Improving crop condition monitoring using phenologically aligned vegetation index anomalies – A case study in central Iowa

Authors: ZHAO, H - Former ARS Employee; Gao, Feng; Anderson, Martha; Cirone, Richard; CHANG, JISUNG - Oak Ridge Institute For Science And Education (ORISE)

Submitted to: International Journal of Applied Earth Observation and Geoinformation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/5/2025
Publication Date: 4/11/2025
Citation: Zhao, H., Gao, F.N., Anderson, M.C., Cirone, R.J., Chang, J. 2025. Improving crop condition monitoring using phenologically aligned vegetation index anomalies – A case study in central Iowa. International Journal of Applied Earth Observation and Geoinformation. 139. https://doi.org/10.1016/j.jag.2025.104526.
DOI: https://doi.org/10.1016/j.jag.2025.104526

Interpretive Summary: Monitoring crop growth conditions is crucial for effective crop management and accurate yield prediction. The remote sensing vegetation index (VI) offers a valuable measure for assessing crop conditions by comparing current VI values with historical data from the same calendar day. However, since planting dates and growth stages can vary from year to year, comparing VI data from the same day across different years might lead to inaccuracies as the crops may be at different growth stages. This study adjusted the VI time series based on crop emergence dates and growing degree days to ensure VI comparisons are made at consistent crop growth stages. Experiments conducted in central Iowa demonstrate that this adjusted VI time series approach enhances the accuracy of crop condition assessments. This improved method provides a more reliable way to monitor crop conditions, benefiting crop management and yield estimation throughout the growing season.

Technical Abstract: Crop condition refers to the overall health and status of crops during their growth stages. The vegetation index (VI), derived from remote sensing data that reflects the condition, density, and overall health of vegetation, is a good indicator for assessing the crop condition on a large scale. VI, therefore, has been explored in many crop condition assessment efforts where VI in the current year was compared to the average VI over multiple years. However, averaging VI on the same calendar day may not reflect the general crop condition at the same growth stages due to their interannual variability. In this study, phenologically corrected Enhanced Vegetation Index (EVI2) for corn and soybean was generated at 30-m resolution to assess crop conditions at the same growth stages. The analysis was conducted in central Iowa, U.S., from 2018 to 2023, and involved climate conditions from drought to rain, providing a high interannual variability in crop phenology and condition. Weekly crop condition and seasonal yield reported by the USDA National Agricultural Statistics Service (NASS) were correlated with EVI2 anomalies computed using VI time series temporally aligned based on (1) day of the year (DOY), (2) days after emergence (DAE), and (3) a growing degree day (GDD) scaled time axis. Results showed that EVI2 anomalies perform well in crop condition assessment at 30-m resolution, and better with DAP and GDD corrections of VI time series, eliminating the effects of yearly differences. The assessment results with the NASS 9-km crop condition and county-level yields also indicated an improvement in correlation after the phenological corrections. The proposed method could potentially provide within-season crop condition monitoring technology and further facilitate in-season crop yield