# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
## 2 Analysis of the Current Situation and Limitations of Bottom-Up Monitoring Methods
### Land Ecosystem Process Modeling  
The precision of fire carbon emission estimates can be enhanced by optimizing and refining input parameters, including fire area, combustible biomass, combustion efficiency, and emission factors. The main equation used in this model is:  
\[\frac{dC_{bio}}{dt}=GPP-R_{a}-LF-CE \tag{1}\]  
where the carbon emissions from fire (\(CE\)) are calculated as:  
\[CE=\sum_{i=1}^{n}BA\times F\times CF\times EF \tag{2}\]  
In this equation, \(BA\) represents the burned area, \(F\) represents the fuel biomass, \(CF\) is the combustion factor, and \(EF\) is the emission factor.  
These equations provide a comprehensive framework for estimating fire-related carbon emissions and serve as the basis for the enhanced model algorithms described below. (1) The use of enhanced model algorithms has led to an improvement in the precision of remote sensing inversion products pertaining to fire area. For instance, the MCD64A1 dataset is a newer satellite product designed for more accurate fire area detection. Compared to the earlier MCD45A1 dataset, the relative error of MCD64A1 has been reduced from 27.9% to 17.9% [11]. (2) Ecosystem process models may be employed to simulate the spatial distribution of fuel-based biomass, thereby replacing the land-use-type-based uniform biomass [12]. For instance, based on the global fire emissions database derived from the CASA (Carnegie-Ames-Stanford Approach) biogeochemical model, as documented by [PERSON] et al. (2017) [1], the BEAMS-Fire (Biosphere model integrating Eco-physiological And Mechanistic approaches using Satellite data-Fire) ecosystem model was employed to simulate the biomass of diverse forest types in Southeast Asia, subsequently enabling the estimation of carbon emissions [13]. (3) The application of vegetation indices and moisture conditions allows for the quantification of burning factors for different burn types in different pixels. This, in turn, enables the establishment of a correlation between the burn factor of each vegetation type and the moisture content of combustible materials under different moisture conditions [14]. This approach allows for the simulation of spatio-temporal patterns and changing characteristics of global burn factors, as well as the accurate reflection of combustion ratios of different combustible material types under different moisture conditions. (4) The emission factors of different vegetation types in various regional contexts, along with their spatio-temporal dynamic changes, are quantified through field surveys and indoor experimental analyses [15].  
The aforementioned models and methods have enhanced specific input parameters of the biomass burning emission inventory model, thereby reducing the uncertainty in biomass burning emission estimates. However, these methods typically improve only one or a few parameters independently, and few studies have systematically optimized all of these improvements in a unified manner. For instance, the MCD64A1 dataset improves the accuracy of burned area detection, while the CASA model enhances the precision of carbon emission estimates by optimizing biomass and fuel load parameters. While each method contributes to different aspects of fire carbon emission modeling, there remains significant potential for further improving the overall accuracy of these simulations.