Hyperspectral assessment of wheat lodging: From field to EnMAP satellite observations

作者：Mehmet Furkan Çelik, Padmageetha Nagarajan, Andrew Nelson, Zaib Unnisa, Booker Ogutu, Jadunandan Dash, Mirco Boschetti, Ewelina Dobrowolska, Espen Volden, Roshanak Darvishzadeh · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2026 · DOI：10.1016/j.jag.2026.105289 · 被引用次数：1 · 研究领域：Remote Sensing in Agriculture、Impact of Light on Environment and Health、Crop Yield and Soil Fertility

Crop lodging, the permanent displacement of crop stems from their vertical position, causes substantial yield and quality losses in wheat production. Early and accurate detection of lodging and its severity is therefore essential for improving harvest management and reducing economic risk. This study, for the first time, examines hyperspectral data from the Environmental Mapping and Analysis Program (EnMAP) satellite in conjunction with field hyperspectral measurements and machine learning algorithms to detect wheat lodging and its severity and to identify spectral regions important for lodging detection. The study was conducted at Bonifiche Ferraresi Farm in Italy, where wheat biophysical measurements were collected alongside spectral measurements acquired using an Analytical Spectral Device (ASD) spectroradiometer, concurrent with EnMAP data acquisition. Wheat spectral reflectance derived from both field and EnMAP data was analyzed to determine how lodging alters wheat spectral characteristics and to identify sensitive wavelengths. Following spectral preprocessing, lodging severity was quantified using a lodging score and modeled with Principal Component Analysis (PCA) based Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Multilayer Perceptron (MLP), and Explainable Boosting Machine (EBM). Model performances and spectral relevance were evaluated through PCA loadings, Variable Importance in Projection (VIP), SHapley Additive exPlanations (SHAP) va...