## 1 Introduction
Today, due to relatively higher accuracy than conventional systems, fast and cost-effective performance remote sensing systems have gained much attention in the evaluation of the forest biomass and change assessment. Remote sensing technique usually evaluates biomass and change detection/information indirectly rather than directly by retrieving remotely sensed parameters. These extracted parameters help in evaluating \(AGB\), forest, and vegetation mapping and monitoring by integration, inversion, and modeling approaches [1; 6; 13; 14]. Further, high-quality data available from the state-of-the-art sensors onboard recently launched satellites by various space agencies are helpful in modeling ecosystems in an innovative manner, as well as to derive new improved land-based inventory systems for estimation of forest canopy height, biomass, carbon stocks, and spatiotemporal changes with relatively high accuracy [6; 13; 14]. The availability of Sentinel-2 imagery at high spatial resolution (10 to 60 m) from the Copernicus programme offers a new opportunity to study and evaluate forest bioresources and natural carbon stocks. Currently, remotely sensed multiple datasets through passive and active sources are being acquired from different platforms including spaceborne, airborne, and ground data. These data are being used for the noted applications by utilizing innovative processing approaches with advanced statistical, mathematical, and machine learning skills [11; 16; 17]. Usually, data from active sensors contain a high signal-to-noise ratio (\(S\):\(N\)) and better penetration compared to passive sensors. Thus, active systems such as synthetic aperture radar (\(SAR\)), light detection and ranging (_LIDAR_) can penetrate the canopy of the forests, vegetations, and land covers. Consequently, active remote sensing has become a powerful tool for vertical structural and volumetric measurements of the forests and biomass. Data from passive sensors have great potential for identification, classification, and mapping of forest cover, types, and vegetation classifications [14; 18; 19; 20]. Since the last decade, forest resource evaluation and atmospheric investigation were remarkably boosted by remote sensing techniques [11]. These techniques are frequently used for forest \(AGB\) estimation utilizing various datasets. Microwave data such as \(SAR\) is useful for texture characteristics and backscattered coefficient information extraction, and optical data such as Sentinel-2 are used for vegetation indices and leaf area index (_LAI_) information extraction [1; 14; 21]. For example, Sentinel-1 as \(SAR\) data C-band and Sentinel-2 optical imagery at high spatial resolution (10 to 60 m) are available freely from the European Copernicus Programme for effective vegetation and forest mapping and monitoring, biomass estimation, and natural resources assessments [1; 11]. Similarly, there are plenty of remote sensing datasets freely available from \(NASA\), \(ESA\), and other space agencies for the above applications. Different modeling and regression approaches between remote sensing microwave data (viz., Sentinel-1--vertical-vertical (_VV_) and vertical-horizontal (_VH_) polarizations) and optical data (viz., Sentinel-2--_LAI_ and normalized difference vegetation index (_NDVI_)) and field data are being developed at present for biomass assessments and ground truth validation [22]. For biophysical structure estimation, microwave data of Sentinel-1 were used to derive \(InSAR\)-based tree height estimation of a hilly forest. This study indicated a reduction in tree height estimation uncertainty even from an undulated terrain [23]. The optical images provide great potential for multitemporal monitoring, improvement in the classification of the forest type with high spatial resolution, and quick revisit time availability [24]. For example, optical remote sensing images viz. Sentinel-2 have been used by various researchers in forest classification, mapping, and monitoring [18; 19; 20; 25].