Our study aims to provide a comparison of the P- and L-band TomoSAR profiles, Land Vegetation and Ice Sensor (LVIS), and discrete return LiDAR to assess the ability for TomoSAR to monitor and estimate the tropical forest structure parameters for enhanced forest management and to support biomass missions. The comparison relies on the unique UAVSAR Jet propulsion Laboratory (JPL)/NASA L-band data, P-band data acquired by ONERA airborne system (SETHI), Small Footprint LiDAR (SFL), and NASA Land, Vegetation and Ice Sensor (LVIS) LiDAR datasets acquired in 2015 and 2016 in the frame of the AfriSAR campaign. Prior to multi-baseline data processing, a phase residual correction methodology based on phase calibration via phase center double localization has been implemented to improve the phase measurements and compensate for the phase perturbations, and disturbances originated from uncertainties in allocating flight trajectories. First, the vertical structure was estimated from L- and P-band corrected Tomography SAR data measurements, then compared with the canopy height model from SFL data. After that, the SAR and LiDAR three-dimensional (3D) datasets are compared and discussed at a qualitative basis at the region of interest. The L- and P-band’s performance for canopy penetration was assessed to determine the underlying ground locations. Additionally, the 3D records for each configuration were compared with their ability to derive forest vertical structure. Finally, the vertical structure extracted from the 3D radar reflectivity from L- and P-band are compared with SFL data, resulting in a root mean square error of 3.02 m and 3.68 m, where the coefficient of determination shows a value of 0.95 and 0.93 for P- and L-band, respectively. The results demonstrate that TomoSAR holds promise for a scientific basis in forest management activities.

Ho Chi Minh City (HCMC), the most populous city and the economic center of Viet Nam, has faced ground subsidence in recent decades. This work aims at providing an unprecedented spatial extent coverage of the subsidence in HCMC in both horizontal and vertical components using Interferometric Synthetic Aperture Radar (InSAR) time series. For this purpose, an advanced InSAR technique PSDS (Permanent Scatterers and Distributed Scatterers) was applied to two big European Space Agency (ESA) Sentinel-1 datasets composed of 96 ascending and 202 descending images, acquired from 2014 to 2020 over HCMC area. A time series of 33 Cosmos SkyMED images was also used for comparison purpose. The combination of ascending and descending satellite passes allows the decomposition of the light of sight velocities into horizontal East-west and vertical components. By taking into account the presence of the horizontal East-west movement, our finding indicates that the precision of the decomposed vertical velocity can be improved up to 3 mm/year for Sentinel-1 data. The obtained results revealed that subsidence is most severe in areas along the Sai Gon river in the northwest-southeast axis and the southwest of the city with the maximum value up to 80 mm/year, consistent with findings in the literature. The magnitude of horizontal East-West velocities is relatively small and a large-scale westward motion can be observed in the northwest of the city at a rate of 2-5 mm/year. Together, these results reinforced the remarkable suitability of ESA's Sentinel-1 SAR for subsidence applications even for non-Europe countries such as Vietnam and Southeast Asia.

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.

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