# Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence
## 4 Discussion  
The results of this study showed that AGB and LAI, which were significantly correlated with cotton yield, were also significantly correlated with canopy SIF. At present, AGB and LAI have been proven to be important biometric indices affecting cotton yield [57; 58]. [PERSON] et al. [59] found that the fluorescence relationship between the leaves and canopy at 740 nm was mainly affected by LAI. [PERSON] et al. [60] showed that SIF increases with LAI; then, it is saturated at LAI > 2-4 depending on the spectral wavelength. [PERSON] et al. [42] concluded that SIF had a good relationship with aboveground biomass (R\({}^{2}\)= 0.65-0.82). These results indicate that SIF is indirectly related to the cotton yield through LAI and AGB. However, some studies have concluded that canopy SIF can be used as a crop monitoring indicator to directly estimate crop yield [23; 61]. The mechanisms and indicators of SIF change in response to yield before the cotton fluffing periods are currently unclear. LAI and AGB, which represent vegetation canopy structure and organic matter accumulation, respectively, are dynamic at various periods of cotton growth. Therefore, it is important to study the relationship between cotton canopy SIF and dynamic change indicators for cotton yield estimation. The optimal observation time for the model to estimate cotton yield based on canopy SIF was 14:00-15:00. The diurnal variation in cotton canopy SIF was basically consistent with that of photosynthetically active radiation (PAR), which reached the maximum value from 14:00 to 15:00. The R\({}^{2}\) of canopy SIF and yield model during the day basically showed a gradual increase from 10:00 to 14:00 and a gradual decrease from 15:00 to 19:00. This may be due to the fact that 14:00-15:00 is the midday period in Xinjiang when the light intensity is the highest. PAR drives photosynthesis and excites chlorophyll fluorescence [62]. SIF mainly responds to changes in PAR rather than actual changes in photosynthetic efficiency, and thus, photosynthetically active radiation is a key driver of SIF [63; 64]. It has been shown that PAR is one of the main sources of SIF inversion errors [65; 66]. Therefore, in this study, PAR was used to normalize SIF to mitigate the effect of light intensity on SIF. The optimal time for the R\({}^{2}\) for the canopy-based SIF estimation yield model for cotton with PAR effects removed was also 14:00-15:00. The optimum growth period for yield estimation using both SIF and SIFy was during the flowering and boll period with R\({}^{2}\) values of 0.9422 and 0.9671. The effects of LAI  
Figure 16: Yield estimation model verification of cotton based on canopy SIFy during the flowering and boll period. Figure 15: Yield estimation model verification of cotton based on canopy SIF during the flowering and boll period. and AGB on cotton canopy SIF and cotton yield were similar. During the whole growth period, LAI and AGB increased from the budding period to the flowering and boll period but decreased in the boll period. The correlation of LAI and AGB with cotton yield and SIF was elevated from the budding period, reached a maximum during the flowering and boll period, and declined during the boll period. The trend of the R\({}^{2}\) of the cotton yield model based on SIF and SIFy basically follows the pattern of LAI and AGB. It shows that LAI and AGB are factors that influence the SIF and yield models. This may be because when LAI and AGB increase, cotton is in a vigorous growth period, and due to the scattering and reabsorption effects inside the leaves and canopy [67], the SIF obtained from the canopy will also increase with the increase in light area. When LAI and AGB decreased, some cotton leaves fell off, resulting in a decrease in light area and AGB. In the boll period, cotton approaches maturity, leaves gradually age, and non-photochemical quenching (NPQ) increases [68], leading to a decrease in the correlation between SIF and yield, which increases the uncertainty of the SIF yield model in the later growth period.