## 1 Introduction  
Aboveground biomass (AGB) includes all vegetation above the ground (i.e., stems, branches, bark, seeds, flowers, and foliage of live plants) and approximately 50% of its composition is carbon [1]. AGB usually measures in metric tons of dry matter per hectare (e.g., t ha\({}^{-1}\) or Mg ha\({}^{-1}\)) or in metric tons of carbon per hectare (e.g., t C ha\({}^{-1}\) or Mg C ha\({}^{-1}\)). The United Nations Framework Convention on Climate Change (UnFCCC) identified it as an Essential Climate Variable (ECV). Therefore, accurate information on biomass stock in world forests is necessary to reduce uncertainties and to fill the knowledge gaps of the climate system [2]. Further strong impetus to improve methods for measuring global biomass comes from the reduction of emissions due to deforestation and forest degradation (REDD) mechanism, which was introduced in the UNFCCC Committee of the Parties (COP-13) Bali Action Plan. REDD which is now popular with REDD+ (with additional elements of carbon stock enhancement and biodiversity conservation) is dedicated to the developing countries around the world including Malaysia. Its implementation relies fundamentally on systems to assess available carbon stock and monitor changes due to loss of biomass from deforestation and forest degradation [3], which are amalgamated in a system called monitoring, reporting, and verification (MRV). Remote sensing has been recognized as one of the primary spatial inputs for this process [4; 5; 6]. Satellite remote sensing technologies are currently widely tested and suggested as a tool in REDD+MRV. Along with scientific programs and field tests, there is also a debate as to the overall feasibility and cost-benefit ratio of remote sensing approaches, depending on the wide range of ecosystem and land use conditions as well as the range of approaches to carbon credit accounting [7]. In many parts of the world, especially in tropical region, the frequent cloud conditions often restrain the acquisition of high-quality remotely sensed data by optical sensors. The acquisition of cloud-free, wall-to-wall optical satellite images in tropical countries is almost impossible [8]. Thus, SAR data become the only feasible way of acquiring remotely sensed data within a given timeframe because the SAR systems are independent of cloud coverage, weather, and light conditions. Due to this unique feature compared with optical sensor data, the SAR data have been used extensively in many fields, including forest-cover identification and mapping, discrimination of forest from other land covers, and forest biomass estimation. Previous studies demonstrated that the L-band polarimetry backscatter tends to saturate at certain levels of biomass, and hence limits the accuracy of estimates [9; 10; 11]. However, the saturation level varies with the type and structure of the forest. It was demonstrated that the sensitivity of SAR polarimetry is depending on the structure, density, and tree elements (i.e., trunk/stem, branches, and leaves) of the forests [12]. Other than these issues, several other inter-related issues can affect the biomass estimations using remotely sensed data. These issues can be generalized into three major groups, which are (i) the natural conditions of the forest, (ii) the forest management system being practiced, and (iii) the technical issues related to the remote sensing system being used [13]. Short wavelength SAR sensors on board several satellites such as the Earth Resources Satellite (ERS-1), Radarsat, and Environmental Satellite (Envisat) have been used to quantify forest biomass. A number of studies have been conducted in relatively homogeneous or young forests, but the signal tends to saturate at low biomass (100-200 Mg ha\({}^{-1}\)) [14; 15; 16]. However, L-band SAR has shown better potential in retrieving the biomass of forests, including those in the tropics [17; 18; 19; 20; 21].