and airborne LiDAR at four tropical forest sites in South America and Africa. We found that the GEDI relative height (RH) metric is the best at 98% (RH98), filtered by full-power shots with a sensitivity greater than 98%. We found that the optical Sentinel-2 indices are dominant with respect to radar from 77 possible features. We proposed the nine essential optical Sentinel-2 and the radar cross-polarization HV PALSAR-2 features in CHM estimation. Using only ten optimal indices for the regression problems can avoid unimportant features and reduce the computational effort. The predicted CHM was compared to the available airborne LiDAR data, resulting in an error of around 5 m. Finally, we tested cross-validation error values between South America and Africa, including around 40% from validation data in training to obtain a similar performance. We recommend that GEDI data be extracted from all continents to maintain consistent performance on a global scale. Combining GEDI and RS data is a promising method to advance our capability in mapping CHM values.

Estimating tropical forests’ vertical structure using remote sensing is a challenge. Active sensors such as low-frequency synthetic aperture radar (SAR) operating at the P-band, with a wavelength of ~69 cm, 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 3-D images through the 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 the 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, GEDI vegetation height root-mean-square error (RMSE) 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 that 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. Indeed, as most deformation phenomena develop slowly, a strategy of a processing scheme can be worked on reduced volume data sets. 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. Both persistent and distributed scatterers (PSDS) are exploited in the ComSAR algorithm. The ComSAR performance is validated via simulation and application to Sentinel-1 data to map land subsidence of the salt mine Vauvert area, France. The proposed ComSAR yields consistently better performance when compared with the state-of-the-art PSDS technique. We make our PSDS and ComSAR algorithms as an open-source TomoSAR package. To make it more practical, we exploit other open-source projects so that people can apply our PSDS and ComSAR methods for an end-to-end processing chain. To our knowledge, TomoSAR is the first public domain tool available to jointly handle PS and DS targets.

Forest type classification using spaceborne remote sensing is a challenge. Low-frequency Synthetic Aperture Radar (SAR) signals (i.e., P-band, ∼0.69 m wavelength) are needed to penetrate a thick vegetation layer. However, this measurement alone does not guarantee a good performance in forest classification tasks. 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.

Exploring vertical forest structures worldwide via remote sensing faces challenges. Recent technologies like waveform LiDAR from NASA’s Global Ecosystem Dynamics Investigation (GEDI) and SAR Tomography (TomoSAR) from future European Space Agency (ESA) BIOMASS offer promising solutions. This paper 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. Ground elevation measurements are more accurate than canopy height estimates for temperate forests, with DTM RMSE about 2m and CHM RMSE about 3m for GEDI and TomoSAR measurements. By analyzing the vertical structure of monthly GEDI data, we note a 1-meter 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 meters 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.

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.