# Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US
## Materials and Methods
### Sources of Model Input
For the neural network training, TOC reflectances were simulated with the PROSPECT + SAIL (Scattering by Arbitrarily Inclined Leaves) model [78] corresponding to the eight Sentinel-2 bands and considering variations of primary biophysical variables related to leaf optical properties and to the canopy structure. According to [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [77], the uncertainty of LAI retrievals from SNAP is 0.51 m\({}^{2}\)/m\({}^{2}\). The LAI products are derived in 20-m resolution. Although Sentinel-2 has a high revisit frequency, the availability of cloud-free and cloud shadow-free images is limited depending on the tile location. The LAI retrievals from SNAP are often contaminated by cloud and cloud shadow; therefore, abrupt decreased LAI values are shown in the time series. To minimize the effects of cloud contamination, a locally adjusted cubic-spline capping (LACC) method was used to screen affected LAI data points and to replace them through temporal interpolation. [79]. The interpolated LAI data at the daily step were used as input to BEPS for GPP simulation. A global reanalysis dataset, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), was chosen to drive BEPS simulation [80]. Six key meteorological elements including relative humidity, wind speed, and air temperature at 2 m above the surface, surface atmosphere pressure and incoming solar shortwave flux, and total precipitation at the surface level were resampled into 20-m resolution as input. In BEPS, the soil is stratified into five layers (up to 2 m depth) for the simulation of soil water and heat fluxes. For BEPS input, four soil attributes, including texture, bulk density, organic content, and reference depth, were extracted from Soil Grids ([[https://soilgrids.org](https://soilgrids.org)]([https://soilgrids.org](https://soilgrids.org))) [81], which provides global soil attributes at 250-m resolution. After the cotton is harvested from the field, the raw cotton (or seed cotton) goes through the ginning processing, in order to separate the raw fiber (cotton lint) from the seeds and other debris. The raw fiber is compressed into bales. According to the Joint Cotton Industry Bale Packaging Committee (JCIBPC) in the US established in 2001, a standard cotton bale weighs 500 pounds. However, specific cotton bale weight varies between 480 and 500 pounds in different states; for example, the average bale weight in 2017 was 490 pounds ([[https://www.nass.usda.gov/Publications/Todays_Reports/reports/ctgnan18.pdf](https://www.nass.usda.gov/Publications/Todays_Reports/reports/ctgnan18.pdf)]([https://www.nass.usda.gov/Publications/Todays_Reports/reports/ctgnan18.pdf](https://www.nass.usda.gov/Publications/Todays_Reports/reports/ctgnan18.pdf))). For statistical purposes, NASS often reports cotton yield based on 480-pound net weight bales. In this study, we used the reported planted area (acres) and quantity of bales to calculate yield, and the bale weight was taken as 490 pounds according to the NASS statistics. The cotton yield data at the county level in 2017 from NASS were used in this study. The NASS cotton yield data are based on surveys and are the only publicly available data that cover statistics of cotton yield for all US counties. The USDA Cropland Data Layer (CDL) product in 2017 [82; 83] was used to mask out non-cotton areas for GPP simulation in the same year. The simulated cotton GPP values at 20-m resolution for each US county were averaged according to their Federal Information Processing Standard (FIPS) county code; the county-average GPP for cotton was then compared to the county-level yield data from NASS.