# Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS
## 4 Discussion  
As the commonly used remote sensing data cannot meet the high spatial and temporal resolution simultaneously, some researchers [51; 53; 73] have tried to obtain the high spatial and temporal resolution data by fusing the high spatial resolution data and the high temporal resolution data using the spatial and temporal data fusion model. However, there are still large difficulties and uncertainties in spatio-temporal data fusion during summer in cloudy and rainy areas. [PERSON] et al. [74] developed a methodology for downscaling the coarse spatial resolution of MODIS LAI images to a higher resolution, and the results showed moderate to high correlation for most cases, while lower accuracies  
Figure 8: Comparison of simulated daily LAI and measured LAI for two sample plots. (**a**) LAI of sample plot III; (**b**) LAI of sample plot IV.  
Figure 7: Normalized LAI curve based on MODIS of study area in 2019.  
were observed during the rainy season for all the study areas. Data fusion with high temporal and spatial resolution has been successfully applied to crop growth monitoring in previous research [64; 66]. However, it has rarely been applied in forest growth factor retrieval. For the Sentinel-2 data fused with the MODIS LAI data at a 4-day interval, this study proposes a fast forest land LAI reconstruction method at small and medium regional scales to reconstruct the LAI images of the Saihanba area.  
Sentinel-2 data have a spatial resolution of up to 10 m, which is higher than the Landsat series in all similar bands; therefore, it is helpful for producing higher spatio-temporal fusion image accuracy. The accuracy of Sentinel-2 data depends on its image level and processing level. [PERSON] et al. [75] proposed that there are marked differences between the processing levels and insignificant differences between the spatial resolutions of the SNAP-derived LAI. The analysis results in this study show that the multiplicative power regression model of the ARVI has proper inversion accuracy for the forested LAI in Saihanba.  
In this study, MODIS LAI data with a 4-day interval were used as the input data for the normalized LAI curves of forest land. The unsmoothed MODIS LAI curves showed large fluctuations, up and down, in the curves due to cloud and shadow images, but still showed the year-round variation trend. After LACC smoothing, the stand LAI curves conform to the year-round variation of tree leaves. By grouping the stand ages, it can be found that the year-round change trend of the stand LAI curve is almost the same between different stand ages. Therefore, this study concluded that the annual change trend in the LAI is not related to the age of the stand.  
The annual LAI extracted from the MODIS LAI was compared with the field observation, and the two were found to be in poor numerical agreement. After normalizing the two curves separately, the trends of the normalized curves were in good agreement. This indicates that the MODIS LAI normalized curves can better reflect the annual change of the forest land LAI.  
In this study, while the results are promising, there are several opportunities for enhancing the model further. First, the LAI dataset smoothed using the LACC algorithm for MODIS LAI products fluctuated slightly during the leaf spreading period. It was speculated that the reason for this was cloud interference. How to remove the cloud accurately needs further research and improvement. Second, based on the current study, results would be achieved if the measurements of the LAI as \(LAI_{max}\) and the LAI annual curve are carried out jointly. Third, broad leaved forest needs to be considered to improve this model in the future.