# Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence
## 1 Introduction
It was found that the yield estimation model using only NIRv data had an accuracy of 64%, while the addition of SIF data increased it to 69% [35]. It has been shown that crop SIF is affected by incident radiation, and since photosynthetically available radiation (PAR) is absorbed by the plants, a small portion of the absorbed radiation is released in the form of SIF; therefore, SIF responds to changes in PAR [36; 37; 38]. Since SIF photons emitted inside the canopy are reabsorbed by other leaves, which depends not only on the optical properties of the leaf but also on the leaf areaindex (LAI) and canopy structure, the effect of canopy structure must also be considered when using remotely sensed SIF data to estimate large-scale GPP [39; 40]. For example, seasonal variation in the leaf area index (LAI) affects the different peak times of SIF and GPP, which in turn affects the relationship between far-red SIF and GPP [41]. [PERSON] et al. [42] explored the correlation between SIF and aboveground biomass (AGB) to assess the effects of drought on grasslands. Previous studies have also pointed out that the time scale of the observations can change the relationship between the observed SIF and GPP [43]. It has been shown that time averaging can mitigate the noise in SIF observations, leading to a tighter SIF-GPP relationship [44]. However, there is still a lack of direct evidence of noise reduction to improve the relationship between SIF and yield. Most of the current SIF studies are based on the satellite scale, which is highly affected by weather and has a low resolution [45]. It is difficult to accurately assess the performance of SIF in crop yield estimation, and ground-based sensors provide an ideal scale for experimental studies of SIF yield estimation. Therefore, to quantify the use of SIF for yield estimation, it is necessary to explore the effects of radiation and biometric parameters based on ground canopy SIF and establish a mechanistic understanding of their relationship with yield and the effects at different times. Data optimization and model integration can improve the performance of crop yield estimation models [46]. Machine learning methods are able to utilize empirical data to characterize the relationships between variables, thereby potentially improving the accuracy of the inversion of predictive models. [PERSON] et al. [47] used SIF to predict wheat yield in the Indo-Gangetic Plain and showed that machine learning outperformed two linear regression methods and deep learning methods in predicting wheat yield. [PERSON] et al. [48] used SIF to predict county summer corn yields, and the machine learning algorithm predicted yields with an overall accuracy of 90%. In recent years, various machine learning methods such as Random Forest (RF), Artificial Neural Network (ANN), and Adaptive Boosting (Ada Boost) have been successfully applied to explore the relationship between yields and predictors as they are able to cope with the nonlinear laws between the independent variables and the dependent variable, thus improving the performance of the models [49]. In order to realize cotton yield estimation based on canopy SIF, in this study, we set up field trials with different nitrogen fertilizer gradients. The changes of canopy SIF and the biometric parameters of cotton in different growth periods were analyzed. To investigate the effects of LAI and AGB on canopy SIF estimation of cotton yield, four algorithms, Ada Boost (Adaptive Boosting), Bagging (Bootstrap Aggregating), RF (Random Forest), and BPNN (Backpropagation Neural Network), were used to construct cotton yield estimation models based on the SIF and SIFy (the normalization of SIF by incident photosynthetically active radiation) for different time and growth periods. This study provides methodological and model support for the accurate estimation of cotton yield.