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Abstract
High-resolution data with nearly global coverage from Sentinel-2 mission open a new era for crop growth monitoring and yield estimation from remote sensing. The objective of this study is to demonstrate the potential of using Sentinel-2 biophysical data combined with an ecosystem modeling approach for estimation of cotton yield in the southern United States (US). The Boreal Ecosystems Productivity Simulator (BEPS) ecosystem model was used to simulate the cotton gross primary production (GPP) over three Sentinel-2 tiles located in Mississippi, Georgia, and Texas in 2017. Leaf area index (LAI) derived from Sentinel-2 measurements and hourly meteorological data from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis were used to drive the ecosystem model. The simulated GPP values at 20-m grid spacing were aggregated to the county level (17 counties in total) and compared to the cotton lint yield estimates at the county level which are available from National Agricultural Statistics Service in the United States Department of Agriculture. The results of the comparison show that the BEPS-simulated cotton GPP explains 85% of variation in cotton yield. Our study suggests that the integration of Sentinel-2 LAI time series into the ecosystem model results in reliable estimates of cotton yield.
Highlights
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◦30 north (N), since cotton grows well at higher temperature and in sunlight
Boreal Ecosystems Productivity Simulator (BEPS) was initially developed for boreal ecosystems, it was expanded and used for temperate and tropical ecosystems in regional and global scales [48,51,52,54,55,56,57,58,59] and for the estimation of winter wheat yield in China [18], as C3 plants share the same photosynthesis theory at the leaf level, i.e., Farquhar’s leaf-level biochemical model [60], and BEPS has a “two-leaf” approach to upscale the ecosystem gross primary production (GPP) to canopy level for various ecosystems including crops [49,61]
We evaluated the potential of Sentinel-2A and 2B data for mapping cotton lint yield in an ecosystem model
Summary
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◦30 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. 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. Cotton is somewhat salt- and drought-tolerant and is an attractive crop for arid and semiarid regions, the cotton yield heavily relies on irrigation
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