# Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US
## 1 Introduction  
There are two major cotton types in the United States (US), one is the Pima cotton, located in the counties of Pinal and Graham in Arizona, and another type is the upland cotton (_Gossypium hirsutum_), a species that is native to Central America, Mexico, and the Caribbean and Gulf Coast regions, and is mainly located in southern states below \(36^{\circ}30^{\prime}\) north (N), since cotton grows well at higher temperature and in sunlight. Upland cotton is the most common type of cotton in the US, making up 95% of the planted cotton area in the US, and it is the focus of this study. Although cotton is naturally a perennial shrub, it is primarily grown as an annual crop to help pest control. In the US, the cotton is planted in spring, varying from the beginning of February to the beginning of June, and harvested in the fall. Its growing season is approximately 150 to 180 days, making it the longest of annually planted crops in the US. Since cotton is a thermophilic crop, the growing season length is important to the cotton yield. Although cotton is somewhat salt- and drought-tolerant and is an attractive crop for arid and semiarid regions, the cotton yield heavily relies on irrigation. The cotton yield is determined by a few key factors, including species and genetics [1], soil [2] and nutrition [3, 4] conditions, weather [5], irrigation [6, 7], and management events [8]. Specifically, cotton is a perennial warm-season crop that requires a long growing period. Therefore, the length of growing season for cotton has a significant effect on cotton yield. For example, lint yield in double-cropping systems is significantly lower than that in monocultures [8, 9]. Reliable estimation of cotton yield relies on the available information during the cotton growing season. Crop growth models, such as the Decision Support System for Agrotechnology Transfer(DSSAT) [10], are very mature in yield simulations at the plot level; however, they heavily rely on accurate inputs, such as numerous cotton traits, which are usually unavailable for large-scale applications. In contrast, remote sensing was proven as a practical approach for yield estimation at the regional and global scales [11; 12; 13; 14; 15; 16; 17; 18]. However, the accuracy of cotton yield estimation based on remote sensing still relies on how much information is used to capture the important physiological stages during cotton development [19; 20; 21; 22]. Using a single vegetation index [23], such as normalized difference vegetation index (NDVI), from a specific date (e.g., the maximum NDVI) can only provide limited information about cotton yield. For example, directly relating NDVI to cotton yield often gives low correlation (\(R^{2}=0.47\)) [24]. [PERSON] et al. [25] suggested that lint yield best correlates to the canopy reflectance indices measured at the early flower stage of cotton growth (\(R^{2}\) of 0.56-0.89). [PERSON] et al. [26] found that cotton yield variation (\(R^{2}=0.52\)) is mostly explained by airborne hyperspectral imagery collected 9.9 weeks after planting. Inclusion of multitemporal remote sensing to capture the phenology of cotton improves the accuracy of cotton yield estimation [27; 28; 29; 30]. Further improvement of the estimation of cotton yield requires the integration of a crop growth simulation model, remote sensing, and climate data via a data assimilation approach [31; 32; 33]. Essentially, remote sensing provides the biophysical parameters, e.g., leaf area index (LAI), to the crop growth model for the simulation of gross primary production (GPP), net primary production (NPP), and cotton biomass, where GPP and NPP are the indicators of crop productivity [34]. GPP is the rate at which plants capture and store atmospheric carbon dioxide to generate oxygen and energy as biomass. NPP is the difference between GPP and the energy lost during plant autotrophic respiration. NPP, thus, represents the daily accumulation of standing biomass.