Work

Hybrid retrieval method for leaf chlorophyll content mapping of winter wheat from EnMAP spaceborne imaging spectroscopy data

Year: 2026

Type: article

Abstract: Accurate mapping of leaf chlorophyll content (Cab) is critical for monitoring plant growth and maximising crop yields in sustainable precision farming. This study developed a nonparametric, hybrid retrieval method to map winter wheat (Triticum aestivum) Cab from EnMAP spaceborne hyperspectral data, using in situ data collected in southern Hungary. A simulated training dataset was optimised using principal component analysis, active learning (AL), and spectral and sampling reduction, then used to train a Gaussian process regression (GPR) model. Validation yielded R² = 0.76 and RMSE = 8.22 μg/cm². The best results were achieved after adding 101 AL samples to 50 starting samples (NRMSE = 15.72%). Adding non-vegetated (NV) spectra slightly reduced validation accuracy (NRMSE = 16.93%, R² = 0.74) but produced more consistent models able to handle spectral variability across the heterogeneous EnMAP scene. GPR also provides uncertainty estimates alongside trait retrievals, indicating model reliability.

Source: Geocarto International

DOI: 10.1080/10106049.2026.2743440