Improved maize mapping using multi-source data fusion coupled with height-spectral Gaussian mixture modeling

Accurate and scalable maize mapping is essential for reliable yield prediction and efficient resource allocation. However, many existing approaches rely heavily on large training datasets and often underuse prior knowledge, which limits their performance in data-scarce regions and weakens spatiotemporal transferability. To address these challenges, we developed a Height-Spectral Gaussian Mixture Model (HSGMM) that integrates maize canopy relative height indicators from the Global Ecosystem Dynamics Investigation (GEDI) lidar shots and the plant nitrogen status indices derived from Sentinel-2 imagery. We propose a novel height label to accurately indicate crop relative heights and achieve robust spatial extrapolation across six different test sites. On the spectral side, we adapt the Dual-Peak Canopy Nitrogen Index (DCNI) to Sentinel-2 bands and combine it with the Red Edge Position to construct a composite maize separability index, termed the DCNI-REP index (DRI). HSGMM further incorporates an adaptive penalty with an optimized Bhattacharyya coefficient ratio to tighten class separability in the joint height and spectral feature space, thereby reducing confusion with spectrally similar crops such as soybean and sorghum. Across six test sites and three years, cross-validation results show that the HSGMM model achieves overall accuracies of 0.87 to 0.95 and F1 scores of 0.86 to 0.95, consistently outperforming random forest classifiers, which achieve overall accuracies of 0.79 to 0.89 and F1 scores of 0.80 to 0.89, as well as other HSGMM variants. With a lightweight and modular design and minimal training sample requirements, our HSGMM model offers a practical solution for regional maize mapping and annual crop distribution updates in data-poor environments.