Estimation of wheat kernel moisture content based on hyperspectral reflectance and satellite multispectral imagery

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Zhaocong Wu, Junhan Luo, Keyi Rao, Haoyu Lin, Xiaohui Song

Published: 2024, Last Modified: 06 Nov 2025Int. J. Appl. Earth Obs. Geoinformation 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Highlights•Wheat kernel moisture content (KMC) is accurately estimated (R2 > 0.85) based on spectral features from hyperspectral (350–2500 nm) data and satellite multispectral imagery (PlanetScope and Sentinel-2) on single-spot and large-scale.•The incorporation of hyperspectral reflectance and multispectral data from imagery can improve the estimation accuracy on large-scale through the transfer learning algorithm of Two-stage Tradaboost.R2.•The absorption valley at 600–700 nm and SWIR are distinct spectrum regions sensitive to wheat KMC.•PlanetScope imagery is an ideal data source for monitoring rapid fluctuations in wheat KMC on large-scale during the physiological maturity period.