Improving crop condition monitoring using phenologically aligned vegetation index anomalies – A case study in central Iowa
Timely monitoring of crop conditions is essential for optimizing and assessing agricultural management. Vegetation indices (VIs) derived from remote sensing data can be useful for assessing crop conditions on a large spatial scale. Traditional crop condition assessments compare a VI in the current y...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-05-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225001736 |
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| Summary: | Timely monitoring of crop conditions is essential for optimizing and assessing agricultural management. Vegetation indices (VIs) derived from remote sensing data can be useful for assessing crop conditions on a large spatial scale. Traditional crop condition assessments compare a VI in the current year to a baseline VI, averaged over multiple years. However, comparing VIs across years by calendar day may not capture the general crop condition at the same development stage due to interannual variability in planting date and weather. This study proposes a phenological alignment approach for assessing differences in corn and soybean crop condition at commensurate growth stages rather than by day of year. The analysis was conducted in central Iowa, U.S. from 2018 to 2023, which included periods of drought and excess rainfall, providing a high interannual variability in crop phenology and condition. Weekly crop condition and seasonal yield data reported by the USDA National Agricultural Statistics Service (NASS) were compared with aggregated Enhanced Vegetation Index (EVI2) anomalies to evaluate relationships both spatially and temporally. Three EVI2 anomaly time series were computed with temporal alignment based on: day of the year (DOY), days after emergence (DAE), and accumulated growing degree day (AGDD), with a scaled time axis aligned at the emergence date. For the DAE- and AGDD-aligned EVI2 time series, emergence date was determined using a within-season emergence detection approach based on remote sensing. Results showed that EVI2 anomalies perform well in crop condition assessment at 30-m resolution, and correlations improved with DAE and AGDD corrections to the EVI2 time series, reducing the effects of yearly differences in crop phenology. The proposed method has potential for improving within-season crop condition monitoring and yield prediction. |
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| ISSN: | 1569-8432 |