# Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US
## Materials and Methods
### Sources of Model Input  
The Copernicus Sentinel-2 mission comprises twin polar-orbiting satellites in the same sun-synchronous orbit, phased at 180\({}^{\circ}\), aiming at monitoring land surface conditions at five-day intervals under cloud-free conditions. Each satellite carries a multispectral imager (MSI) with a swath of 290 km. The MSI provides a set of 13 spectral bands spanning from the visible and near-infrared to the shortwave-infrared, featuring four spectral bands at 10-m, six bands at 20-m, and three bands at 60-m spatial resolution. Sentinel-2A was launched on 23 June 2015 and Sentinel-2B was launched on 7 March 2017. In this study, Sentinel-2 Level-1C data, top-of-atmosphere (TOA) reflectances, were used as input to the Sen2 Cor atmospheric correction processor in order to convert the TOA reflectance images to the top-of-canopy (TOC) reflectance [75]. The Level-1C product results from using a digital elevation model (DEM) to project the images in cartographic geometry and is organized into tiles having an area of about 100 km\({}^{2}\) in the Universal Transverse Mercator projection and World Geodetic System 1984 ellipsoid; the Level-1C product is available from the Copernicus Open Access Hub ([[https://scihub.copernicus.eu/dhus/#home](https://scihub.copernicus.eu/dhus/#home)]([https://scihub.copernicus.eu/dhus/#home](https://scihub.copernicus.eu/dhus/#home))). Three tiles (14 SKB, 15 SYT, and 17 RKQ) in 2017 were chosen in order to cover a range of high to low cotton yields in southern US as summarized in Table 1. We chose 2017 as this is the first year when both Sentinel-2A and Sentinel-2B data were available in order to minimize the contamination of cloud for cotton yield mapping; furthermore, cotton yield data for validation in 2018 were unavailable at the time when the study was conducted. The tile in Georgia (GA; 17 RKQ) represents the cotton along the coastal states and a cotton yield in the middle ranges, the tile in Mississippi (MS; 15 SYT) represents the cotton with better irrigation conditions and a higher yield, and the tile in Texas (TX; 14 SKB) represents drier conditions and a lower cotton yield. The locations of these three tiles are shown in Figure 1. \begin{table}
\begin{tabular}{c c c} \hline
**Tile ID** & **Number of Images** & **Summary** \\ \hline
14 SKB & 25 & TX, mainly rainfed \\
15 SYT & 16 & MS, largely irrigated \\
17 RKQ & 20 & GA, mainly rainfed \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of Sentinel-2 images used in this study. The three tiles cover 17 United States (US) counties in total. ID—identifier; TX—Texas; MS—Missississippi; GA—Georgia. Figure 1: The locations of three Sentinel-2 tiles used in this study. Left tile: 14 SKB in Texas (TX); middle tile: 15 SYT in Mississippi (MS); right tile: 17 RKQ in Georgia (GA). The colors highlight the location of each county. LAI is defined as one-half of the total green leaf area per unit ground surface area (m\({}^{2}\)/m\({}^{2}\)) for broadleaf canopies [76]. It is an important structural property of vegetation. Because leaf surfaces are the common boundary for atmosphere-biosphere energy and mass exchange, important processes such as canopy interception, evapotranspiration, and gross photosynthesis are directly proportional to LAI. In BEPS, LAI is one of the most important parameters to determine the total photosynthetic rate at the canopy level. The LAI data are derived using TOC reflectance as the input to the biophysical processor available from the Sentinel Application Platform (SNAP). In SNAP, the LAI retrieval is based on an artificial neural network that relates TOC reflectances in eight bands and sun-view geometries to LAI [77]. The eight bands are B3 (560 nm), B4 (665 nm), B5 (705 nm), B6 (740 nm), B7 (783 nm), B8a (865 nm), B11 (1610 nm), and B12 (2190 nm).