# 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
### Fire Radiation Power Method  
The fire radiation power (FRP) method employs real-time observations of fire points from platforms such as satellite remote sensing to ascertain the radiative energy of a fire. The carbon emissions resulting from the combustion of biomass are calculated by multiplying the rate of biomass consumption by a conversion factor. The fundamental equation employed in the model can be stated as follows:  
\[FRE=\int_{0}^{24}FRP_{peak}\Bigg{(}b+e^{-\frac{(t-b)^{2}}{2\alpha^{2}}}\Bigg{)}\ dt\ \times\ CR\ \times\ EF \tag{4}\]  
where \(FRP_{peak}\) is the peak fire radiative power and \(b\) represents the background power. \(t\) is time, \(h\) is when the peak occurs, and \(\sigma\) sigma\(\alpha\) reflects the fire's duration. \(CR\) converts fire radiative power into burned biomass, while \(EF\) converts burned biomass into carbon dioxide emissions. This method addresses the limitations of the conventional biomass burning emission estimation approach, which is based on the combustion area and unable to monitor carbon emissions from minor fire sources. This method meets the criteria for dynamic emission inventories and high-resolution datasets, as exemplified by the Global Fire Assimilation System (GFAS) [18]. In a study conducted by [PERSON] et al., the FRP method was employed for the estimation of biomass burning emissions in the North China Plain [19]. The resulting estimates were then compared with those obtained through the use of a terrestrial ecosystem model, specifically the Global Fire Emission Database (GFED) approach. It was determined that the GFED significantly underestimated the carbon emissions from biomass burning in the North China Plain due to the absence of identification and monitoring of minor radiating fire points [19]. [PERSON] et al. employed this methodology to estimate fuel consumption per unit area on the fire front and combined it with the diffusion rate to obtain the fire intensity and spread rate of Baalam in the Sahel [20]. This approach enabled the successful estimation of carbon emissions from biomass burning in this region. [PERSON] et al. developed a methodology based on the FRP method for estimating carbon emissions from the open burning of straw in various regions of China [21]. This approach demonstrated a high degree of consistency with field survey results, with a correlation coefficient of 0.70. This method offers a more precise and timely estimation of emissions from straw open burning. In a recent study, [PERSON] et al. employed the fire radiation power to estimate open biomass burning in the Heilongjiang River Basin [22]. They also established a high-spatial-and-temporal-resolution daily 1 km long time series emission inventory from 2003 to 2020. [PERSON] et al. employed MODIS fire counts and the FRP method, integrated with empirical data from the Tropospheric Sounding Instrument (TROPOMI), to elucidate the carbon emissions from forest fires in Australia during the early months of 2020 [23]. Integrating TROPOMI-derived NO\({}_{2}\) and CO data with MODIS data led to a notable improvement in estimation accuracy, with the explained variance for NO\({}_{2}\) increasing from 40% to 56% and for CO from 35% to 51%. This was particularly evident in the estimation of emissions from savanna and temperate forest fires. The FRP method is a monitoring technique that assesses carbon emissions from the standpoint of energy consumption and biomass combustion conversion efficiency. However, during the process of satellite identification and the quantification of fire points, there are significant fluctuations in the daily cycle parameters of fire radiation power intensity, which presents a challenge in continuously observing local fire point areas. This can result in errors of omission. Furthermore, the fire radiation power conversion factor is derived from traditional empirical statistics, which introduces a lag that affects the real-time and precise estimation of carbon emissions from diverse fire types.