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Due to the great structural and species diversity of tropical forests and limitations of the methods used to estimate aboveground biomass, there is uncertainty in quantifying its carbon sequestration potential. Measuring carbon sequestered in the terrestrial ecosystem and monitoring its dynamics is one of the key objectives in sustainable development goals. Synthetic Aperture Radar (SAR) has evolved as a key satellite technology in measuring and monitoring terrestrial carbon sink stored as biomass in plants. This study attempts to model forest above-ground biomass (AGB) using a random forest machine-learning approach where the predictor variables are from C-band (Radarsat-2), L-band (ALOS-2/PALSAR-2), and multi-temporal spaceborne LiDAR data from the GEDI platform. Training and validation data for the machine learning approach are obtained from the field measured inventory campaigns to evaluate the modeled forest biomass accuracies. The results show that variables from L-band (HH, HV), C-band (HV), and canopy height from the GEDI LiDAR platform performed effectively to model forest AGB with the coefficient of determination (R2) of 0.81 and root mean squared error (rmse) of 19.35 Mg/ha (%rmse – 17.17). In the case of single frequency SAR data, the analysis shows that the model derived from the L-band SAR data and LiDAR performed comparably better than the combination of C-band SAR and LiDAR data with an R2 of 0.78 and rmse of 21.36 Mg/ha (%rmse – 18.94). The results, thus, demonstrate the potential of SAR data (both single frequency and multiple frequencies) in combination with GEDI LiDAR data in effectively modeling AGB over highly biodiverse tropical forest regions.

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Canopy height is a critical parameter in quantifying the vertical structure of forests. Polarimetric SAR Interferometry (PolInSAR) is a radar remote sensing technique that makes use of polarimetric separation of scattering phase centers obtained from interferometry to estimate height. This article discusses the potential of the X-band PolInSAR pair for forest height retrieval over tropical forests in the Western ghats. A total of 19 fully polarimetric datasets with various spatial baselines acquired from November 2015 to February 2016 in bistatic mode are utilized in this study. After compensating for all possible non-volumetric decorrelations in the data-sets, the remaining volume decorrelation is modeled using a Random Volume Over Ground (RVoG) model to invert height from PolInSAR data. A modified three-stage algorithm developed by Cloude and Papathanassiou (2003) is adopted for height inversion. PolInSAR derived heights were cross-validated against reference height data measured during a field survey conducted in March 2019. RMSE values of all TerraSAR-X/TanDEM-X PolInSAR heights with respect to field measured heights range from 3.3 to 13.8 m and the correlation coefficient r2 varies between 0.16 and 0.79. The results suggest that the use of a dataset with optimal wavenumber can improve the tree height estimation process. The best performance was achieved for the dataset acquired on 11 December 2015 with RMSE = 3.4 m and r2 = 0.79. Furthermore, the effects of parameters such as angle of incidence, precipitation, and forest biomass on height inversion accuracy are assessed. A large-scale Shimoga Forest height map was generated using multiple TanDEM-X acquisitions with the best correlation results. To improve the accuracy of the height estimation, a merged height approach is explored. The best height estimates among all PolInSAR estimates for a given field plot are chosen in this regard. The merged height approach gave rise to an improved inversion accuracy with RMSE = 1.9 m and r2 = 0.92. The primary objective of this study was to demonstrate the ability of spaceborne X-band data to estimate height with maximum accuracy over natural forests in India, in which height retrieval research has seldom been done.