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

作者：Guilong Xiao, Kaiqi Du, Xuecao Li, Juepeng Zheng, Shuangxi Miao, Anne Gobin, Jianxi Huang · 发表于：GIScience & Remote Sensing · 年份：2026 · DOI：10.1080/15481603.2026.2671603 · 被引用次数：1 · 研究领域：Soil Geostatistics and Mapping、Remote Sensing in Agriculture、Spectroscopy and Chemometric Analyses

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 ...