# Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US
## 1 Introduction
Since yield is related to the cumulative production of biomass during a growing season, integration of multi-source datasets achieves high accuracy in cotton yield estimation, e.g., \(R^{2}=89\%\) in the study of [PERSON] et al. [35]. By incorporating geospatial data from remote sensing, such as LAI, into a crop growth model, [PERSON] et al. [36] reported that the root-mean-squared errors between measured and simulated seed cotton yield were 5% or less. In contrast to the use of complicated crop growth models, the light use efficiency (LUE) model is simple for implementation in cotton yield estimation; the LUE model can incorporate some meteorological parameters, such as temperature and vapor pressure deficit, into the yield estimation process [37], while it often lacks the capability to simulate soil water stress and the mechanism to explain the effects of CO\({}_{2}\) fertilization (i.e., the increased rate of photosynthesis in plants that results from increased levels of carbon dioxide in the atmosphere) [38]. In addition to the improvements in modeling, the satellite images for cotton yield estimation evolved from low to high spatial resolution, e.g., Advanced Very-High-Resolution Radiometer [39], Moderate Resolution Imaging Spectroradiometer (MODIS) [40], Landsat [41], and Sentinel-2 [42]. In particular, the Sentinel-2 mission is a constellation that consists of twin satellites (Sentinel-2A and Sentinel-2B) and is operated by the European Union (EU) Copernicus Program. With these two satellites, Sentinel-2 is able to map land surface with 13 bands in the visible, near-infrared, and shortwave-infrared spectrum, a five-day revisiting time, and spatial resolutions of up to 10 m. Given its powerful capability of data collection and free, full, and open data policy, Sentinel-2 opens a new era for operational agricultural monitoring. With only Sentinel-2A data and a 10-day revisit interval, cloud contamination is an open issue for consistently tracking the growing conditions of cotton; this challenge is similar to using only Landsat 8 for crop growth monitoring. To mitigate the cloud contamination, the available approach is to blend MODIS and Landsat for practical mapping of crop progress [43; 44]. Since 2017, both Sentinel-2A and B data are available; this gives an opportunity to rely on a single data source for operational agricultural applications, as the probability of cloud contamination is reduced. So far, the utilities of Sentinel-2A and 2B data for cotton yield mapping are still limited [42; 45; 46]. The objective of this study was to evaluate the potential of Sentinel-2 data as the key input to simulate cotton GPP for the purpose of yield estimation in an ecosystem model. For this aim, we collected Sentinel-2 reflectance data to derive the time series of LAI as the input of an ecosystem model; the ecosystem model integrated LAI, meteorological data, and soil data to simulate cotton GPP; the cotton GPP-yield relationship was then calibrated using yield data collected from the National Agricultural Statistics Service (NASS) in the United States Department of Agriculture (Sections 2 and 3). The uncertainty of the GPP simulation and conclusions are summarized in Sections 4 and 5.