# Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence
## 1 Introduction  
As an important cash crop in the world, cotton is the main raw material for the textile industry and has important uses in the defense, pharmaceutical, and automotive industries [1]. In agricultural production, crop yield forecasting is of great practical importance for producers, allowing them to make rational management decisions, for example, decisions on crop insurance, storage requirements, and cash flow budgets [2]. Cotton's potential yield can be predicted by physiological parameters such as the leaf area index (LAI) and aboveground biomass (AGB) during crop growth and development [3; 4]. Since the application rate of nitrogen fertilizer has a significant effect on both the cotton yield and the physiological parameters [5], predicting the yield is also an important guide for nitrogen application. Currently, crop yield estimation methods mainly comprise traditional agricultural methods as well as remote sensing technology. Traditional agricultural yield estimation methods require many years of ground data sampling and a large amount of computational processing to build a complete crop yield estimation model; they are time-consuming, involve large survey tasks, high cost, poor timeliness, and other shortcomings, and cannot be instantly applied to a wide range of crop growth monitoring projects [6]. With the rapid development of remote sensing technology in recent years, it has become possible to continuously monitor the growth of agricultural commodities and to extract or estimate crop growth parameters that are closely linked to crop yields, such as the vegetation index, leaf area index, biomass, and productivity [7; 8]. This technology has thus become an important tool for crop yield prediction at the regional scale [9; 10]. Optical remote sensing, such as hyperspectral techniques, inverts the main biometric and chemical parameters of crops based on spectral profiles and the main parameters of the relevant characteristics, and the reflectance signals characterize the dynamics of the biometric characteristics of the plant [11; 12]. Many studies use spectral band reflectance [13], vegetation indices (VIS) [14], the fraction of absorbed photosynthetically active radiation (fAPAR) [15], and lidar-based measurements to estimate crop yield [16]. However, many remote sensing variables have a high correlation with yield, where the prediction accuracy of crop yield cannot fully meet the demand [17; 18; 19]. Early in the plant's exposure to external environmental stresses, such as nitrogen stress [20], and before changes in leaf pigmentation and canopy structure occur, the plant is able to adapt to environmental changes by regulating internal physiological mechanisms, such as a decrease in the rate of photosynthesis [21], which leads to an increase in chlorophyll fluorescence and heat dissipation [22]. Therefore, chlorophyll fluorescence, which can directly reveal the internal physiological changes of crops [23], has become a hot research topic in recent years. Sun-induced chlorophyll fluorescence (SIF) is a spectral signal emitted by the photochemical system of plants between 650 and 800 nm under sunlight conditions [24]. As an ideal \"probe\" for detecting crop photosynthesis, chlorophyll fluorescence can directly reflect the dynamics of actual crop photosynthesis and is superior to \"greenness\"-based vegetation indices in crop monitoring [25; 26]. A strong linear relationship between crop SIF values and gross primary productivity (GPP) has been observed [27; 28; 29], with GPP being the main photosynthetic flux directly related to crop yield [30; 31]. This relationship therefore provides the basis for a model to estimate crop yield based on SIF [32; 33]. In recent years, SIF has offered unique potential for crop yield estimation. [PERSON] et al. [34] presented a framework for estimating hybrid crop yields using SIF and analyzed the correlation between the net primary productivity (NPP) of crop yields and the NPP estimated by SIF.