## 4 Discussion  
### Influence of Illumination on the Estimation of LAI  
CIs that use ratios between color channels are theoretically stable under variable illumination [22], and they are designed to cope with the variability of natural daylight illumination [46; 47]. However, our results were not consistent with this statement. After Retinex correction, there were significant increases in the correlations between CIs and LAI and the R2 values of the multiple regression models at all growth stages (Figure 4; Table 3). This demonstrated that variable illumination could have a significant impact on CIs. Similarly, [PERSON] et al. [23] found that RGB-derived CIs were substantially affected by variable illumination. This is primarily because when cloud cover reduces the amount of direct sunlight, the phenomenon of light scattering becomes more prominent at shorter wavelengths compared to longer wavelengths [48]. This has varying effects on different color channels that in turn affect RGB-derived CIs [23]. This could be the reason why, in our study, there were low correlations between LAI and CIs related to the blue channel except for C18.  
Therefore, it is necessary to select CIs based on specific conditions, and illumination correction is a crucial step for processing and application of RGB images. We found that CIs with strong resistance to illumination variation were not highly correlated with LAI. Hence, when choosing CIs to estimate LAI, it is important not to focus solely on their robustness to variation in illumination. Meanwhile, unlike [PERSON] et al. [23], C7, C16, and C19 in our study performed less effectively in dealing with variation in illumination compared to  
Figure 6: Absolute values of Pearson correlations between CIs (Retinex-corrected CIs and segmented CIs) and LAI. RC represents Retinex corrected Cis; SC represents segmented CIs. T, E, B, H, and G represent filtering stage, elongation stage, booting stage, heading stage, and grouting stage, respectively.  
C6 and C18. Due to the variation in cloud cover, the amount of transmitted blue light also differs [49], in turn significantly affecting the performance of the mentioned indices. Consequently, there is a reduced correlation between these indices and the LAI.  
When considering the entire growth period, we observed that after Retinex correction, the correlation between CIs and LAI showed the greatest enhancement at the jointing stage, while the improvement was relatively minimal during the grouting stage. Similar results were obtained from the regression analysis. After Retinex correction, the R\({}^{2}\) value of the multiple regression model at the elongation stage showed the largest improvement, while showing the least improvement at the grouting stage (Table 3). This could likely be attributed to the fact that at the elongation stage of the study area for early rice, there is largely cloudy weather. The variable illumination during UAV flights leads to spatial discrepancies in the color rendition of the captured images. This affects the correlation between CIs and LAI as well as the accuracy of the regression model. Retinex correction can effectively address this issue, thereby improving the correlations between CIs and LAI as well as the accuracy of the model [23]. In contrast, at the grouting stage, the color of the canopy gradually transitions from green to yellow. This is due to the inherent absorption and reflection characteristics of yellow objects, resulting in less variation in wavelength, as the perception of color in yellow objects is less affected by changes in illumination compared to green objects. Therefore, illumination plays a significant role in the estimation of LAI, and it is necessary to perform illumination correction when conducting LAI research with UAVs.