Relationships among vegetation indices for different crops in the Northern Great Plains

Abstract Wide‐spread adoption of proximal sensors in crop health assessment requires understanding of changes in canopy reflectance during the growing season and associations among readings from different sensors. Chlorophyll meter reading (Soil Plant Analysis Development, SPAD), red normalized difference vegetation index (RNDVI), and red‐edge normalized difference vegetation index (RENDVI) were measured for sugarbeet ( Beta vulgaris L.), corn ( Zea mays ), sunflower ( Helianthus annuus L.), soybean ( Glycine max ), and spring wheat ( Triticum aestivum ) throughout the 2021 growing season. Cumulative growing degree days (GDD) had a significant relationship with SPAD, RNDVI, and RENDVI. The correlation coefficient indicated SPAD was more associated with RENDVI ( r = 0.73) than RNDVI (0.50). The R 2 values of multiple linear regression of SPAD and GDD with RNDVI and RENDVI were the lowest for spring wheat (0.33 and 0.52, respectively) and the highest for corn (0.94 and 0.95, respectively). For all major five crops grown in the Northern Great Plains, GDD showed a strong relationship with all three indices. For in‐season crop health assessment based on vegetation indices, inclusion of GDD could be a useful predictor variable to develop a single model algorithm applicable for multiple crops at a regional scale.

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Publication Details

Journal
Crop Forage & Turfgrass Management
Published
2025-04-08
DOI
https://doi.org/10.1002/cft2.70041
Citations
1
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
1.72
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article

Relationships among vegetation indices for different crops in the Northern Great Plains

Amitava Chatterjee
1 citations
Crop Forage & Turfgrass Management
Remote Sensing in Agriculture
1.72
article

Relationships among vegetation indices for different crops in the Northern Great Plains

Amitava Chatterjee
article en
1 citations

Abstract

Abstract Wide‐spread adoption of proximal sensors in crop health assessment requires understanding of changes in canopy reflectance during the growing season and associations among readings from different sensors. Chlorophyll meter reading (Soil Plant Analysis Development, SPAD), red normalized difference vegetation index (RNDVI), and red‐edge normalized difference vegetation index (RENDVI) were measured for sugarbeet ( Beta vulgaris L.), corn ( Zea mays ), sunflower ( Helianthus annuus L.), soybean ( Glycine max ), and spring wheat ( Triticum aestivum ) throughout the 2021 growing season. Cumulative growing degree days (GDD) had a significant relationship with SPAD, RNDVI, and RENDVI. The correlation coefficient indicated SPAD was more associated with RENDVI ( r = 0.73) than RNDVI (0.50). The R 2 values of multiple linear regression of SPAD and GDD with RNDVI and RENDVI were the lowest for spring wheat (0.33 and 0.52, respectively) and the highest for corn (0.94 and 0.95, respectively). For all major five crops grown in the Northern Great Plains, GDD showed a strong relationship with all three indices. For in‐season crop health assessment based on vegetation indices, inclusion of GDD could be a useful predictor variable to develop a single model algorithm applicable for multiple crops at a regional scale.

Crop Forage & Turfgrass ManagementVol. 11(1)
National Laboratory for Agriculture and the Environment (US)
Openalex Percentile: Top 18%
Remote Sensing in Agriculture
1.72
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