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

Lingling Liu · Xiaoyang Zhang · Yunyue Yu · Feng Gao · Zhengwei Yang

Remote Sensing · 25 Sept 2018 · 10.3390/rs10101540

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

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.

Datasetpublic

we used CDL in 2014 and 2015 that were publicly available at https://www.nass.usda.gov/Research_and_Science/Cropland/ Release/index.php or at USDA CropScape (https://nassgeodata.gmu.edu/CropScape/)

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