Real-Time Monitoring of Crop Phenology in the Midwestern United States Using VIIRS Observations

Abstract

Real-time monitoring of crop phenology is critical for assisting farmers managing crop growth and yield estimation. In this study, we presented an approach to monitor in real time crop phenology using timely available daily Visible Infrared Imaging Radiometer Suite (VIIRS) observations and historical Moderate Resolution Imaging Spectroradiometer (MODIS) datasets in the Midwestern United States. MODIS data at a spatial resolution of 500 m from 2003 to 2012 were used to generate the climatology of vegetation phenology. By integrating climatological phenology and timely available VIIRS observations in 2014 and 2015, a set of temporal trajectories of crop growth development at a given time for each pixel were then simulated using a logistic model. The simulated temporal trajectories were used to identify spring green leaf development and predict the occurrences of greenup onset, mid-greenup phase, and maximum greenness onset using curvature change rate. Finally, the accuracy of real-time monitoring from VIIRS observations was evaluated by comparing with summary crop progress (CP) reports of ground observations from the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA). The results suggest that real-time monitoring of crop phenology from VIIRS observations is a robust tool in tracing the crop progress across regional areas. In particular, the date of mid-greenup phase from VIIRS was significantly correlated to the planting dates reported in NASS CP for both corn and soybean with a consistent lag of 37 days and 27 days on average (p

Plant phenotyping relevance

VIIRS・MODISによる作物フェノロジー（緑化開始、成長段階、最大緑度）の推定手法を提示し、地上観測報告との精度比較で検証しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper uses two public datasets directly in its phenotyping analysis: USDA NASS Crop Progress field observations (via QuickStats) for evaluation, and the Cropland Data Layer (via CropScape/Cropland release) to identify 'pure' corn and soybean VIIRS pixels. No author code, models, or derived data deposits are stated.