Mapping Crop Emergence in Near Real-time at Field Scales Using High Temporal and Spatial Resolution Imagery: Challenges and Opportunities

Crop emergence is a key stage for crop development and crop growth modelling. Crop growth stages in the United States are reported weekly at state or district (multiple counties) level by the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) based on the field observations. However, the crop progress and condition ground data collection is time consuming and the summaries at the state and district level do not reflect the spatial variability within the unit. Remote sensing data have been used to extract vegetation phenology for many years. Because remotely sensed phenologic metrics (e.g. green-up dates) are different from crop physiological growth stages (e.g. emergence dates), their connections need to be established for use in crop modeling systems. Previous remote sensing phenology algorithms can detect early crop growth stages when corn and soybean are in V3-V4 stages (a few weeks since emergence). Most algorithms require entire or multiple years of remote sensing data and mainly used frequent but coarse resolution data (e.g. MODIS). To identify crop phenology at field scales, routine and frequent remotely sensed data at 30-m or less spatial resolution are required. In addition, mapping crop growth stages in near real-time is challenging since only partial year of time-series data to current date is available. Recently, the harmonization of Landsat-8 and Sentinel-2 provides opportunity to collect more frequent medium resolution data. In this presentation, we will first present algorithm and results using experimental VENμS images (2-day repeat at 5-10m resolution). The approach will be extended to include Landsat, Sentinel-2 and MODIS/VIIRS time-series. The utility of the algorithm in mapping early crop growth stages over a large area will be compared to the NASS crop progress reports. Opportunities and challenges to map crop growth stages in near real-time at field scales will be discussed.