Spectral reflection and crop parameters: can the disentanglement of primary and secondary traits lead to more robust and extensible prediction models?
2023
Rose, Maren | Rose, Till | Kage, Henning
Recently the application of spectral reflection data for the prediction of crop parameters for applications in precision agriculture, such as green area index (GAI), total aboveground dry matter (DM), and total aboveground nitrogen content (N content) increases. However, the usability of vegetation indices (VI) for the prediction of crop parameters is strongly limited by the fact that most VI calibrations are only valid for specific crops and growth periods. The results of the presented study based on the differentiation of primary (main driver of the reflectance signal) and secondary (not directly related to reflectance signal) crop parameters. For GAI prediction, a universal (without crop-specific parametrization) simple ratio vegetation index (SR) provided good calibration (R2 adj. = 0.90, MAE = 0.32, rMAE = 22%) and evaluation results (MAE = 0.33, rMAE = 18%). The disentanglement of primary and secondary traits allowed the development of a functional two-step model for the estimation of the N content during vegetative growth (MAE = 19.2 g N m−1, rMAE = 44%). This model was based on fundamental, crop-specific relationships between the crop parameters GAI and N content. Additionally, an advanced functional approach was tested enabling the whole-season prediction of DM and confirming a reliable GAI estimation throughout the whole growing season (R2 = 0.89–0.93).
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