# Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images
## 4 Discussion  
The traditional stepwise regression method was used for modeling, the R\({}^{2}\) between the predicted value and the actual value after adjustment of the linear model was only 0.3625, and the Pearson correlation coefficient between each band of Landsat 8 and Sentinel 1A VV and VH polarization mode was less than 0.36. The accuracy of the model can be significantly improved by adding vegetation index data and texture index data, which is consistent with many research results [36; 37]. At the same time, when the elevation data were input into the model, the dynamic elevation gradient of the study area had a greater impact on biomass, which is consistent with the research results of [PERSON] and [PERSON] [38]. Each band of Landsat 8 (band2-band7) was negatively correlated with biomass, and the other vegetation indices except the enhanced vegetation index were positively correlated with biomass, which was consistent with the research results of [PERSON] [39]. The correlation coefficient between the band2 value and biomass was the highest, which may be because the chlorophyll in green plants absorbed the most light, making the spectral curve of reflected plants at the lowest point in the spectral range. NDPI had the highest correlation coefficient among all vegetation indices. NDPI is the ratio of the difference between the sum of near-infrared light and red light and short-wave infrared light to the difference between the sum of near-infrared light, red light and short-wave  
Figure 10: Spatial distribution of above-ground biomass.  
infrared light. The main absorption band of chlorophyll is red light. The lower the red light brightness value, the lower the red light reflectivity, the higher the chlorophyll content and the larger the biomass.  
The running time and model determination coefficient R\({}^{2}\) of the three models were compared, as shown in Table 3. It can be seen that the model training speed based on the linear stepwise regression method is slightly faster than that of the BP neural network model, but the model accuracy is lower. The BP neural network model has high prediction accuracy and short prediction time, indicating that the nonlinear model is more suitable for the inversion of tree biomass in Liaoning Province. However, the prediction value of the improved PSO neural network model is better than that of the BP neural network model. Although the time is longer, it is also within the acceptable range.  
On the other hand, some output results were quite different from the measured value of biomass. In the validation of linear model results, the measured biomass distribution range was 0.56 kg/hm\({}^{2}\)-430 kg/hm\({}^{2}\), and the scattered points were evenly distributed on both sides of the line in a ratio of approximately 1:1. However, the distance between the scattered points and the line was relatively large, indicating a large deviation in biomass prediction and a large coefficient of determination. The decision coefficient value of 0.5295 also reflected this point. There were outliers such as (0.609, 163.863) in the model, which cannot be explained by the BP neural network model. The reasons for this phenomenon may be as follows:  
1. This experiment was only a preliminary study on a forest estimation model, and there were still some shortcomings and deficiencies in the specific technical processing. More parameter characteristics would be needed for further analysis in order to better understand the impact of biological factors on factors of quantitative inversion accuracy;
2. Due to limited conditions, the number of training data samples used in this experiment was small, which made the prediction network not stable enough and could cause certain errors in the results;
3. The data collected with remote sensing technology were greatly affected by factors such as sensors, shooting angles and atmosphere, which may cause the inversion errors.